>> Live from San Francisco, it's theCUBE covering IBM Think 2019. Brought to you by IBM. >> Hey, welcome back everyone, we're here live with theCUBE's coverage in San Francisco, the Moscone Center for IBM Think 2019. I'm John Furrier, my co-host David Vellante. Dave, we've been doing theCUBE 10 years, our second ever CUBE event was SAP SAPPHIRE, so going back into the archives. >> Great memories. >> SAP, we've been watching the SAP evolve, we've got two guests from SAP. Scot Henney, Global VP of SAP Customer Experience CX and Marcus Venth, who's a Global VP of S/4HANA, Business and Market Development, talking about enterprise, intelligence, making data, making it reason. We've been covering you guys and I got to say, Bill McDermott has always been on the front wave of all the big waves. He was talking about data and iPads right at the beginning. And the things he was talking about in 2012, 2013 is what everyone is doing today. >> Yes. >> This has been a big part of SAP, not new to you this transformation, how's the journey going? How's the partnership going with IBM? >> So, the relationship that we have with IBM is, I guess, about 40 years old and we're not even halfway done yet. You know, we're still working together and successfully delivering great business outcomes for our customers, and I think that's because not only do you have great global reach and scale, but you also understand how data and business processes impact business outcomes. Both in the back office and also in the front office too. So you were mentioning Bill McDermott. We have a phrase with inside SAP CX called, "Be Bold." Right, it's really taken in on the mantra for us and we're making some really bold acquisitions with inside the front office space. So, one of the ones he's done recently that's really focused on data is around Qualtrics. >> Yeah Huge, huge acquisition for us about experiential data and how we bring that back to organizations and we're really keen to work with IBM on that too. >> He said that was a game changer on his press conference. I watched that, I was really interesting acquisition. >> Yeah, bold move. >> Because you bring in real time data, you bring in real telemetry, real analytics, all this stuff together in a kind of new powerful way, with an existing system that SAP has been powering business software, in all these apps, what does it mean? Does this make this enterprise more intelligent, is that where is connects? What's some of the key things there? >> So, that's a really good question. So, if you can connect the back office to the front office and then create trusted relationships, then you're going to deliver a better customer experience. And that has a huge impact on shareholder value. Specifically around Qualtrics. That enables to move that next level on into what we call the experience economy. So, not only do we understand implicit data and explicit data like you were just saying before, how many people have just seen that mail, but also how they react to you. But we could also say, "What do they feel about you? "What else would they like you to do?" "What relationship do they currently have with you "and what would they like to see improve?" >> This is interesting, one of the things we talk about all the time at theCUBE is, you know, 'cause we're in the information business, we're a media company. Information's everywhere. >> Yeah >> It's knowledge and experience is the new thread. >> Totally. >> So the outcome is the word you used to use but now you're thinking, okay, if experience and presence and knowledge, this is a new kind of user experience. Is that what the intelligent enterprise is? I mean, what is the intelligent enterprise? Give us the definition. >> Right, so I think I can take that one. So, simplistically it's about taking data that you've referenced earlier on and applying new technologies to ultimately make business processes or optimize business process or come up with entirely new business models. You know, we talk about Uber and Airbnb and all these but the reality is that there are new business models being enacted within certain industries. Whether it's direct to consumer type changes or changes moving from a productized, or selling products to selling services. And so when we look at intelligent enterprise it's about taking your business partners, which are the stakeholders that make you as a company successful, that would be your customers, your suppliers, your employees and connecting them. And then ultimately leveraging the data that you're collecting as part of those business processes, applying machine learning technology, and then looking at how can we make that more efficient, or how can we now leverage that data to create new insights that then tie in to the customer experience side of things. >> You know it's interesting, John, you talk about McDermott during the big data craze. Bill McDermott never really used that term, at least not that much, but he did talk about the importance of fast data being able to respond quickly. Obviously SAP customers have a lot of data. And so you've got this platform now, this sort of data platform. How are customers making investments to, sort of, alter or modernize that data platform for this purpose? >> So, the digital platform is really interesting because what we're looking for if we look at the sort of components of an intelligent enterprise is three components. There's the intelligence suite which includes the digital core, then there's the platform, and then we have the intelligent technologies like machine learning and artificial intelligence wrapped around all of that. The platform is really helping our customers get to a more standardized approach. Where it's helping them integrate the applications within the suite. It's also a platform with which they can then implement these machine learning scenarios. It's a platform which with they can innovate and build new applications and allowing them to do that means that they can keep their core standard. And that's the key now as customers are thinking more and more about moving to the cloud, it's all about how do I keep my core clean and standard and allow myself to take advantage of those innovations and then move some of those customer specific innovations to the platform and then layer a UI on top of that, that basically means the end user doesn't know which system they're in, they're just leveraging an integrated suite. >> Substracting all the complexities and all that intelligence out. >> Yeah >> What are the obstacles for, it sounds easy, but it's not, it's hard. What are the obstacles, what's it take? Culture, we always talk about cultural sift. >> I mean, the easy, easy one is the organizational challenges, right? We see that the executive support, the charter, having clearly defined objectives and having the talent in-house that has the courage and the skillset to implement those changes. But I think one of the biggest challenges we see touches on what I was saying before where we have a highly customized environment with lots of disparate applications that really are poorly integrated and then trying to get the customers to then move that to a new platform is very difficult. So, with that, they need the courage, in many cases, to leave that stuff behind, right? >> Completely, and I completely concur. So that's the same challenge that we find in the front office. So, we aim to create a phenomenal experience platform for our clients, but unless they're reorganized internally, to remove those siloed thinking around what do sales do, what does marketing do, then they're not going to be able to fully utilize the tools and platforms that we deliver. So, it's actually about a mind shift change and about focusing on the customer. >> I'd like to get your perspective, since you're here, 'cause we go to a lot of events, we go to 120 events last year. We go to CloudNative, Computing Foundation, AWS re:Invent, we're here at IBM Think, we used to go to SAP Sapphire, but that's a different story. But one of the things we hear about is we see new trends like Kubernetes and containers. People are doing it, but they're doing it kind of like in an experimental way, or doing it, you guys are actually implementing technology with customers. >> Yes >> Integrating it in, like, mission critical kind of integrations. You're not standing up to Kubernetes, saying, "Hey we've got a Kubernetes cluster, look at this." In one or two apps, what's your experience with it 'round the integration? Because putting these piece parts together is hard. What are some of the trends that your customers are doing around really standing up cloud-native, intelligent enterprise, apps, what is some of the real use cases that our people are doing? >> I guess first of all, if we're dealing with the SAP portfolio, we're delivering a lot of those integration points out of the box, so that sort of takes away a lot of the guesswork when we talk about integrating sort of disparate applications. And I think one of the key aspects of that is just having, the plumbing is not good enough. You really need to have a data strategy around that where our data hub is then able to provide a consistent master record strategy. Where these systems can then seamlessly talk to each other. 'Cause one of the biggest problems in integrations is not the plumbing, it's actually having these systems being able to talk to each other and rationalize this information. >> Can we, maybe, do a before and after example? I mean, take a supply chain example. So, what's the before look like? What's the after, ideal after state look like, or the sort of outcome that you're looking for? >> So let's take an example, right? Let's say you're buying goods from a supplier and you now want to be connected to that supplier so that you can see where those goods are in transit. And then you want to be notified when there's a delay in those goods so that they can then adjust your production plan to make sure you're still accommodating a customer's order cycle. Now let's say, for instance, that we start recognizing a pattern, or the system starts recognizing a pattern, that every February we seem to see a five day delay, for whatever reason. Now the system can automatically start applying an additional lead time and accommodating for those changes automatically. So, that's what we think of when we think about an intelligent enterprise. It's about an enterprise that live and able to adjust and therefore able to build the trust with the customers in order to fulfill their expectations. >> I think that's a really, really important point. Can I answer that from a customer perspective? >> Yeah, please. >> Please. >> Because we're all consumers as well, of services, and also within our business lives. I think what you want, as a customer, after you've used our services and our systems, is you want to be treated like a person, right? And you want to feel like your data has been treated with some respect, yeah? And then you want to feel that promise that customer has, sorry, that business offered you is being kept. So, you want to be treated like a person, I wasn't just a transaction to you. You understand what I needed, right? And then, you treated my data appropriately. I can trust you with our relationship and I know that you're going to fill in the promise. That's what our platform delivers >> Yeah, 100%, I mean-- >> Yeah >> I ordered something, I want to know if it's not here when you said it was going to be here? I want you to either tell me, tell me why, or do something about it, not force me to call you and find out. I mean that's, it's proactive, it's anticipatory. Not reactive, or no active. >> You got it and that can only be done if you integrate the front office to the back office. And that's what IBM and SAP are working on right now. >> That's great, I mean, that's the greatest segue into my question, which is, here in San Francisco IBM Think 2019, moved from Vegas, now they're doing so, so great. Great venue 30,000 people. What kind of conversations were you guys having here at the show? Take us through a kind of day in the life. What kind of meetings did you have, what were people talking about, what's on the top of minds of meetings, your customers, and your partners at IBM? >> Well, from my perspective, there's a lot of discussion around how to move toward the cloud and what tools we have available, and so with the collaboration with IBM, they've made a tremendous investment in SAP and SAP technologies. They've built the impact assessment tools to help customers evaluate the value and the cost of making that move. And they've also invested in the impact solution, which is the content and pre-configuration to help accelerate implementations and move towards that standard. So, a lot of the discussions I'm having with customers are taking mission critical applications and moving them to the cloud. with the support of partners like that, yeah. >> And at a speed What kind of speed? It used to be weeks, months, days, now what? Cycle time for moving. >> If you go to some of those presentations there's 12, 16 week implementations out there, right? >> And when you say moving to the cloud one could infer actually moving but it may not be moving, it may be bringing the cloud model or operating model to the data, is that fair? >> Absolutely So, when we're looking at the cloud, it's not necessarily a wholesale shift. It could be a hybrid model where we're bringing subsidiaries up on the cloud and looking at more of a two tier deployment model where we're looking at an on-prem for the core business and cloud models for subsidiaries. >> It's funny the apps are driving dictating workloads or dictating what resources and architecture to it. >> So, I've had some really exciting conversations here. I was really really impressed with the conversations I had with the IX teams in IBM but also with the GBS teams. >> What's the IX teams? >> They're a-- >> Experience. >> Okay, okay. >> VR, ART, cool stuff. >> That's it, really, really cool, forward-thinking group of design-thinking experts focused on customer experience. So, the total adjustable market opportunity for CX, commerce, marketing, sales, service is over 30 billion per year. So, I don't have to come in and tell anyone what the size of the market opportunity is, the question is, where do we begin, because there is so much opportunity ahead of us. All of our market is investing around, how do I deliver better customer experience, and that's because it has a really tangible business impact. I mean, I guess, 80% of consumers have said that they have changed brands because of poor customer experience. That's a huge financial cost. And organizations that deliver better customer experience have over 200% more shareholder value delivered back. So, we've got a great business case\ and a great platform, where do we point the gun? >> You know, they bring up a good point, I want to hear your thoughts. Dave and I, internally our research team, had looked at all the successful companies that we cover. >> Yeah >> And look at the successful ones, and, you know, the not so successful ones, and look at why they are successful. And the winners, at the top of the heap, have design thinking in all of their methodologies. >> Yes >> We just had Accenture's Innovation kickoff last week. Design thinking is at the core of this. Can you give us your view on why that's the case? I mean, I'll see, I'm thinking design, is that just customer experience? Is having more or other impacts in terms of other aspects of tech, why is design thinking such a critical component, design thinking a critical component, of these new innovations? >> 'Cause I think people are, okay. So I think thinking is the operative word there. You've got to think about your customer and what they want from you. And what you've got to think about is how do I deliver a service that is compelling to you, rather than a product you may want through a channel you may choose to buy on? So, if you look at all of those organizations, they've gone through that process of thinking, "How does digital improve my customer relationship?" Because ultimately, if you don't own your customer, then you're out of business really soon. >> Marcus, bring intelligent enterprise now in context to that. Does that close the loop on intelligent enterprise equals customer relationships and impact on outcome? Am I, how does that-- >> Intelligent enterprise definitely plays a part in that, right? So I mean, when we're looking at the intelligent enterprise, especially the intelligent suite, we're really tying all the interim components together. Whether it's dealing with your employees, your suppliers, or your customers, right? So, it's really about the full end-to-end process. My particular area is around the digital core, so that's order to cash, procure to pay, order fulfillment, revenue, these are mission critical applications, right? So, when it comes to making that transformation this is not just some thing that you want to take lightly. That's where the partnership with IBM and SAP really counts. 'Cause those are the sort of partners that you want with that kind of transformation. >> You know what's interesting John? I'll make an observation. If we go back to the early days of ERP >> Yeah >> It wasn't clear that SAP was going to win. It was hard to squint through. But if you could've bet on the companies, invested in the companies who adopted ERP early, despite its complexity and the time it took, you actually could have made a lot of money. Because those companies won in the end. And I feel like you guys are on the cusp of the intelligent enterprise narrative of the next wave of competitive advantage. >> If you combine experiential data with operational data, we're going to blow past the competition and create a whole new market category. Thanks for that observation. I completely agree. >> Yeah, and it's back to your front office back office qualigers and that's why McDermott was all giddy about the acquisition. He was like a kid in a candy store. >> We're all in. >> A spring in his step. >> We're all in. >> We don't want Billy, he's already cool. >> Be bold, be bold >> Yeah. He must do a lot of handshakes. Guys, thanks for coming on theCube. Thanks for sharing that insight. Thanks for clarifying the SAP position. Great innovation. Love following you guys, we think highly of the company. Been following you guys for 10 years and look forward to continuing to track it. SAP here on theCUBE talking about innovation, design thinking, customer experience, and intelligent enterprise. theCUBE is bringing all that intelligent data to you live here in Moscone. Stay with us for more coverage after this short break. (techno music)

>> Live from San Francisco, it's theCUBE, covering IBM Think 2019. Brought to you by IBM. >> Hey, welcome back everyone this is theCUBE's live coverage in San Francisco, California Moscone Center for IBM Think 2019. It's the wrap up of our four days of wall-to-wall live coverage. All the publishing on Siliconangle.com. I've got the journalism team cranking it out. Dave Vellante just put up a post on Forbes, check that out. And Stu's got the team cranking on the videos. Stu and Dave, four days, team's done a great job. Tons of video, tons of content, tons of data coming through theCUBE. We're sharing that live, we're sharing it on Twitter, we're sharing it everywhere on LinkedIn. What's going on with the data? Let's synthesize, let's extract the signal from the noise, let's assess IBM's prospects in this chapter two, as Ginni says. A lot of A.I., lot of data, I mean IBM is an old company that has so much business, so many moving parts and they've been working years to kind of pivot themselves into a position to run the table on the Modern Era of computing and software. So, what do you think, Dave? >> Well, I mean, this has been a long time coming and we're here, you pointed out John, to me privately that IBM's taking a playbook similar to Microsoft in that they're cloudifying everything. But there's differences, right? There's a bigger emphasis on A.I. than when, not that Microsoft's not in A.I. they of course are, but when Microsoft cloudified itself there wasn't as much of an emphasis on A.I. Ginni Rometty said, "Well, the first chapter was only about 20%, the remaining 80% is going to be chapter two. We're going hard after that." I wrote in that post today that, in 2013, IBM had a wake-up call. They lost that deal to Amazon at the C.I.A. They had to go out and buy Softlayer because their product was deficient, their cloud product was deficient. >> And by the way it looks like they're going to lose the JEDI Contract by the D.O.D., another agency that's a 10 billion dollar contract. >> So we can talk about they're going to lose that one too. >> We can talk about is Amazon's lead extending in Cloud? And so, IBM cannot take on Amazon head-to-head in infrastructures of service period, the end. It doesn't have the volume, >> And they know that, I think. >> It doesn't have the margins, and they know that. They got to rely on it's, as a service business it's SaaS, it's data, it's data platforms, obviously A.I. and now Red Hat. The fact that IBM had to spend, or spent, 34 billion dollars on Red Hat, to me underscores the fact that it's Cloud and it's 10-year attempt to commercialize Watson, isn't enough. It needs more to be a leader in hybrid. >> And let's talk about the Red Hat acquisition because Ray Wang on theCUBE yesterday and said, "Oh, P.E., private equity prices are driving up 34 billion dollars, pretty much market in today's world." He thinks they overpaid and could have used those services. You debated that, you've heard me say that, hey I could have used that 34 billion dollars of cobbled-together stuff, but you made a comment around speed. They don't have the gestation period there to do it. So, if you take market price for Red Hat, Stu, with open shifts accelerated success since Kubernetes really accelerated its adoption. You got IBM now with a mechanism to address the legacy on premise into Cloud Modern, and you got with this Cloud Private, Stu, this really is a secret weapon for IBM and to me, what I'm pulling out of all the data is that Rob Thomas at Interpol, the CDO have a great data A.I. strategy as a group. They have a team that's one team and this Cloud Private is a secret weapon for them. I think it's going to be a very key product and not a lot of people are talking about it. >> Well John, it shouldn't be a secret weapon for IBM because of course IBM has a strong legacy in the data center. We've talked about Z this week, you talk about power, talk about all the various pieces. Red Hat absolutely can help that a lot. What we noticed is there wasn't a lot of talk about Red Hat here just because it's going through the final pieces. We expect later this year to come out, but it's about the developers. That is where Red Hat is going to be successful, where they are successful and where they should be able to help IBM leverage that going forward. The concern we have is culture. IBM says that Red Hat will be separate. There will be no layoffs, they'll keep that alone but when I wrote about the acquisition I said, we should be able to see, for this to really be a successful acquisition, we should be able to see the Red Hat culture actually influence what's happening at IBM. And to be honest when I talk to people around this show, they're like, "That's never going to happen, Stu." >> I just want to make a point about the price. Ray was saying how they overpaid and made the private equity thing. IBM's paying a hundred and ninety dollars a share. If you dial back to June of '18, Stu you and I talked about this in our offices, Red Hat was trading at one seventy five a share. So they're paying an 8 1/2% premium over that price. Yes, when they made the deal in the fall you're talking about a 60% premium. So, the premium is really single digits over what it was just a few months earlier. >> And Cisco, Google, >> It was competitive, right. >> Microsoft all could have gone after that. I think it's a great buy for IBM. >> That's what they had to pay to get it. >> And definitely it helped there. So from my stand-point, looking at the show this week, first of all I was impressed to see really that data strategy and how that's pervasive through the company and A.I. is something that everyone's talking about how it fits in. John you commented a bunch of times Ginni mentioned Kubernetes two times in her Keynote. So, they're in these communities, they're working on all these environments. The concern I have is if this is chapter two and if A.I. is one of the battlefields, Amazon's all deep into A.I. I think heavily about Google when I talk about that. When I talk to Microsoft people they're like, "Satya Nadella is Mr. A.I.", that's all they care about. >> I don't think Microsoft has a lot of meat on the A.I. bone either. >> Really? >> No look it, here's the bottom line. A.I. is a moonshot it is an aspirational marketplace. It's about machine learning and using data. A.I.'s been around for a while and whoever can take advantage of that is going to be about this low-hanging use cases of deterministic processes that you throw machine learning at no problem. Doing cognition and reasoning a whole 'nother ballgame. You got state, this is where the Cloud Native piece is important as a lynch-pin to future growth because that wave is coming. And I think it's not going to impact IBM so much now, as it is in the future, because you got developers with Red Hat and you got the enablement for Cloud growth, Modern Cloud, stuff in any Cloud. But IBM has a zillion customers Dave, they have a business, they have mission critical workloads. And you pointed out in the Forbes post that we posted and on the Silicon Angle, that I.T. Economics are changing. And that the cloud services market is growing, so IBM has pre existing, big mission critical companies that they're serving. So, you can't just throw Kubernetes at that and say lift and shift. Z's there, you got other things happening. So, to me, that is IBM's focus, they nail their bread and butter, they bring multi-cloud from the table. Throw hybrid at it with Private Cloud and they're stable. Everything else I think is window dressing in my mind, because I think you're going to see that adoption more downstream. >> Well, the other thing you gave me for the piece actually, you helped me understand that IBM with Red Hat can use Cloud Native techniques and apply them to its customer base and to really create a new breed of business developers, right? Probably not the hoodie crowd necessarily, but business developers that are driving value apps based on mission critical apps and using Cloud Native techniques. Your thoughts on that? >> The difference between Oracle and IBM is the following, Oracle has no traction in developers in Cloud Native, IBM now with Red Hat can take the Cloud Native growth and use containers and Kubernetes and these new technologies to essentially containerize legacy workloads and make them compatible with modern technologies. Which means, if you're in business or in I.T. or running a lot of big shops, you don't have to kill the old to bring in the new. That's one factor. The other factor is the model's flipped. Applications are dictating architecture. It used to be infrastructure dictates what applications can do, it's completely reversed. We've heard this time and time again from the leading platforms, the ones that are looking at the applications with data as a fabric in there will dictate resource, Whether it's one Cloud or multiple Clouds or whatever architecture that's the fundamental shift. The people who get that will win and the people who don't won't. >> And the other thing I've pointed out in that article is that Ginny kept saying it's not backend loaded, The Red Hat deal, it's not back end loaded. IBM has about a 20 billion dollar business, captive business, in outsourcing, application management, application modernization and they can just point Red Hat right at that base, bring it's services business, Stu you've made this point, it's about scaling Red Hat. Red Hat's what, about a three and a half billion dollar company? >> Yeah >> And so that really is, she was explaining the business case for the acquisition. >> Yeah absolutely, I mean we've watched IBM for years, Bluemix had a little bit of traction but really faltered after a while, that application modernization. You hear from IBM, similar to what we've heard from Cisco a few weeks ago, meet customers where they are and help them move forward. We did a nice interview this week with a UK financial services company talking about how they've modernized what they're doing. Things like I.T. ops, new ops, these environments that are helping people with that app development. 'Cause IBM does have a good application work flow. There's lots of the infrastructure companies don't have apps and that's a big strength. >> When was the last, I got a direct message from the crowd, I want to get to Stu, but I want to ask you guys a question. When was the last time you saw a real innovation and disruption in a positive way around business applications. We're talking about business applications, not a software app, that's in a created category. We're talking about blocking and tackling business applications. When have you seen any kind of large scale transition innovation. Transition and innovation at the business application level? >> Google Docs? I mean >> I mean think about it. >> Right? >> So I think this is where IBM has an opportunity. I think the data science piece is going to transform into a business app marketplace and I think that's where their value is. >> Workday? >> Service Now. >> It's a sass ification of everything. >> Salesforce? >> Service Now, features become products. Products become companies. I mean this a big debate. I mean you can win on >> But that's not, Service Now really not a business, I mean it is a business app but it's more of an I.T. app. Alright Workday I'd say is an example. Salesforce I guess. >> And look here's one of the flaws in that multi-cloud picture, is it's I'm going to take all this heterogeneous environment and I'm going to give you a multi-Cloud manager. We've seen that single pane of glass discussion my entire career and it never works. So I'm a little concerned about that. >> So Andy Jassy makes the case that multi-cloud is less secure, more complex, more expensive. It's a strong case that he makes. Now of course my argument is that it's multi-vendor. It's not really multi-cloud. >> Well here's the Silicon Valley >> So he didn't have any control over that. It's not a procurement thing, it's just the way that people go by. >> The world has changed with cloud and I'll give you a Silicon Valley example anecdote. It used to be an expression in Silicon Valley, in venture capital community if you were a start-up or entrepreneur you'd build a platform. And there was an old expression, that's a feature, not a company. Kind of a joke within the VC community and that's how they would vet deals. Oh, that's a good feature" >> "Oh it's a feature company." >> "That's a great idea." Now with Cloud as a platform and now with all the stuff that's coming to bear, horizontally scalable, all the things that IBM's rolling out, sets the table for a feature to be a company. Where you have an innovation at the business model level, you don't really need tech anymore other than to scale up build it out and that's all done for you by other people. So people who are innovating on say an idea, well let's change this little feature in HR app or, that could meet up to Workday. Or let's change this feature. Features can become companies now so I think that's my observation. >> I think it's really interesting >> It could live in the cloud marketplaces too. It's so easy to get that scale if I could plug into all those marketplaces. IBM for years has had thousands of partners in their ecosystem. Of course Amazon's Marketplace, growing like gangbusters. >> But this is what Jerry Chen said when we were at Reinvent last year and we were asking him about Amazon, will it go up the stack, will it develop applications? He said, well, look but then what we got to do is give people a platform for application developers to build those features to disrupt, to your point, the core enterprise apps. Now, can IBM get there before Amazon, who knows? I mean its. >> Alright guys let's look at the big picture, zoom out. Your thoughts on Think 2019 IBM Think, Stu what's your final thoughts? >> Yeah, final thoughts is, I think IBM first of all is coming together. Just as this show was six shows and last year it was in two locations, there's cohesion. I heard the four days of interviews, we saw a lot of different pieces. Everything from talking about augmented reality through storage and we talked about the Z, and those pervasive themes of data, A.I., Dave what do you call it, It's the innovation cocktail now in Cloud. Data A.I. in cloud, put those three together. >> Innovation sandwich, innovation cocktail. Got to have a cocktail with a sandwich. That's your big take away? Okay, my take away Dave is that the, you nailed it in your post I thought, you should go to Forbes and check out, search on IBM Think you'll find the post by me and Dave Vellante but it's really written by Dave. I think to me IBM can change the game on two fronts. I learned and I walked away with a learning this week about these business apps. To me, my walk away is there's going to be innovation at a new genre of developers. I think you're going to see IBM target, they should target these business app ties as well as with the Could Native in Red Hat. I really think highly of that acquisition. From a speed stand point, I think the culture of Red Hat, although different, will be a nice check against IBM's naturally ability to blue-wash it. Which means you don't want to lose the innovation. I think Ginni saying Kubernetes twice on stage, is a sign that she sees this path, I think the Cloud Private opportunity could be a nice lever to bring open shifts and Kubernetes into that growth. And I think A.I. is going to be one of those things where they're either going to go big or go home. I think it's going to be one of those things. >> My take, love the venue, way better than last year in terms of the logistics. I like the new Moscone, easy to get around. May next year, May 2020 is going to be better than February here. I would've liked to see Ginni sell harder. She laid out a vision, she talked about a lot of sort of of high level things. I would have liked to seen her sell the new IBM and Red Hat harder. I guess they couldn't do that because they're worried about compliance. >> Quiet Period? >> Yeah right, you know monopolistic behavior I guess. But that I'm really excited to hear that story and a harder sell on the new IBM. >> I think if they can take the Microsoft playbook of cloudifying everything going with the open source with Red Hat and then just getting the great Sass if app revenue up, they're going to, can do well. >> Alright guys, great job. Thanks for hosting this week. Lisa Martin's not here today. Want to thank Lisa Martin if you're out there watching, great time. Guys, thanks to the crew. Thanks to IBM. Thanks to all of our sponsors that make theCUBE do what we do and thanks for all of your support to the community. I'm John Furrier along with Stu Miniman. Thanks for watching. See you next time. (pulsing electronic music)

>> Live from San Francisco. It's theCUBE covering IBM Think 2019 brought to you by IBM. >> Welcome back everyone, this is the CUBE's live coverage in San Francisco at Moscone Center for IBM Think 2019. I'm John Furrier with Dave. Volante Dave it's been in AI, it's been cloud, it's been in data changing the game. We've got two great guests here Murali Nemani, CMO of ScienceLogic, your CEO has been on the CUBE before and Joe Damassa who is the VP of strategy and offerings for hybrid cloud service at IBM. Thanks for joining us. >> Welcome. >> Appreciate it. >> Thank you guys. >> Welcome to CUBE. So day four of four days coverage, yes, you can see the messaging settling the feedback settling, AI clearly front and center, role of data in that and then cloud scale across multiple capabilities. Obviously on premise multi cloud is existing already. Software's changing all this. >> Right. >> And so AI impacting operations is key. So how do you guys work together? What's the relationships in ScienceLogic and IBM? Could you just take a minute to explain that? >> I think I mean, clearly, as you talked about the hybrid nature of what we're dealing with, with the complexity of it, it's all going to be about the data. You know, software is great, but it's about software that collects the data, analyzes the data, and gives you the insights so you can actually automate and create value for our clients. So it's really this marriage, it's a technology but it's a technology that allows us to get access to the data so we can make change, it's all about the data. >> And so a lot of what IBM has been doing is building the analytics engines and Watson it's for them. Our partnership has been really building the data and the data lake and the real time aspects of collecting and preparing that data so that you can really get interesting outcomes out of it. So when you think about predictive models, when you think about the the way that data can be applied to doing things like anomaly detection that ultimately accelerate and automate operations. That's where the relationship really starts taking hold. >> So you guys are specialized in AIops and IT apparatus as that transforms with scale and data which you need machine running, you need a kind of gave it automation. >> Yes. >> And which is the devops use of operations is don't go down, right, up and running, high availability. >> Yeah. >> So on the cloud services side, talk about where the rubber is meeting the road from a customer standpoint, because the cultural shift from IT Service Management, IT operations has been this manual, some software here and there, but it's been a process. Older processes change a little bit, but this is a new game. Talk about how you guys are engaging the customers. >> Well, a part of it I mean, it's interesting when you step back and you stop breathing, you're on exhaust in terms of pushing what you're trying to sell and you listen to your customers what we're hearing is that they all understand the destination. They understand they're moving to the cloud, they understand the value that's going to bring, they're having a hard time getting started. It's how do I start the journey ? I've got all of this estate and traditional IT operations capabilities it's kind of move. How do I modernize it? How do I make it so it's portable across different environments. And so when you step back, you know, we basically said, hey, you need the portability of the platform. So what we're doing with Red Hat, what we're doing with IBM, cloud private, it creates that portable containerizing ability to take our existing workloads and start moving them, right. And then the other thing that the clients need are the services. Who's going to help me advise me on what workloads should move, which one shouldn't, most of the staff fails because you move the wrong things. How do you manage that? How do you build it? And then when you're done, and you've got this hybrid complex environment, how do we actually get insights to it and the data I need to operationalize it? How do I do IT apps, when I don't own everything within the four walls of my data set. >> Now, are you guys going to market together? You guys sell each other products, the relationship with ScienceLogic and IBM is it a partnership, is it a joint development? Can you explain a little bit more on how you guys work together? >> Well, we're one of the largest sort of services provider in the industry. So as we bring, our products, our technologies and our capabilities to market, we bring ScienceLogic into those deals, we use ScienceLogic in our services so that we can actually deliver the value to our clients. So it is sort of a co development, co joint partnership plus also our goal to market. >> So you use that as a tool to do discovery and identify the data that's in and the data that we're talking about is everything I need to know about my IT operations, my applications, the dependencies. Maybe you could describe a little bit more. >> Sure if you think about one of the things that Joe was mentioning is, today, the workloads are shifting, you're going from, let's say management performance monitoring and management platforms that you need to evolve from, to incorporate new technologies like containers and microservices and server-less architectures. That's one area of how did the tool sets fundamentally evolve to support the latest technologies that are being deployed? So think about that. Second is, how do you consolidate those set of tools now you're managing? Because you're adopting cloud based technologies or new capabilities, and so get consolidation there. And the third is, these workloads that are now migrating out of your private cloud or private data center into public clouds, right? And then that workload migration, I think it is Forrester level saying, about 20% of the total workloads are currently in some sort of a public cloud environments. So there's a lot of work to do in terms of getting to that tipping point of where workloads are now truly in a multi cloud hybrid cloud. So as IBM accelerates that transition and their core competencies in helping these large enterprises make that transition, you need a common manageable environment, that the common visibility across those workloads. So that's at the heart of what we're pulling, and then the data sets happened to be data sets that are coming either from the application layer, data coming from the log management systems, it could be data coming from a service desk in terms of the kind of CMDB based data sets, and we're building a data lake that ultimately allows you to see across these heterogeneous system. >> It could be service request to get that really touches the business process so you can now start to sort of map the value and how change is going to affect that value, right? >> Yeah, exactly. >> Yeah. >> I mean, what's interesting about ScienceLogic as a partner, it's the breadth of their platform in terms of the different things they can monitor, the depth, the ability to go into containers, and kind of understand what the applications are doing in them and the scale in terms of the types of devices. So when you think about, the types of devices, we're going to have to manage everything from, sensors in an Internet of Things, environment to routers, to sophisticated servers and applications that can be running anywhere, you need the flexibility of the platform that they have in order to be able to deliver that. >> And I think that's a key point when you talking about containers and Kubernetes, we heard your CEO Jeannie remitting mentioned Kubernetes, onstage like, that's great, good time(mumbles) I know no one like Kubernetes now it's mainstream. >> Yeah. >> So this is showing them what's going on the industry which is the on premise decomposition of on premise with cloud private, you guys have. >> Yes. >> Is giving them the ability to use containers to manage their existing stuff and do that work and then have the extension to cloud, public cloud or whatever public cloud. This gives them more mount modern capabilities. So the question is, this change the game we know that but how has it changed AIOps and what does it mean? So I guess the first question is, what is AIOps? And what is this new on premise with cloud private and full public cloud architecture look like in AIOps 2.0? >> So for me, it's a very simple definition. It's really using algorithmic mechanisms, right? Towards automating operations, right? It's a very simple way, simplistic way of looking at it. But ultimately, the end game is to automate operations because you need to move at the pace of business and machine speed. And if you want to go, move in machine speed, you can have, I mean, you can't throw enough humans at this problems, right? Because of the pace of change, the familiarity of the workloads spinning up and sitting down. We have a bank as a customer who turns up containers for every 90 seconds and then turn them down. Just can't keep that in that real time state of change and being able to understand the topological relationships between the application layer and the underlying infrastructure so that you can truly understand the service health because when an application degrades in performance, the biggest issue is a war room's scenario where everyone's saying, it's not me, it's not me and because everyone's green on their front, but it's now how do you get that connective tissue all the way running-- >> Well it's also not only the change, it's also the velocity of data coming off that exhaust or the changes and services is thrown off tons of data that you need machines now I mean, that's kind of the thing. >> Exactly, yeah. And I would add to that, I think part of the definition of AIOps is evolving. We know where we're coming from is more fit for purpose analytics, right? I have this problem, I'm the collect this data, I'm going to put these automations in place too address it. We need to kind of take it data Model approach that says, how do I ingest all of this data? You know, even at the start, when you're looking at which workloads and you're doing discovery and assessment of workloads, that data should go into a data lake that can be used later when you're actually doing the operations and management of those workloads. So what data do we collect at every stage of the migration and the transformation of it, and including the operational data? And then how do we put a form analytics on it, and then get the true insights? I think we're just scratching the surface of applying to AI, because it's all been very narrow cast, narrow focus, I have this problem, I collect this data, I can automate this server, it needs to move much beyond that to it... >> And services are turning up and on and off so fast as a non deterministic angle here, and you got state, non deterministic, I mean, those are hard technical computer science problems to solve >> Yeah. >> That's you don't just put a processor around say, oh, yeah. >> Well, let's back to the the scalability of the platform, the ability in real time to be monitoring and looking at that data and then doing something right. >> All right now, humans aren't completely removed from the equation, right? And so I'm interested in how the humans are digesting and visualizing all this data, especially at this speed there a visualization component? How does that all evolving? >> Yeah, I think that to me I mean, that's part of the biggest challenges. You humans are a, they have to be the ones that kind of analyze what's coming and say, what does this mean when you haven't already algorithmically built it into your automation technology, right? And then they also don't have to be the one to train, the system is doing to actually do it. So one of the things that were are that struggling with not struggling with, we're experimenting with is, how best to visualize this, right? We do some things now, we've got a hybrid cloud management platform, we're teaming with the product guys, and it's the ability to have four consoles. One from a consumption, how do I consume services from Amazon, IBM Cloud on premise, how do I deploy it? So in a Dev apps model, how do I fulfill that very quickly and operational councils, right, and then cost on asset management so you can actually have at glance say, oh, you know, I've got a big Hadoop cluster which been spun up, I'm paying $100,000 for it and it has zero utilization. So how do you visualize that so you can say oh, I'm need to put a rule in that if somebody's spinning something up on, you know, IBM Cloud and they're not using it, I either shut it down, or I sent messages out, right, for governance in top of it. So it's putting business rules and logic in terms, in addition to visualization to help automate. >> And Jeannie talked about this at our keynote efficiency versus innovation around how to manage and this is where the scale comes in. Because if you know that something's working, you want to to double down on it, you can then, kind of automate that away and then you just move someone, the humans to something else. This is where the AIOps I think it's going to be, I think, going to change the category. I mean, it's a Gartner Magic Quadrant for the IT operations. >> Right. >> AI potentially decimates that, I mean... >> Yeah, there's this argument that you know, you have these nice quadrants or let's say nicely defined market segments. You have the NPMD, the ITSM, the ITOM, you know, you have APM and so what's happening is in this world of AIOps, none of those D marks really fit anymore because you're seeing the convergence of that. And then the other transition that's happening is this movement from, you know, classic ops or Dev and a dev to Ops, Dev Ops and now dev sec Ops, you know, you're trying to get worlds to converge. And so when we talk about the data and being able to build data models, those data models need to converge across those domains. So a lot of the work we do is collect data sets from log management, from service desk and service management, from APM etc, and then build that data model in real time. So you can.... >> It kind of building an Uber or CMDB or I mean, right? (loud laughter) I mean, do most of your clients have a single CMDB? Probably not, right? >> Yeah. So this is sort of a new guidepost, isn't it? >> Yeah, a part of it is. There are these data puddles if you will, all right data exist in a lot of different places How do you bring them together so you can federate different data sources, different catalogs into a common platform because if a user is trying to decide, okay, should I spin this up on, you know, this environment or that one, you want the full catalog of capabilities that are on premise in your CMDB system with the legacy environment out of the catalogs that may exist on Amazon or Azure, etc and you want data across all that. >> It seems that everything's a data problem now. And datas are being embedded into the applications which are then the workflows are defining infrastructure, architecture, or are sole cloud, multi cloud, whatever the resource is, so we had JPMorgan Chase on top data geek on and she was talking about, we have models for the models and IBM has been talking about this concept of reasoning around the data. This is why I always like the cognition kind of angle of cognitive, because that's not just math, math is math, you do math on, you know, supervised machine learning and knowing processes to be efficient, but the cognition and the reasoning really helps get at that data set, right. So can you guys react to that? I mean, is everything a data problem? Is that how you should look at it and how does reasoning fit into all this? >> Well, I mean, that's back to your point about what is the humans role in this, right. So we're moving in a services business from primarily labor base with tools to make them more efficient to the technology doing the work. But the humans have to then say, when the technology get stumped, what does that mean? So should I build a new, how do I train it better? How do I, you know, take my domain expertise? How do I do the deep analytics to tell me all right, how do I solve those problems in the future? So the role changes I think Jenny talks about in terms of new collar workers. I mean, these are data scientists, these are people that understand the dynamics of the inner relationship of the different data, the data models that need to get built and they are guiding in effect the automation. >> I thought your CTO was on theCUBE talking about, Paul was talking about, you know, take the heavy and Rob Thomas was also on, the GM of the data plus AI team. I think he really nailed it. If you guys to take away the heavy lifting of the setup work then the data science who're actually there to do the reasoning or help assist in managing what's going on and putting guard rails around whatever business policy is. >> Today, I mean, we talked to in this about 79 percent I think it's a gardener stat of 79 percent of the data scientists. And these are these PhDs, they're highly valuable, spend their time collecting, preparing, cleansing those data models, right? So, you're now really applying that PhD level knowledge base towards solving a problem, you're just trying to make sense of the data. So one, do you have a holistic and a few? Two, is there a way to automate those things so you can then apply the human aspects towards the things that Joe was talking about. So that's a big part of what we're trying to come together in terms of the market for. >> Well guys thanks for the insight, thanks for coming on, great job. I think we talked for you know, an hour and on cultural shift because you mentioned the sets in here Ops and devs. It's a melting pot and it's a cultural shifts. I think that topic is worth following up on. But I'll let you guys just get a quick plug for you. I know you going to an event coming up and you got some work. You can talk about what you guys are doing. You got an event coming up, what your pitch, give a quick flag. >> Yeah, so we've got our symposium, which is our big user conference. It's in April. It's right in, it's on April 22 to 23rd to the 25th. It's in downtown Washington DC, Cherry Blossom festival season at the Ritz Carlton. And so a lot of that, we'll have theCUBE there as well. >> Yeah of course. >> So, we're looking forward to it. A lot of great energy to be carried over. >> We love going to the District. (laughs loudly) >> What don't we say, you guys are great, great to visit. So give the plugs with a service you're doing. Just give an update on what you guys are up to. >> Yeah, I think I mean, we're also we're investing the technology when we're full on board with the containerization, as we talked about, we're putting together a services portfolio. I think Jenny mentioned that we're taking a whole bunch of capability across IBM Global Technology Services, Global Business Services, and really coalescing into about, you know, 23 offerings to help customers advise on cloud, move to cloud build for cloud and manage on cloud and then you've seen the announcements here about what we're doing around the multi cloud management system. Those four console I talked about how do we help, you know, put a gearbox in place to manage the complexity of the hybrid nature that our customers are dealing with. >> It seems IBM got clear visibility on what's happening with cloud, cloud private, I think a really big announcement. I think it's not talked about in the show and I'll always kind of mentioned the key linchpin but you see cloud, multi cloud, hybrid cloud, you got AI and you got partnerships, ecosystem now its execution time, right? >> Yeah, exactly and, and frankly, that's the challenge, right? So we used to be able to manage it all on the four runs, right? Your SAP instances was in the data center, your servers were in the data center, your middleware is in the data center. Now I got my applications running in Salesforce.com often software as a service. I've got three or four different infrastructures of service providers. But I still have the legacy that I got to deal with. I mean the integration problems are just tremendous. >> Chairman VP of strategy at IBM hybrid cloud and Murali Nemani, CMO ScienceLogic, AI operations, bringing in hybrid clouds to theCUBE bringing all the coverage day four. I'm with Dave Volante, it's all about cloud AI developers all happening here in San Francisco this week. Stay with us from this short break. (upbeat music)

>> Live from San Francisco, it's theCUBE covering IBM Think 2019. Brought to you by IBM. >> Welcome back, we're here at Moscone North. You're watching theCUBE, the leader in live tech coverage. This is day four of our wall to wall coverage of IBM the Think. The second annual IBM Think, first year at Moscone. Dave Vellante here with Stu Miniman. Eric Herzog is here, he's the CMO of IBM Storage and Sam Werner is the VP of Offering Management for Storage Software at IBM. Guys welcome back to theCUBE. Always good to see ya both. >> Thanks >> Thank you. >> So we were joking yesterday and today, of course multi cloud, the clouds opened, it's been raining, it's been sunny today, so multi cloud is all the rage. Evidently you guys have done some work in multi cloud. Some research that you can share with us. >> Yeah, so couple things. First of all, the storage vision in multi cloud at IBM for years. We work with all the cloud providers including IBM cloud, but we work with Amazon and we work with Azure, we work with Google cloud and in fact our Spectrum Protect, modern data protection product, has about 350 small and medium cloud providers across the world that use it for the engine for their back up as a service. So we've been doing that for a long time, but I think what you're getting is, what we found in a survey multi cloud and I actually had had a panel yesterday and all three of my panelists, including Aetna, use a minimum of five different public cloud providers. So what we're seeing is hybrid is a subset of that, right? On and off, but even if someone is saying, I'm using cloud providers, they're using between five and 10, not counting software as a service because many of the people in the survey didn't realize software as a service is theoretically a type of cloud deployment, right? >> So that's obviously not just the big three or the big five, we're talking about a lot of small guys. Some of the guys maybe you could have used in your Spectrum Protect for back up, local cloud providers, right? And then add sas to that, you could probably double or triple it, right? >> Right, well we've have been very successful with sas providers so for example, one of people on the panel, a company called Follett, they're a privately held, in the mid close to a billion dollars, they provide services to universities and school districts and they have a software package for universities for the bookstores to manage the textbooks and another software as a service for school districts across the United States. They have 1,500 and it's all software service. No on prem licensing and that's an example. That's in my mind, that's a cloud deployment, right? >> Ginni talked Tuesday about chapter two how chapter one was kind of, I call it commodity cloud, but you know, apps that are customer facing, chapter two, a lot of chapter two anyways, is going to be about hybrid and multi cloud. I feel like to date it's largely been, not necessarily a purposeful strategy to go multi cloud, it's just we're multi vendor. Do you see customers actually starting to think about a multi cloud strategy? If so, what's behind that and then more specifically, what are you guys doing from a software stand point to support that? >> Yeah, so in the storage space where we are, we find customers are now trying to come up with a data management strategy in a multi cloud model, especially as they want to bring all their data together to come up with insights. So as they start wanting to build an AI strategy and extend what they're doing with analytics and try to figure out how to get value out of the data they're building a model that's able to consolidate the data, allow them to ingest it and then actually build out AI models that can gain insights from it. So for our software portfolio, we're working with the different types of service providers. We're working closely with all the big cloud providers and getting our software out there and giving our customers flexible ways to move and manage their data between the clouds and also have clear visibility into all the data so they can bring it together. >> You know, I wonder sort of what the catalyst is there? I wrote an article that's going up on SiliconANGLE later and I talked about how the first phase was kind of tire kicking of cloud and then when the down turn hit, people went from capex to opex. It was sort of a CFO mandate and then coming out of the down turn, the lines of business were like, whoa agility, I love this. So shadow IT and then IT sort of bought in and said, "we got to clean up this mess." and that seems to be why, at least one catalyst, for companies saying, "hey, we want a single data management strategy." Are you seeing that or is there more to it? >> Well I think first of all, we're absolutely seeing it and there's a lot of drivers behind it There's absolutely IT realizing they need to get control over this again. >> Governance, compliance, security, edix >> And think about all the new regulations. GDPR's had a huge impact. All a sudden, these IT organizations need to really track the data and be able to take action on it and now you have all these new roles in organizations, like data scientists who want to get their hands on data. How do you make sure that you have governance models around that data to ensure you're not handing them things like pi? So they realized very quickly that they need to have much better control. The other thing you've seen is, the rise of the vulnerabilities. You see much more public attacks on data. You've seen C level executives lose their jobs over this. So there's a lot more stress about how we're keeping all this data safe. >> You're right. Boards are gettin' flipped and it's a big, big risk these days >> Well the other thing you're seeing is legal issues. Canada, the data has to stay in Canada. So if you're multi national and you're a Japanese company, all your Canadian offices, the data has to be some cloud of ours got an office in Canada. So if you're a Japanese headquarter company, using NTT cloud, then you got to use IBM or Amazon or Azure, 'cause you have to have a data center inside the country just to have the cloud data. You also have shier maturity in the market. I would argue, the cloud used to be called the web and before it was the web, it was called the internet and so now that you're doing that, what happens in the bigger companies, procurement is involved, just the way they've been involved in storage servers and networking for a long time. Great you're using CISCO for the network. You did get a quote from HP or using IBM storage, but make sure you get at least one other quote so as that influences aside from definitely getting the control is when procurement get involved, everything goes out for RFP or RFQ or at ten dure, as they say in Europe and you have to have multiple vendors and you sometimes may end up for purely, we need the way to club 'em on price so we need IBM cloud and Microsoft so we can keep 'em honest. So when everyone rushed the cloud, they didn't necessarily do that, but now that it's maturing >> Yeah, it's a sign of maturity. >> It's a sign of maturity that people want to control pricing. >> Alright, so one of the other big themes we've been talking a lot about this week is AI. So Eric talks about, when we roll back the clock, I think back to the storage world, we've been talking about intelligence in storage for longer than my career. So Sam, maybe you can tell us what's different about AI in storage than the intelligence we've been talking and what's the latest about how AI fits into the portfolio? >> Yeah, that's a great question and actually a lot of times we talk about AI and how storage is really important to make the data available for AI, but we're also embedding AI in our storage products. If you think about it, if you have a problem with your storage product, you don't just take down one application. You can take down an entire company, so you've got to make sure your storage is really resilient. So we're building AI in that can actually predict failures before they happen so that our storage never takes any outages or has any down time. We can also predict by looking at behavior out in the network, we can predict or identify issues that a host might be causing on the network and proactively tell a customer before they get the call that the applications are slowing down and we can point out exactly which host is causing the problem. So we're actually proactively finding problems out on the storage network before they become an issue. >> Yeah and Eric, what is it about the storage portfolio that IBM has that makes it a good solution for customers that are deploying AI as an application in use cases? >> Yeah so we look at all, so one is AI, in the box if you will, in the array and we've done a ton of work there, but the other is as the underlying foundation for AI workloads and applications so a couple things. Clearly, AI often is performance dependent and we're focused on all flash. Second thing as Sam already put it out, resilience and availability. If you're going to use AI in an automotive factory to control the supply chain and to control the actual factory floor, you can't have it go down because they could be out tens of millions, hundreds of millions of year just for that day of building Mercedes or Toyotas or whatever they're building if you have an automated factory. The other areas we've created what we call, the data pipeline and it involves three, four members of our storage software family. Our Spectrum Scale, a highly parallel file system that allows incredible performance for AI. Our Spectrum Discover which allows you to use meta data which is information about the data to more accurately plan and the AI software from any vendor can use an API and go in and see this meta data information to make the AI software more efficient that they would use. Our IBM Cloud Object Storage and our Spectrum Archive, you have to archive the data, but easily bring it back because AI is like a human. We are, smart humans are learning non-stop, whether you're five, whether you're 25, or whether you're 75, you're always learning. You read the newspaper, you see of course theCUBE and you learn new things, but you're always comparing that to what you used to know. Are the Russians our friends or our enemies? It depends on your point in time. Do we love what's going on in Germany? It depends on your point in time. In 1944, I'd say probably not. Today you'd say, what a great Democratic country, but you have to learn and so this data pipeline, this loop, our software is on our storage arrays and allows it to be used. We'll even sell the software without our storage arrays for use on any AI server platform, so that softwares really the huge differentiator for us. >> So can you, as a follow up to that, can you address the programmability of your portfolio? Whether it's through software or maybe the infrastructure as well. Infrastructure, I'm thinking infrastructure's code. You mentioned you know API's. You mentioned the ability to go into like Spectrum Discover for example, access meta data. How programmable is your infrastructure and how are you enabling that? >> I mean across our entire portfolio, we build restful API's to make our infrastructure completely extensible. We find that more and more enterprises are looking to automate the deployment of the infrastructure and so we provide API's for programming and deploying that. We're also moving towards containerizing most of our storage products so that as enterprises move towards cubernetes type clusters, we work with both Red Hat and with our own ICP and as customers move towards those deployment models and automate the deployment of their clusters, we're making all of our storage's available to be deployed within those environments. >> So do you see an evolution of the role of a storage admin, from one that's sort of provisioning luns to one that's actually becoming a coder, maybe learning Python, learning how to interact through API's, maybe even at some point developing applications for automation? Is that happening? >> I think there's absolutely a shift in the skills. I think you've got skills going in two directions. One, in the way of somebody else to administer hardware and replace parts as they fail. So you have lower skilled jobs on that side and then I believe that yes, people who are managing the infrastructure have to move up and move towards coding and automating the infrastructure. As the amount of data grows, it becomes too difficult to manage it in the old manual ways of doing it. You need automation and intelligence in the storage infrastructure that can identify problems and readjust. For example, in our storage infrastructure, we have automated data placement that puts it on the correct tier. That use to be something a storage administrator had to do manually and figure out how to place data. Now the storage can do it themselves, so now they need to move up into the automation stack. >> Yeah, so we've been talking about automation and storage also for a lot of years. Eric, how are enterprises getting over that fear that either I'm going to lose my job or you know, this is my business we're talking about here. How do I let go and trust? I love, I saw downstairs, there was a in the automation booth for IBM, it was free the humans, so we understand that we need to go there. We can't not put automation with the scale and how things are moving, but what's the reality out in the field? >> So I think that the big difference is and this is going to sound funny, but the economic down turn of seven, eight and nine, when downturn hit and certainly was all over the IT press, layoff, layoff, layoff, layoff, layoffs, so we also know that storage is growing exponentially, so for example, if I'm Fortune 500 company x and I had 100 people doing storage across the planet. If I laid off 50 of them and now I'm recovered. I'm making tons of money, my IT budget is back up. I didn't go to the CIO and say, you can hire the 50 storage people back. You can hire 50 people back, but no more than five or six can be storage people. Everything else has to be dev ops or something else. So what that means is, they are managing an un-Godly amounts of more storage every year with essentially the same people they had in 2008 or maybe a tiny bit more. So what matters is, you don't manage a peta bite or in the old days, half a peta bite. Now, one storage admin or back up admin or anyone in that space, they want you to manage 20 peta bites and if you don't have automation, that will never happen. >> Stu and I were interviewing Steven Hill from KPMG yesterday and he was talking about the macro numbers show we're not (stutters) as globally and even in the US, we're not seeing productivity gains. I'm saying yeah, you're not looking at the storage business you know, right? Because if you look at anybody who's running storage, they're doing way more with much less, to your point. >> Which is why, so for example when Sam talked about our easy tier, we can tier, not only as AI base. So in the old days, when you guys weren't even born yet, when I was doing it. >> Well I don't know about that >> What was it? It was move the data after 90, so first it was manual movement, then it was set up something, a policy. Remember policy automation was the big deal 10 years ago? Automatically move the data when its 90, 60, or 30 days old. AI based, what we have an easy tier, automatically will determine what tier it should go on, whether when the data's hot or when the data's cold and on top of that, because we can tier over 440 arrays that are not IBM logo'd, multi vendor tiering, we can tier from our box to an EMC box. So if you have a flash array, you've got an old or all hard drive that you've moved into your back up in archive tier, we can automatically tier to that. We can tier from the EMC array out to the Cloud, but it's all done automatically. The admin doesn't do anything, it just says source and target and the AI does all the work. That's how you get the productivity that you're talking about, that you need in storage and back ups even worse because you got to keep everything now, which Sam mentioned GDPR, all these new regulations and the Federal Government its like keep the data forever. >> But in that case, the machine can determine whether or not it's okay to put it in the Cloud, if it's in Canada or Germany or wherever, the machine can adjudicate and make those decisions. >> And that's what the AI, so in that case you're using AI inside of the storage system versus what we talked about with our other software that makes our storage systems a great platform for other AI workloads that are not, if you will, AI for storage. AI for everything else, cars or hospitals or resume analysis. That's what the platform can, but we put all this AI inside of the system 'cause there aren't that big, giant, global, Fortune 500 has 55 storage admins and in 2007 or eight, they had 100, but they've quintupled the amount of storage easily if not 10x'd it, so who's going to manage that? Automation. >> Guys, good discussion. Not everyday, boring, old storage. It's talking about intelligence, real intelligence this time. Eric, Sam, thanks very much for coming to theCUBE. Great to see you guys again. >> Thank you. >> Thank you. >> You're welcome. Alright, keep it right there everybody. Stu and I will be back with our next guest shortly, right after this break. John Furrier is also here. IBM Think, Day four, you're watching theCUBE. Be right back. (tech music)

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019, brought to you by IBM. >> Welcome back to intermittently sunny San Francisco, this is theCUBE, the leader in live tech coverage. We're here at day four at IBM Think. My name is Dave Vellante. I am here with Stu Miniman. John Furrier is also here. Wall to wall coverage Stu. The second Think, first big show really of the year at Moscone. The new Moscone, Armando Ortiz is here. He is the vice president and partner from Mobile & Extended Reality Leader at IBM iX. An interesting part of IBM that you may not know about. Armando, welcome to theCUBE, thanks for coming on. >> Thanks for having me. >> So tell us a little bit about iX. >> So IBM iX is a part of IBM services. We focus on user experiences, whether it's a consumer experience or an employee experience. And the we look at user experience it really kind of sticks together and allow you to unlock the value of all the technology investments that companies are making. >> So, you guys are not making headsets, or are you? >> No we don't make hardware, we just put hardware to work. >> So talk a little bit about the sort of state of whether its augmented reality or extended reality. Lay out the terminology for us if you would. >> Sure, sure. As part of the role I have I lead our mobile practice as well as the extended reality practice and this kind of all related together. We use the term extended reality to kind of encompass all of the different technologies along that spectrum from augmented reality to mixed reality to virtual reality. Of course there are a lot of technologies whether it's the glasses on your face like the wearables or it's in your hand as a lot of mobile platforms today like Apple's ARKit and Google's ARCore allow you to have AR experienced within your mobile apps. >> Yeah, I wonder if you can expand a little bit on that? We're all ready for the role out of 5G and that's going, holds the promise at least for a lot more band width and a lot more applications and that's one of the lynch pins we understand kind of make your world more of a reality. When do we see that role out? What devices are going to happen? You got a preview of the next iPhone for us? >> I certainly don't have a preview of the next iPhone, even though I do lead the Apple partnership for us in North America, the Apple IBM partnership. When you look at 5G, obviously some of the use cases for extended reality in enterprise are around field services and 5G will have an amazing impact on the ability. Not only because of the band width but also the low latency that you have for 5G. So we're excited to see that role out in the different markets around the world and you know the pilots and things that are starting this year. There are going to be a lot of great devices and I think for handsets all the way to the wearables. It'll really allow us to put more use cases on these devices. >> Can you walk us through some of those use cases? Any specific customer examples you have that may make our audience understand a little bit more what's really available today. Sure, I mean in the XR space or in the extended reality space there's a lot that we learned through what we've done in mobile for years. I mean, even our Apple partnership for the past five years and things we've done across the 16 industries we work on. But the initial sort of wave one use cases that we're really seeing today kind of follow along these categories of work related use cases that are like in field services, training related use cases that go all the way from virtual reality immersive training like teaching someone how to do something in a dangerous situation where you want to simulate that. All the way to on the job sort of training and step-by-step guidance that you can get with AR. Step one attach the cable here. Step two, check this over here. Those kind of use cases and then into use cases related to shopping and retail. If you look at what augmented reality is going to do for shopping and retail allow people to assess sort of fit and purpose of something they want to buy. Does it fit in my home? Does it fit in my life? And then also even in the stores as people in retail sort of navigate a store they can use AR to help understand. Add all that metadata to the in store experience that we're gotten used to in our online experiences. And the last broad category we sort of call it share ideas or sharing of ideas, which kind of expands the game from collaboration to even having AR brochures and augmented realty tools to help people understand a product or a service that you're offering. Imagine that we can just kind of expand a piece of equipment here on the table, walk through it and help understand how that piece of equipment is going to help your business. >> You're giving me flashbacks. I remember IBM had a huge initiative in like Second Life and it was like come build an island and we're going to do recruiting and things like that. So, tell us why this generation is, going to be better for business and not have everyone put some money in and have it stolen by you know. >> Not as goofy. >> It's funny you should ask that, the Second Life topic actually came up with someone I was speaking to yesterday. It's come up before. I think there is a significant difference between what Second Life was trying to be and what extended reality is going to be and it already is. I mean when you look at extended reality today, I think one important thing to think about this is not future tech, this is not some sort of dream of sort of Ready Player One type of situation. But more, it's looking at real enterprise use cases that are already driving a value; time savings on inspections, productivity enhancements for people assembling, consistency and increase safety. All the key performance indicators and value drivers we have for mobile. So there's a real path to business value and the uses are much clearer than it might have been in the days of Second Life. >> Less mistakes, less rework. Armando, what kind of infrastructure would a consumer need? You gave the example of retail for instance, what kind of infrastructure would I need? Am I just, is it just my mobile home? Am I going to wear headsets, what does that look like? >> So when we talk about extended reality, we tend to keep one foot in today and one foot in the future cause its changing so fast. When you talk about retail there is a sale associate side of things that might be helping you decide an automotive. Maybe you're looking at configuring a car right in front of you or in a retail store maybe you're looking to look at a piece of furniture or something that's not on the show room floor. Now those experiences can start today with tablets and iPhones and other devices. But we see also as well devices that people be wearing wearables that are available today and that trend moving that glass kind of from your hand to your face is going to be something that is really going to be accelerated. >> So, this is maybe how a piece of clothing will fit or what a couch might look like in a particular room, is that right? >> Yeah. >> And you would envision that people will purchase this infrastructure for a variety of uses. Not only to see how things look but maybe there's gaming. So it's a multi-use kind of environment or not necessarily? Is it more specialized to use it? >> No absolutely, it's important, it's a good thing that you brought up sort of gaming as well. Because, obviously we all know that gaming has been kind of at the fore front for virtual reality but when you look at gaming and entertainment those are also going to include many use cases. When we look at the enterprise side we're kind of focused on those other wave one use cases. But I also expect in the sort of share ideas category I spoke of marketing and sales activities will also include AR experienced to help people understand the product or service that you're positioning. >> What's the state of adoption? We always joke about google glass. Remember the movie The Jerk with the Opti-Grab and the guy was cross-eyed? So that didn't take off but what's the state of hardware and hardware adoption today? >> So I think what's unique about this technology and what's happening now, the technology we already all have in our hands on our mobile phones is already there and that's where you're going to see it happen first. I think the numbers by next year are like 3.4 billion phones will have an AR capability so the technology is already with us. The next sort of technology set that we're talking about is getting to the wearables and of course we see things today in the VR space that's much more available in the consumer side, things like the oculus go. In the enterprise space you also have headsets from many manufacturers that maybe grew up doing things in the military that are now more commercially available. Things like someone trying to repair something that needs to be hand free. We're seeing those technologies readily available in the enterprise. >> Tell about how AI fits into this new world? >> That's a great question. If you think about it its really kind of a really great combination. You take XR, extended reality, so whether its AR or VR and you add AI to it you can kind of give AI the ability to kind of enter the 3D space. So as you think about AI solutions that we had in the mobile world where you might be using AI to solve a problem, diagnose a problem, visual diagnostics, acoustic detection AI can kind of give sort of super powers to an employee. At the same time we see that the experiences that we have in the extended reality space get really enhanced because you now have the ability to democratize expertize with AI. You take all of the expertize of your organization and that one technician whose only been there for 10 days now has the power of your entire collective knowledge. >> What about privacy? Anytime you hear some of these and I think about you can have wearables out there, there is concern about you know with facial recognition is going to be everywhere my privacy is going to be invaded. What's IBM positioning? Where does that fit in this whole environment? >> Of course we take privacy very seriously. When we talk about our AI and Watson you know your data is your data. If you look at some of the things, I mean, you'll make decisions, enterprises will make decisions on the same way they do with mobile devices. Is it okay to have a camera in this environment? And if I do have a camera in this environment, what's my cloud strategy and where am I going to host this data to make sure that I have not just privacy but also IP concerns, considered? All of the same things we've learned in the mobile world are going to apply to this and it'll get even a little more important as you think of the different types of sensors that are required to make these experiences happen. >> I wonder if you could help us understand about the pre-requisites to do things like technician actually trouble shooting a problem. Many of us have seen, we put on the glasses you walk around a show floor and you look at a new system or something and its really very cool. You can look inside and inspect the different layers. What has to be done, I'm inferring from what you're saying that a technician would be able to inspect live, real time a device and identify problems on that device. So what has be done? It has to be instrumented? It has to have cameras installed? What does the infrastructure build out look like? >> Sure, when you look at. Lets take the technician scenario for a moment and unpack that. When you look at that there are a couple of things that are already happening like a lot of major pieces of equipment are instrumented. So you have the internet of things data, sort of the data streams coming off of that. How do you make that available to that technician in the moment, sort of the vital signs of that piece of equipment that you might be operating on? So, having all that information like temperature and all the things from an IOT perspective, that's one angle of it. The other side of it really is when you think of failure of equipment usually at some point there's a situation that technician may not have encountered before but maybe someone else has. Maybe you've already had a bunch of closed tickets on that three years ago. So having all that information available and using cognitive processing to kind of navigate that unstructured data, that will let you navigate that. Voice will be part of this interface as well. I think voice is an important part because you're going to be hands free and you're going to be having a dialogue with Watson, let's say to help diagnose a problem. >> How about healthcare? It's not something we've really talked about a lot. Just in terms of applications, whether its for the operating room of the future, remote guidance from doctor, training. Do you see those kind of use cases emerging? >> Yeah absolutely, all the way from training through execution of surgery and other things. This is where the 5G topic really comes into play because low latency is really required if you're talking about surgery and things like that. >> Give me a few minutes. >> You get that round trip of that signal going back and forth. I think when you think about the VR side of things for training is immensely powerful. The AR side for during execution of procedures will also be powerful as well and it comes back to that general theme od democratizing expertize. One expert that's physically on this part of the world can serve many people that need their services around the world. >> It sounds like there are a lot of uncertainties in terms of how this is going to evolve. First of all od the a fair statement? Given that, not withstanding that can you give us a sense of expectations for how it will evolve and the adoption levels that you expect over the next two to five years. >> Five years is a long horizon for this technology. >> Too long, too long perhaps so what's more fair, 18 months? >> Lets talk more immediate. I think when you look at, there may be some uncertainty in terms of which use cases will drive the most value but there are already many use cases that companies are probably sharing information out. Like some companies, especially inspection use cases, you know there is a company that published 96% savings on time because really you are using AR to document. Okay inspect this point, this point, this point, this point. Assembly use cases, diagnostics with AI and AR are working together. All of these are already happening, so what I think is going to happen is enterprises are going to be able to more and more easily justify the spend to make these investments because the RY is rapid. Just like the RY in mobile was rapid for enterprise, the RY in XR will be extremely rapid. >> Armando for people who didn't come to IBM Think, give them a little taste of what they missed from an iX stand point. Some of the conversations that you've been having. >> Yeah, when we look at, I mean iX across the IBM Think we've had a lot of conversations and a lot of sessions around how experience is really driving the business value and also around marketing technologies and marketing services and all of the things that relate to experience on the consumer side and the employee side. We're really enjoyed some great show casing of our client stories and the works we've done. Everything from mobile to commerce to marketing platforms to sales floors across everything we do in the IBM services part that we're in. >> How long has this been around? >> IBM iX? >> Yeah. >> IBM iX has been a part of IBM originally since the 96 Olympics in Atlanta. I've been with IBM about 25 years and this space is kind of like really evolved in terms of the position of user experience and design. IBM has become really a design focused company and you look at enterprise design thinking in everything we do so this is really a part of our business that's really become focal point as companies start thinking more about design. >> Wow, it's been a long time but it's certainly not mature but it's a revenue generating business obviously. >> Yeah and a very high growth part of the company. >> Awesome, well Armando thanks so much for sharing this part of IBM that's not well known. Really exciting futures and I really appreciate you coming on theCUBE. >> Thank you very much, I appreciate being here. >> Alright, keep it right there everyone. Stu and I will be back. Day four, IBM Think, we're at Moscone. Stop by, we're at Moscone North. I'm Dave Vellante, Stu Miniman and John Furrier is here. We'll be right back, you're watching theCUBE. (techno music)

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019. Brought to you by IBM. >> Welcome back, live here in San Francisco, it's theCUBE's coverage of IBM Think 2019. I'm John Furrier--Stu Miniman. Stu, four days, we're on our fourth day, the sun's shining, they've shut down Howard Street here at IBM. Big event for IBM, in San Francisco, not Las Vegas. Lot of great cloud action, lot of great AI data developers. Great story, good to see you again. Our next two guests, Julie Lockner, Director, Offering Management, Portfolio Operations at IBM, Data+AI, great to see you. >> Thank you, it's great to see you too, thank you. >> And Jay Limburn, Director of Offering Management, IBM Data+AI, thanks for coming on. >> Hey guys, great to be here. >> So, we've chatted many times at events, the role of data. So, we're religious about data, data flows through our blood, but IBM has put it all together now. All the reorgs are over, everyone's kind of, the table is set for IBM. The data path is clear, it's part of applications. It's feeding the apps. AI's the key workload inside the application. This is now a fully set-up group, give us the update, what's the focus? >> Yeah, it's really exciting because, if you think about it, before, we were called IBM Analytics, and that really is only a part of what we do. Now that we're Data+AI, that means that not only are we responsible for delivering data assets, and technology that supports those data assets to our customers, but infusing AI, not only in the technologies that we have, but also helping them build applications so they can fuse AI into their business processes. >> It's pretty broad, I mean, data's very much a broad swath of things. Analytics, you know, wrangling data, setting things up, cataloging them. Take me through how you guys set this up. How do you present it to the marketplace? How are clients engaged with it? Because it's pretty broad. But it could be, it needs to be specific. Take us through the methodology. >> So, you probably heard a lot of people today talk about the ladder to AI, right? This is IBM's view of how we explain our client's journey towards AI. It really starts at the bottom rung of the ladder, where we've got the collection of information. Collect your data. Once you've collected your data, you move up to the next rung, which is the Organize. And this is really where all the governance stuff comes in. This is how we can provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information, so they can actually use that in AI. That's where all the data science capabilities come in, allowing people to actually be able to consume that information. >> So, the bottom set is just really all the hard and heavy lifting that data scientists actually don't want to do. >> And writing algorithms, the collecting, the ingesting of data from any source, that's the bottom? And then, tell me about that next layer up, from the collection-- >> So, Collect is the physical assets or the collection of the data that you're going to be using for AI. If you don't get that foundation right, it doesn't really make sense. You have to have the data first. The piece in the middle that Jay was referring to, that's called Organize, our whole divisions are actually organized around these ladders to AI, so, Collect, Organize, Analyze, Infuse. On the Organize side, as Jay was mentioning, it's all about inventorying the data assets, knowing what data you have, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available so you can use your data, and the users are those data scientists, they're the analytics teams, they're the operation organizations that need to be able to build their solutions on top of trusted data. >> So, where does the Catalog fit in? Which level does that come into? >> Yeah, so, think of the Data Catalog as the DNS for data, all right? It's the way in which you can provide a full view of all of your information. Whether it's structured information, unstructured information, data you've got on PRAM and data you've got in a cloud somewhere. >> That's in the Organize layer, right? >> That's all in the Organize layer. So, if you can collect that information, you can then provide capabilities that allow you to understand the quality of that data, know where that data's come from, and then, finally, if you serve that up inside a compelling, business-friendly experience, so that a data scientist can go to one place, quickly make a decision on if that's the right data for them, and allow them to go and be productive by building a data science model, then we're really able to move the needle on making those data science organizations efficient, allowing us to build better models to transform their business. >> Yeah, and a big part of that is, if you think about what makes Amazon successful, it's because they know where all their products are, from the vendor, to when it shows up on the doorstep. What the Catalog provides is really the similar capability of, I would call it inventory management of your data assets, where we know where the data came from, its source--in that Collect layer-- who's transformed it, who's accessed it, if they're even allowed to see it, so, data privacy policies are part of that, and then being able to just serve up that data to those users. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the Analyze layer. >> But one of the things we've been digging into on theCUBE is, is data the next flywheel of innovation? You know, it used to be I just had my information, many years ago we started talking about, "Okay, I need to be able to access all that other information." We hear things like 80% of the data out there isn't really searchable today. So, how do you see data, data gravity, all those pieces, as the next flywheel of innovation? >> Yeah, I think it's key. I mean, we've talked a lot about how, you can't do AI without information architecture. And it's absolutely true. And getting that view of that data in a single location, so it is like the DNS of the internet. So you know exactly where to search, you can get hold of that data, and then you've got tools that give you self-service access to actually get hold of the data without any need of support from IT to get access to it. It's really a key-- >> Yeah, but to the point you were just asking about, data gravity? I mean, being able to do this where the data resides. So, for example, we have a lot of our customers that are mergers and acquisitions. Some teams have a lot of data assets that are on-premises, others have large data lakes in AWS or Azure. How do you inventory those assets and really have a view of what you have available across that landscape? Part of what we've been focusing on this year is making our technology work across all of those clouds. And having a single view of your assets but knowing where it resides. >> So, Julie, this environment is a bit more complicated than the old data warehousing, or even what we were looking at with big data and Hadoop and all those pieces. >> Isn't that the truth? >> Help explain why we're actually going to be able to get the information, leverage and drive new business value out of data today, when we've struggled so many times in the past. >> Well, I think the biggest thing that's changed is the adoption of DevOps, and when I say adoption of DevOps and things like containerization and Docker containers, Kubernetes, the ability to provision data assets very quickly, no matter where they are, build these very quick value-producing applications based on AI, Artificial Intelligence APIs, is what's allowing us to take advantage of this multi-cloud landscape. If you didn't have that DevOps foundation, you'd still be building ETL jobs in data warehouses, and that was 20 years ago. Today, it's much more about these microservices-based architecture, building up these AI-- >> Well, that's the key point, and the "Fuse" part of the stack, I think, or ladder. Stack? Ladder? >> Ladder. (laughs) >> Ladder to success! Is key, because you're seeing the applications that have data native into the app, where it has to have certain characteristics, whether it's a realtime healthcare app, or retail app, and we had the retail folks on earlier, it's like, oh my god, this now has to be addressable very fast, so, the old fenced-off data warehouse-- "Hey, give me that data!"--pull it over. You need a sub-second latency, or milliseconds. So, this is now a requirement. >> That's right. >> So, how are people getting there? What are some use cases? >> Sure. I'll start with the healthcare 'cause you brought that up. One of the big use cases for technology that we provide is really around taking information that might be realtime, or batch data, and providing the ability to analyze that data very quickly in realtime to the point where you can predict when someone might potentially have a cardiac arrest. And yesterday's keynote that Rob Thomas presented, a demonstration that showed the ability to take data from a wearable device, combine it with data that's sitting in an Amazon... MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest! >> That's me! >> And then present that to a call center of cardiologists. So, this company that we work with, iCure, really took that entire stack, Organize, Collect, Organize, Analyze, Infuse, and built an application in a matter of six weeks. Now, that's the most compelling part. We were able to build the solution, inventory their data assets, tie it to the industry model, healthcare industry model, and predict when someone might potentially-- >> Do you have that demo on you? The device? >> Of course I do. I know, I know. So, here is, this is called a BraveHeart Life Sensor. And essentially, it's a Bluetooth device. I know! If you put it on! (laughs) >> If I put it on, it'll track... Biometric? It'll start capturing information about your heart, ECG, and on Valentine's Day, right? My heart to yours, happy Valentine's Day to my husband, of course. The ability to be able to capture all this data here on the device, stream it to an AI engine that can then immediately classify whether or not someone has an anomaly in their ECG signal. You couldn't do that without having a complete ladder to AI capability. >> So, realtime telemetry from the heart. So, I see timing's important if you're about to have a heart attack. >> Yeah. >> Pretty important. >> And that's a great example of, you mentioned the speed. It's all about being able to capture that data in whatever form it's coming in, understand what that data is, know if you can trust that data, and then put it in the hands of the individuals that can do something valuable with the analysis from that data. >> Yeah, you have to able to trust it. Especially-- >> So, you brought up earlier bias in data. So, I want to bring that up in context of this. This is just one example of wearables, Fitbits, all kinds of things happening. >> New sources of tech, yeah. >> In healthcare, retail, all kinds of edge, realtime, is bias of data. And the other one's privacy because now you have a new kind of data source going into the cloud. And then, so, this fits into what part of the ladder? So, the ladder needs a secure piece. >> Tell me about that. >> Yeah, it does. So, that really falls into that Organize piece of that ladder, the governance aspects around it. If you're going to make data available for self-service, you've got to still make sure that that data's protected, and that you're not going to go and break any kind of regulatory law around that data. So, we actually can use technology now to understand what that data is, whether it contains sensitive information, credit card numbers, and expose that information out to those consumers, yet still masking the key elements that should be protected. And that's really important, because data science is a hugely inefficient business. Data scientists are spending too much time looking for information. And worse than that, they actually don't have all the information available that they need, because certain information needs to be protected. But what we can do now is expose information that wasn't previously available, but protect just the key parts of that information, so we're still ensuring it's safe. >> That's a really key point. It's the classic iceberg, right? What you see: "Oh, data science is going to "change the game of our business!" And then when they realize what's underneath the water, it's like, all this set-up, incompatible data, dirty data, data cleaning, and then all of a sudden it just doesn't work, right? This is the reality. Are you guys seeing this? Do you see that? >> Yeah, absolutely. I think we're only just really at the beginning of a crest of a wave, here. I think organizations know they want to get to AI, the ladder to AI really helps explain and it helps to understand how they can get there. And we're able then to solve that through our technology, and help them get there and drive those efficiencies that they need. >> And just to add to that, I mean, now that there's more data assets available, you can't manually classify, tag and inventory all that data, determine whether or not it contains sensitive data. And that's where infusing machine learning into our products has really allowed our customers to automate the process. I mentioned, the only way that we were able to deploy this application in six weeks, is because we used a lot of the embedded machine learning to identify the patient data that was considered sensitive, tag it as patient data, and then, when the data scientists were actually building the models in that same environment, it was masked. So, they knew that they had access to the data, but they weren't allowed to see it. It's perfectly--especially with HIMSS' conference this week as well! You were talking about this there. >> Great use case with healthcare. >> Love to hear you speak about the ecosystem being built around this. Everything, open APIs, I'm guessing? >> Oh, yeah. What kind of partners are-- >> Jay, talk a little bit-- >> Yeah, so, one of the key things we're doing is ensuring that we're able to keep this stuff open. We don't want to curate a proprietary system. We're already big supporters of open source, as you know, in IBM. One of the things that we're heavily-invested in is our open metadata strategy. Open metadata is part of the open source ODPi Foundation. Project Egeria defines a standard for common metadata interchange. And what that means is that, any of these metadata systems that adopt this standard can freely share and exchange metadata across that landscape, so that wherever your data is, whichever systems it's stored in, wherever that metadata is harvested, it can play part of that network and share that metadata across those systems. >> I'd like to get your thoughts on something, Julie. You've been on the analyst side, you're now at IBM. Jay, if you can weigh in on this too, that'd be great. We, here, we see all the trends and go to all the events and one of the things that's popping up that's clear within the IBM ecosystem because you guys have a lot of business customers, is that a new kind of business app developer's coming in. And we've seen data science highlight the citizen data scientist, so if data is code, part of the application, and all the ladder stuff kind of falls into place, that means we're going to see new kinds of applications. So, how are you guys looking at, this is kind of a, not like the cloud-native, hardcore DevOps developer. It's the person that says, "Hey, I can innovate "a business model." I see a business model innovation that's not so much about building technology, it's about using insight and a unique... Formula or algorithm, to tweak something. That's not a lot of programming involved. 'Cause with Cloud and Cloud Private, all these back end systems, that's an ecosystem partner opportunity for you guys, but it's not your classic ISV. So, there's a new breed of business apps that we see coming, your thoughts on this? >> Yeah, it's almost like taking business process optimization as a discipline, and turning it into micro-applications. You want to be able to leverage data that's available and accessible, be able to insert that particular Artificial Intelligence machine learning algorithm to optimize that business process, and then get out of the way. Because if you try to reinvent your entire business process, culture typically gets in the way of some of these things. >> I thought, as an application value, 'cause there's value creation here, right? >> Absolutely. >> You were talking about, so, is this a new kind of genre of developer, or-- >> It really is, I mean... If you take the citizen data scientist, an example that you mentioned earlier. It's really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn't be just a practice that's held away for the hardcore developer anymore. It's about lowering the entry point with the set of tools. One of the things we have in Watson Studio, for example, our data science platform, is just that. It's about providing wizards and walkthroughs to allow people to develop productive use models very easily, without needing hardcore coding skills. >> Yeah, I also think, though, that, in order for these value-added applications to be built, the data has to be business-ready. That's how you accelerate these application development life cycles. That's how you get the new class of application developers productive, is making sure that they start with a business-ready foundation. >> So, how are you guys going to go after this new market? What's the marketing strategy? Again, this is like, forward-pioneering kind of things happening. What's the strategy, how are you going to enable this, what's the plan? >> Well, there's two parts of it. One is, when Jay was mentioning the Open Metadata Repository Services, our key strategy is embedding Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. So, really, that's first and foremost, is making sure that we can catalog and inventory data assets that might not necessarily be in the IBM Cloud, or in IBM products. That's really the first step. >> Absolutely. The second step, I would say, is really taking all of our capabilities, making them, from the ground up, microservices-enabled, delivering them through Docker containers and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on. And being able to optimize the runtime engines, whether it's data integration, data movement, data virtualization, based on data gravity, that you had mentioned-- >> So, something like a whole new developer program opportunity to bring to the market. >> Absolutely. I mean, there is, I think there is a huge opportunity for, from an education perspective, to help our customers build these applications. But it starts with understanding the data assets, understanding what they can do with it, and using self-service-type tools that Jay was referring to. >> And all of that underpinned with the trust. If you don't trust your data, the data scientist is not going to know whether or not they're using the right thing. >> So, the ladder's great. Great way for people to figure out where they are, it's like looking in the mirror, on the organization. How early is this? What inning are we in? How do you guys see the progression? How far along are we? Obviously, you have some data, examples, some people are doing it end-to-end. What's the maturity look like? What's the uptake? >> Go ahead, Jay. >> So, I think we're at the beginning of a crest of a wave. As I say, there's been a lot of discussion so far, even if you compare this year's conference to last year's. A lot of the discussion last year was, "What's possible with AI?" This year's conference is much more about, "What are we doing with AI?" And I think we're now getting to the point where people can actually start to be productive and really start to change their business through that. >> Yeah and, just to add to that, I mean, the ladder to AI was introduced last year, and it has gained so much adoption in the marketplace and our customers, they're actually organizing their business that way. So, the Collect divisions are the database teams, are now expanding to Hadoop and Cloudera, and Hortonworks and Mongo. They're organizing their data governance teams around the Organize pillar, where they're doing things like data integration, data replication. So, I feel like the maturity of this ladder to AI is really enabling our customers to achieve it much faster than-- >> I was talking to Dave Vellante about this, and we're seeing that, you know, we've been covering IBM since, it's the 10th year of theCUBE, all ten years. It's been, watching the progression. The past couple of years has been setting the table, everyone seems to be pumping, it makes sense, everything's hanging together, it's in one group. Data's not one, "This group, that group," it's all, Data, AI, all Analytics, all Watson. Smart, and the ladder just allows you to understand where a customer is, and then-- >> Well, and also, we mentioned the emphasis on open source. It allows our customers to take an inventory of, what do they have, internally, with IBM assets, externally, open source, so that they can actually start to architect their information architecture, using the same kind of analogy. >> And an opportunity for developers too, great. Julie, thanks for coming on. Jay, appreciate it. >> Thank you so much for the opportunity, happy Valentine's Day! Happy Valentine's Day, we're theCUBE. I'm John Furrier, Stu Miniman here, live in San Francisco at the Moscone Center, and the whole street's shut down, Howard Street. Huge event, 30,000 people, we'll be back with more Day Four coverage after this short break.

>> Live, from San Francisco, it's theCUBE covering IBM Think 2019. Brought to you by IBM. >> Hello everyone, welcome back to theCUBE's live coverage here in San Francisco for IBM Think 2019. I'm John Furrier, Stu Miniman with theCUBE. Stu, it's been a great day. We're on our fourth day of four days of wall to wall coverage. A theme of AI, large scale compute with Cloud and data that's great. Great topics. Got two great guests here. Rohit Badlaney, who's the director of IBM Z As a Service, IBM Systems. Real great to see you. And Nataraj Nagaratnam, Distinguished Engineer and CTO and Director of Cloud Security at IBM and Hybrid Cloud, thanks for joining us. >> Glad to be here. >> So, the subtext to all the big messaging around AI and multi-cloud is that you need power to run this. Horsepower, you need big iron, you need the servers, you need the storage, but software is in the heart of all this. So you guys had some big announcements around capabilities. The Hyper Protect was a big one on the securities side but now you've got Z As a Service. We've seen Linux come on Z. So it's just another network now. It's just network computing is now tied in with cloud. Explain the offering. What's the big news? >> Sure, so two major announcements for us this week. One's around our private cloud capabilities on the platform. So we announced our IBM Cloud Private set of products fully supported on our LinuxOne systems, and what we've also announced is the extensions of those around hyper-secure workloads through a capability called the Secure Services Container, as well as giving our traditional z/OS clients cloud consumption through a capability called the z/OS Cloud Broker. So it's really looking at how do we cloudify the platform for our existing base, as well as clients looking to do digital transformation projects on-premise. How do we help them? >> This has been a key part of this. I want to just drill down this cloudification because we've been talking about how you guys are positioned for growth. All the REORG's are done. >> Sure, yeah >> The table's all set. Products have been modernized, upgraded. Now the path is pretty clear. Kind of like what Microsoft's playbook was. Build the core cloudification. Get your core set of products cloudified. Target your base of customers. Grow that and expand into the modern era. This is a key part of the strategy, right? >> Absolutely right. A key part of our private cloud strategy is targeted to our existing base and moving them forward on their cloud journey, whether they're looking to modernize parts of their application. Can we start first with where they are on-premise is really what we're after. >> Alright, also you have the Hyper Protect. >> Correct. >> What is that announcement? Can you explain Hyper Protect? >> Absolutely. Like Rohit talked about, taking our LinuxOne capabilities, now that enterprise trusts the level of assurance, the level of security that they're dependent on, on-premise and now in private cloud. We are taking that further into the public cloud offering as Hyper Protect services. So these are set of services that leverage the underlyings of security hardening that nobody else has the level of control that you can get and offering that as a service so you don't need to know Z or LinuxOne from a consumption perspective. So I'll take two examples. Hyper Protect Crypto Service is about exposing the level of control. That you can manage they keys. What we call "keep your own keys" because encryption is out there but it's all about key management so we provide that with the highest level of security that LinuxOne servers from us offer. Another example is database as a service, which runs in this Hyper Secure environment. Not only encryption and keys, but leveraging down the line pervasive encryption capabilities so nobody can even get into the box, so to say. >> Okay, so I get the encryption piece. That's solid, great. Internet encryption is always good. Containers, there's been discussions at the CNCF about containers not being part of the security boundaries and putting a VMware around it. Different schools of thought there. How do you guys look at the containerization? Does that fit into Secure Protect? Talk about that dynamic because encryption I get, but are you getting containers? >> Great question because it's about the workload, right? When people are modernizing their apps or building cloud-native apps, it's built on Kubernetes and containers. What we have done, the fantastic work across both the IBM Cloud Private on Z, as well as Hyper Protect, underlying it's all about containers, right? So as we deliver these services and for customers also to build data services as containers or VM's, they can deploy on this environment or consume these as a compute. So fundamentally it's kubernetes everywhere. That's a foundational focus for us. When it can go public, private and multicloud, and we are taking that journey into the most austere environment with a performance and scale of Z and LinuxONE. >> Alright, so Rohit, help bring us up to date. We've been talking about this hybrid and multi-cloud stuff for a number of years, and the idea we've heard for many years is, "I want to have the same stack on both ends. I want encryption all the way down to the chip set." I've heard of companies like Oracle, like IBM say, "We have resources in both. We want to do this." We understand kubernetes is not a magic layer, it takes care of a certain piece you know and we've been digging in that quite a bit. Super important, but there's more than that and there still are differences between what I'm doing in the private cloud and public cloud just naturally. Public cloud, I'm really limited to how many data centers, private cloud, everything's different. Help us understand what's the same, what's different. How do we sort that out in 2019? >> Sure, from a brand perspective we're looking at private cloud in our IBM Cloud Private set of products and standardizing on that from a kubernetes perspective, but also in a public cloud, we're standardizing on kubernetes. The key secret source is our Secure Services Container under there. It's the same technology that we use under our Blockchain Platform. Right, it brings the Z differentiation for hyper-security, lockdown, where you can run the most secure workloads, and we're standardizing that on both public and private cloud. Now, of course, there are key differences, right? We're standardizing on a different set of workloads on-premise. We're focusing on containerizing on-premise. That journey to move for the public cloud, we still need to get there. >> And the container piece is super important. Can you explain the piece around, if I've got multi-cloud going on, Z becomes a critical node on the network because if you have an on-premise base, Z's been very popular, LinuxONE has been really popular, but it's been for the big banks, and it seems like the big, you know, it's big ire, it's IBM, right? But it's not just the mainframe. It's not proprietary software anymore, it's essentially large-scale capability. >> Right. >> So now, when that gets factored into the pool of resources and cloud, how should customers look at Z? How should they look at the equation? Because this seems to me like an interesting vector into adding more head room for you guys, at least on the product side, but for a customer, it's not just a use case for the big banks, or doing big backups, it seems to have more legs now. Can you explain where this fits into the big picture? Because why wouldn't someone want to have a high performant? >> Why don't I use a customer example? I had a great session this morning with Brad Chun from Shuttle Fund, who joined us on stage. They know financial industry. They are building a Fintech capability called Digital Asset Custody Services. It's about how you digitize your asset, how do you tokenize them, how you secure it. So when they look at it from that perspective, they've been partnering with us, it's a classic hybrid workload where they've deployed some of the apps on the private cloud and on-premise with Z/LinuxONE and reaching out to the cloud using the Hyper Protect services. So when they bring this together, built on Blockchain under the covers, they're bringing the capability being agile to the market, the ability for them to innovate and deliver with speed, but with the level of capability. So from that perspective, it's a Fintech, but they are not the largest banks that you may know of, but that's the kind of innovation it enables, even if you don't have quote, unquote a mainframe or a Z. >> This gives you guys more power, and literally, sense of pretty more reach in the market because what containers and now these kubernetes, for example, Ginni Rometty said "kubernetes" twice in her keynote. I'm like, "Oh my God. The CEO of IBM said 'kubernetes' twice." We used to joke about it. Only geeks know about kubernetes. Here she is talking about kubernetes. Containers, kubernetes, and now service missions around the corner give you guys reach into the public cloud to extend the Z capability without foreclosing the benefits of Z. So that seems to be a trend. Who's the target for that? Give me an example of who's the customer or use case? What's the situation that would allow me to take advantage of cloud and extend the capability to Z? >> If you just step back, what we're really trying to do is create a higher shorten zone in our cloud called Hyper Protect. It's targeted to our existing Z base, who want to move on this enterprise out journey, but it's also targeted to clients like Shuttle Fund and DAX that Raj talked about that are building these hyper secure apps in the cloud and want the capabilities of the platform, but wanted more cloud-native style. It's the breadth of moving our existing base to the cloud, but also these new security developers who want to do enterprise development in the cloud. >> Security is key. That's the big drive. >> And that's the beauty of Z. That's what it brings to the table. And to a cloud is the hyper lockdown, the scale, the performance, all those characteristics. >> We know that security is always an on-going journey, but one of the ones that has a lot of people concerned is when we start adding IoT into the mix. It increased the surface area by orders of magnitude. How do those type of applications fit into these offerings? >> Great question. As a matter of fact, I didn't give you the question by the way, but this morning, KONE joined me on stage. >> We actually talked about it on Twitter. (laughs) >> KONE joined us on stage. It's about in the residential workflow, how they're enabling here their integration, access, and identity into that. As an example, they're building on our IoT platform and then they integrate with security services. That's the beauty of this. Rohit talked about developers, right? So when developers build it, our mission is to make it simple for a developer to build secure applications. With security skill shortage, you can't expect every developer to be a security geek, right? So we're making it simple, so that you can kind of connect your IoT to your business process and your back-end application seamlessly in a multi-cloud and hybrid-cloud fashion. That's where both from a cloud native perspective comes in, and building some of these sensitive applications on Hyper Protect or Z/LinuxONE and private cloud enables that end to end. >> I want to get you guys take while you're here because one of the things I've observed here at Think, which is clearly the theme is Cloud AI and developers all kind of coming together. I mean, AI, Amazon's event, AI, AI, AI, in cloud scale, you guys don't have that. But developer angle is really interesting. And you guys have a product called IBM Cloud Private, which seems to be a very big centerpiece of the strategy. What is this product? Why is it important? It seems to be part of all the key innovative parts that we see evolving out of the thing. Can you explain what is the IBM Cloud Private and how does it fit into the puzzle? >> Let me take a pass at it Raj. In a way it is, well, we really see IBM Cloud Private as that key linchpin on-premise. It's a Platform as a Service product on-premise, it's built on kubernetes and darker containers, but what it really brings is that standardized cloud consumption for containerized apps on-premise. We've expanded that, of course, to our Z footprint, and let me give you a use case of clients and how they use it. We're working with a very big, regulated bank that's looking to modernize a massive monolithic piece of WebSphere application server on-premise and break it down into micro-services. They're doing that on IBM Cloud Private. They've containerized big parts of the application on WebSphere on-premise. Now they've not made that journey to the cloud, to the public cloud, but they are using... How do you modernize your existing footprint into a more containerized micro-services one? >> So this is the trend we're seeing, the decomposition of monolithic apps on-premise is step one. Let's get that down, get the culture, and attract the new, younger people who come in, not the older guys like me, mini-computer days. Really make it ready, composable, then they're ready to go to the cloud. This seems to be the steps. Talk about that dynamic, Raj, from a technical perspective. How hard is it to do that? Is it a heavy lift? Is it pretty straight-forward? >> Great question. IBM, we're all about open, right? So when it comes to our cloud strategy open is the centerpiece offered, that's why we have banked on kubernetes and containers as that standardization layer. This way you can move a workflow from private to public, even ICP can be on other cloud vendors as well, not just IBM Cloud. So it's a private cloud that customers can manage, or in the public cloud or IBM kubernetes that we manage for them. Then it's about the app, the containerized app that can be moved around and that's where our announcements about Multicloud Manager, that we made late last year come into play, which helps you seamlessly move and integrate applications that are deployed on communities across private, public or multicloud. So that abstraction venire enables that to happen and that's why the open... >> So it's an operational construct? Not an IBM product, per say, if you think about it that way. So the question I have for you, I know Stu wants to jump in, he's got some questions. I want to get to this new mindset. The world's flipped upside down. The applications and workloads are dictating architecture and programmability to the DevOps, or infrastructure, in this case, Z or cloud. This is changing the game on how the cloud selection is. So we've been having a debate on theCUBE here, publicly, that in some cases it's the best cloud for the job decision, not a procurement, "I need multi-vendor cloud," versus I have a workload that runs best with this cloud. And it might be as if you're running 365, or G Suite as Google, Amazon's got something so it seems to be the trend. Do you agree with that? And certainly, there'll be many clouds. We think that's true, it's already happened. Your thoughts on this workload driving the requirements for the cloud? Whether it's a sole purpose cloud, meaning for the app. >> That's right. I'll start and Rohit will add in as well. That's where this chapter two comes into play, as we call Chapter Two of Cloud because it is about how do you take enterprise applications, the mission-critical complex workloads, and then look for the enablers. How do you make that modernization seamless? How do you make the cloud native seamless? So in that particular journey, is where IBM cloud and our Multicloud and Hybrid Cloud strategy come into play to make that transition happen and provide the set of capabilities that enterprises are looking for to move their critical workloads across private and public in bit much more assurance and performance and scale, and that's where the work that we are doing with Z, LinuxONE set of as an underpinning to embark on the journey to move those critical workloads to their cloud. So you're absolutely right. When they look at which cloud to go, it's about capabilities, the tools, the management orchestration layers that a cloud provider or a cloud vendor provide and it's not only just about IBM Public Cloud, but it's about enabling the enterprises to provide them the choice and then offer. >> So it's not multicloud for multicloud sake, it's multicloud, that's the reality. Workload drives the functionality. >> Absolutely. We see that as well. >> Validated on theCUBE by the gurus of IBM. The cloud for the job is the best solution. >> So I guess to kind of put a bow on this, the journey we're having is talking about distributed architectures, and you know, we're down on the weeds, we've got micro-services architectures, containerization, and we're working at making those things more secure. Obviously, there's still a little bit more work to do there, but what's next is we look forward, what are the challenges customers have. They live in this, you know, heterogeneous multicloud world. What do we have to do as an industry? Where is IBM making sure that they have a leadership position? >> From my perspective, I think really the next big wave of cloud is going to be looking at those enterprise workloads. It's funny, I was just having a conversation with a very big bank in the Netherlands, and they were, of course, a very big Z client, and asking us about the breadth of our cloud strategy and how they can move forward. Really looking at a private cloud strategy helping them modernize, and then looking at which targeted workloads they could move to public cloud is going to be the next frontier. And those 80 percent of workloads that haven't moved. >> An integration is key, and for you guys competitive strategy-wise, you've got a lot of business applications running on IBM's huge customer base. Focus on those. >> Yes. >> And then give them the path to the cloud. The integration piece is where the linchpin is and OSSI secure. >> Enterprise out guys. >> Love encryption, love to follow up more on the secure container thing, I think that's a great topic. We'll follow-up after this show Raj. Thanks for coming on. theCUBE coverage here. I'm John Furrier, Stu Miniman. Live coverage, day four, here live in San Francisco for IBM Think 2019. Stay with us more. Our next guests will be here right after a short break. (upbeat music)

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Hey, welcome back here when we're here. Live in San Francisco for IBM. Think twenty nineteen, two cubes Exclusive coverage. I'm over here, students to it's been four days. Were our fourth day powered through a lot of interviews. Obstructing the Civic Lanois number one live event covers a Cuba to great guests here. Ashutosh Mooney, vice president, Applications services with an IBM and carried to lose chief technology officer nationwide Building society in the UK Great to have you guys. Thank you, John, for applications. Big part of the focus because the applications air now dictating the data strategy. The II with a and you could cloud multi cloud underneath. So the chained, changing market requirements around what, after doing are super important? All this is a focus. It's dictating that the infrastructure what to do so that this is the key to the cloud. Talk about what you guys are doing. >> Absolutely, absolutely, in fact, not just for IBM. For clients, mostly for them to be able to ready for their customers, they need to make sure that their applications are up there for their customer experience as well. What we're seeing is most of these supper clients today are saying that all the work that they have done in past for the last five, ten years that's the core that they have been in there trying to look at how they can minimize the spend on that and maximize the spending a ll. The customer facing applications like to enhance the customer experience >> they call and you call that the workload? Oh, yes. Load is code for applications. Carry your customer of IBM. Let me explain what you guys do first. Then we can talk about some things you're working on, >> So we are a large, UK based mutual building society. We have about fifteen million members in the U. K. But you can think of us as a bank. In many respects, most people do. Challenge throws us, as you said, is basically we have thirty or forty years of legacy technology. We need to transform that technology and also bill the next generation digital services alongside that technology. So if Rose, it's the combination of how do we transform that legacy core whilst also building from you? >> And what are some of the use case is that the new technology going bring you because containers has been great with legacy because you don't kill the old to bring in the new. As you look at the modern modernization journey, you're on What is guiding principles? One things you guys are looking at, how you guys thinking that through? >> Okay, so a number of things. One is we've been on a thirty year journey towards looser and looser coupling on smaller and smaller micro services. So what you're starting to see is big applications, monolithic applications being broken down into services and the micro services. So for us, the key is the smaller and smaller micro services. The more agility we can create more value great. And that loose coupling them becomes really important because that then allows us to deliver a high level of parallelism in development in change. So those are two key areas. >> It has it going today. Good scar tissue. You learning its >> learning and its iterating and it's failing and its understanding. But the main thing is, you know, the more we do, the more we learn, the more we can then build that back into Nick's situation. >> Actually, I always love to hear, especially the financial services ones that have been around a while that that modernization and how they do that, I couldn't help but notice. You're both wearing the, you know, I heart a I the shirts. So if you connect the dots for us between that application modernization and the wave of a ay >> yeah. So I heard that Tom fail fast and fail regular. I mean, it's all good until you actually have atleast one success, right? Failing fast is good, but you cannot escape feeling. So where it comes into play is primarily making sure that you're basing your those decisions on what have been proven right in Pastor's. Well, so what we have seen, especially for financial services, is even though the system's off engagement has changed the fundamental principles on which the banking services all the insurance services operator has not changed. So you're still wearing the same set of services just in different ways. The expectation of the client has changed, but the services remain the same. So our ability to be ableto look at what we have been doing in past which services makes sense to be Microsoft's enabled us getting talked about. It's not that you just take all the functions and enable them. That's where we're able to bring value Tour Kari. What's the impact >> on this on your ultimate and user >> better value? So for us, it's about helping our members, who are customers, to make better financial decisions on. To do that, they need data. So what we're trying to do is to really take that Legacy estate, which is really about locking data into the course. Or we can use it trying to liberate that day to get it out into the hands of our members so they could make better decisions on a eyes were really keep part of >> you. I mean that that was what we think back to. That wave of big data was the I should be able to have smaller companies, you know, not take years and millions of dollars to be able to do that. Tell us what's different about, you know today in a I that that we might not have been able to do five years >> ago. There's a couple of things, really. So one is compute power. So what you're seeing really is eyes is not necessarily advancing massively in terms of the algorithms and the approaches in the methodologies. What you're seeing, though, is compute power in storage capacity growing at an exponential rate store. So what it's doing is enabling those algorithms to work in a way that they've never been able to do before. We're getting to value quicker because the time it takes to reach that value is much shorter. >> I want to get your perspective on you mentioned parallel breaking down, decoupling things with looser sets the services. This is certainly the cloud way make AP eyes have micro services. Big part of it. How is that going from a culture standpoint? Because this is one of the things we hear all the time is it's a cultural journey to one. Get people lined up with that. And then what if some of the business benefits that you see what this parallel isn't? His efficiency is an innovation. Where do you see that culture? What did What did you do to change the culture? Go. Cheers. Um, this is what people want to know about. >> So in fact, what we're seeing is a majority of the clients have started to look into this because everybody else was because somebody digital native out there was doing it, so they some of them actually last on too quickly. They have not been ableto change their internal culture within the organization when the customers were ready, but their internal organizations or not. But I think plants like Cup NBS have sought out a fairly good strategy, and it will be great to get if you can >> share with your secret sauce that you like Carrot Stick. They were gonna go this way or you burn the boats, as they say at the How did you get people to go in the right direction? >> For us? There's a really, really important related past this the culture of the people from a culture perspective. You know, we've got teams of people who have been doing phenomenal pieces of work for thirty forty years coming to the end of their career. And you know, the technology that we're using again, we're looking at and the service life. So how do we how do we get away from that world where we're constantly focusing on the legacy to start focusing on new technology? So it's bringing in new people with new ideas. It's changing the way we work, so we started to focus on things like our child. They've ops, automation, new ways of working to allow people to really sort of liberate away from the old ways of working and give them new ideas and new opportunities. That's part of that as well. There's a couple of things in there for us which is really important. So one is bringing new technologies in bringing new people in that Khun, use those technologies. We also have to make sure we keep our own people trained up as well, so we can't forget the people that we've got. So it's it's a set of different things, >> and training is critical. Was gonna open source out there. It's like, you know, every years like a dog here, and you gotta keep up to date, Keep learning >> and all these aspects of procreation, right? So you cannot do it in isolation if you're doing it together. I mean, whether use design, thinking or not right, that's it. That's it. That's the way to do it. But I think the aspect of co creating in your end stakeholders and your own stakeholders, Orin more >> talk about more about that, cause this is a big team co creation we love doing with content were in the Q. We're doing it here with constant when you get into development. This is a new psychological dynamic, but also it's a productivity opportunity. Can you share what you're seeing there? Explain co creation a litte bit deeper >> Look so that we talk hypothetically, right? So from hypothetical perspective, if we were able to look at organization or a flat form where were able to access an amount ofthe computational power computation skills are programming skills. Our ability to be able to do the most creative expects for any use case and industry would be enormous. We just don't have that. We're limited to specific parts that were working with the Limited with specific employees that we have Andrea limited to the customers that were kids, and I think if we expand, so while we don't have, uh, handle off all the things that we haven't played. But if you are able to bring in our customers or internal stakeholders as well as our partners that we're working with and are able to build a common team and one of those common themes could be that I need to get you those services quickly and then figure out how to three can actually work in tandem we'll be able to make. >> How does that change your engagement model? Because I might be the same in eight days there, Miss Captain. Well, we used to do that before we usedto partner and understand their needs Bring solutions to the marketplace. Is it more software driven? So what's changed from the old way to the new way? Because I don't agree with you, by the way, I'm not I'm not a skeptic, but, yeah, that was what skeptic might say. >> Yeah, no, I think earlier what was happening was they were It was more offering leg and what I mean by offering letters these of the sex I have. And let's make these assets find the solutions. So what people will do is they will say this is the banking solution I have in this specific case and let's figure out what fifteen things I can >> do without those solutions. >> Approach now is different. They approach now is This is what the customer is demanding and the reason they're demanding is because customers expectation is based on there most recent experience that they had somewhere else, not necessarily with the bank. They may have experience and over, so when they have experienced that experience there, they want the similar services from the bank. So now the co creation model is actually starting from the other side of the equation rather than coming from Essex out. That's >> so it's flipped. The old model was hears. We got here's what you could do, Your limited and now it's like is what we want to do >> This ice >> program the infrastructure and focus on software to find agile. This is seems to be the new way. >> Let me add to that as well, because I think one of the things that we've done over the last year is really focusing on what our technology strategy, how this technology going change. Our business we've done is created a strategy where our ambition actually exceeds our ability to execute. So from a co creation perspective, we actually need really good partners are going to work with us in that context on be strong challenges br critical friend in the process. >> So it's more efficient and more productive. You get best of both worlds and the outcomes are more aligned via agile. Got me more acute on target. Many pretty much that >> getting Carrie actually love to get your perspective on like, what does it mean to have a cloud strategy today? We heard this week. You know, Jenny said, We're, you know, entering chapter to of the clouds. We took care of the twenty percent that was a little bit easier. We're getting eighty harder. Lots of customers I talked to. It's It's changing all the time, and things like hybrid and multi Cloud don't really mean much to them. Got serious in your shop, how you think of things. >> Great question. I think it's changing, and it's different from industry to industry. So I'm banking. The challenge for us has always been regulation has been the regulators pushing back on public cloud and saying, You know, we were nervous about that. Have you manage the security of the controls around that? So a lot of banking is focused on private Cloud? Can we adopt the technology in those banking's those styles of technology delivery in the private cloud way? But we're now starting to see that there is this shift towards public cloud with the economic advantage that public cloud house on the innovation that's going on in public cloud. It's becoming really attractive. So the strategy for us is about how do we make that happen? How do we build that multi cloud model? And then how do we move that sort of hybrid model from private to public and get the advantages of the different styles of cloud computing? >> Guys, Thanks for coming on, Given the inside love, this Dev ops Co creation model and really applications air driving the requirements now with programmable infrastructure. This is changing. The procurement is changing. The culture hiring strategist is really disrupted. This is really the digital transformation. It's all about creating great shop. Thanks for coming on. We appreciate final question while we're here. Thoughts on think this year in San Francisco. Libit Rainy February. That's okay, but all tightly together. What's your thoughts? What's the themes? What's your What's the top story here? >> Getting your pops? >> Whether it makes me feel like >> home is fantastic. Eso No, it's been It's been an amazing week. >> Lots of innovation, Lots of great conversation. So I really enjoyed it. >> Yeah, No, absolutely. I think we've gone around myself, even though we are definitely aware of what's going on in here. But I think there have been lots of partner ecosystem that has been here, and I think that collaboration has been great. Thank you. >> It's been great. Show a lot of inside Kaspar perspective. Thanks for sharing what your journeys on and some specifics Way appreciates. A cube coverage. I'm shoppers to Minuteman. Stay with us for a day, for we're four days a coverage. We're here on day for Stay with us for more after this short break.

>> Live from San Francisco, it's theCUBE. Covering IBM Think 2019, brought to you by IBM. >> Hello everyone welcome back to theCUBE's live coverage here at IBM Think 2019 in San Francisco, our exclusive coverage, day four, four days of coverage events winding down, I'm John Furrier with Stu Miniman, our next guest, Eric Herzog, CUBE alumni, CMO of IBM storage and VP of storage channels, Eric great to see you wearing the Hawaiian shirt as usual. >> Great, I can't come to theCUBE and not wear the Hawaiian shirt. You guys give me too much of a heart attack. >> Love getting you on to get down and dirty on storage and the impact of Cloud and infrastructure. First, you gave a great talk yesterday to a packed house, I saw that on social media, great response, what's going on for you at the show, tell us. >> So the big focuses for us are around four key initiatives. One is multi-cloud particularly from a hybrid perspective and in fact, I had three presenters with me, panelists and users, all of them were using multiple public cloud providers and all of them had a private cloud. One of them also was a software as a service vendor, so clearly they're really monetizing it. So that's one, the second one is around AI, both AI that we use inside of our storage to make it more efficient and more cost effective for the end user, but also as the platform for AI work loads and applications. Cyber resiliency is our other big theme, we've got all kinds of security, yes everyone is used to of course the Great Wall of China protecting you and then of course chasing the bad guy down when they breach you, but when they breach you it'd sure be nice if everything had data at rest encryption, or when you tiered out to the cloud you knew that it was being backed up or tiered out fully encrypted or how about something that can help you with ransomware and malware. So we have that, and that's a storage product not a regular, you know what you think of from a security vendor. So those are the big things that we've been harking on at the show. >> One of the things that I've observed, you've been very active out in the field, we've seen you at a lot of different events, Cisco Live, others, you guys have had an interesting storage product portfolio, very broad and specific leadership categories, but you also have the ability to work with other partners. This has been a big part of your strategy, you get the channels. What is, how would you summarize the current story around IBM storage and systems, because it's now an ingredient part of other people's infrastructure with cloud storage then becomes a key equation, how would you describe the IBM storage posture, product portfolio, what are the key things? >> So I think the key thing from a portfolio perspective, while it looks broad it's really four things. Software defined storage which we also happened to have bet on on array so theoretically that's one product line, same exact software. Other vendors don't do that, they have an array pack and you buy the array but if you buy their software defined storage it's actually different software, for us it's the same software. Then we have modern data protection and then we have management playing. That's kind of it. I do think one of the big differentiator for us, is even though we're part of IBM, we have already been working with everyone any way. So as we talked about at Cisco Live, for Spectrum Protect alone, our modern data protection platform, we have 400 small and medium cloud service providers all over the world that their back up service is based on it, so even though IBM Cloud has their own cloud division theoretically, we're enabling the competition but we've had that story at IBM storage now for four years. >> So storage anywhere basically is the theme here, AI anywhere storage anywhere, I mean it's not the official tagline but that's the philosophy with software. >> And that's yeah, so even if you think look at AI. We have an AI reference architecture with the power product line, we also have an AI reference architecture with the Nvidia product line, and we're working on a third one right now with another major server vendor because we want our storage to be anywhere there's AI and anywhere there's a cloud, big medium or small. >> Alright, Eric let's tease that out a little bit because I had a great conversation with an IBM fellow yesterday and we think back ten years ago, when you talked about hybrid and multi cloud, when you talked about an application it's "Am I spanning between environments? "Am I bursting between environments?" And architectures just didn't work that way. Today microservices architecture, there's pieces of the solution that can live in lots of environments, Compute I can spin up almost anywhere at any time, data doesn't move and I need to worry about my data, I need to worry about security so there's certain things that multi cloud like data protection, cyber resiliency, those kind of ones need to live everywhere, but when I talk about storage, I'm not moving my storage and my persistent database all over the place. So help us kind of tease out as to what is the multi everywhere and what is the you know the data that the Compute's going to actually move to that data, help us squint through that a little bit. >> So let's do the storage part first. So most applications, workloads, and use cases that are either business critical or mission critical are going to stay on prem, doesn't mean you can't use a public cloud provider for overflow whether that be IBM or Amazon or Microsoft or like I said the 400 cloud providers that we sell to that are not IBM, so but you're still going to have this hybridness where the data is partially on prem and off prem, in that case you're going to be using the public cloud provider, and by the way we did a survey, IBM did, and when you're looking enterprise, so let's say companies that are three or four billion US and up, anywhere in the world, you're seeing that most of them are using five or six different public clouds, whether that be salesforce.com which really is sales enablement software as a service. We have a startup that we work with who uses IBM's flash system and they do cyber security as a service, that's their whole business. So all of this software vendors that now deliver not on prem but you know over the cloud. Then you've got regular public cloud providers for file, block, and object for example we not only support IBM Cloud object storage protocol, but S3. So we have customers that put data out in S3, we have customers that put it out on other clouds because as you know S3's become the de facto standard so all the mid to small cloud providers use it. So I think what you've got is hybrid cloud is a sort of a subset of multi cloud and then multi cloud what you're seeing is because of software as service could even be geographic issues, we have a lot of data centers at IBM Cloud so do the three major cloud providers, but we are not in all 212 countries so if you have the law like in Canada where the data has to physically stay within the premises of Canada, now we all happen to have data centers that are big enough, but that doesn't mean we have data centers in every country, so you have legal issues, you have applications what applications are good, that make sense, what about pricing, and as you know some big companies still buy regionally. >> Eric, one of the things I'd love to get your perspective on is the SAS providers because if we look at the storage market in many ways, you know there was like the threat of public cloud, but really you got to follow the application, follow the data and as SAS proliferation happens, your data is going to go with that, you know you have them as customers in a lot of environments, what are you seeing from the SAS providers, how do they choose what offerings they have and how do they look at their data center versus public cloud mix? >> So when you look at a SAS provider, they've got a couple of different parameters that they look at which is why we've been very successful. One is performance, they already know their subject to the vicissitudes of the cloud so you can't have any bottle neck in your core data center because you're serving that app up, and if it's too slow or it doesn't work right, then of course the end user will go buy a different piece of software from another SAS provider. Second one is availability, because you have no idea when wiki bomb theCUBE is going to turn on that service, it could be the middle of the night right? If you guys expand to Asia, you guys will be asleep but your guy in Australia will be using that software, so it can't ever go down, so availability. Resiliency, can it handle pounding. If CUBE wiki bomb becomes ginormous, and you buy all these other analyst firms and the next thing you know the biggest analyst firm in the world, if you have thousands of people guess what now you're hammering on that software, so it's got to be able to take that workload abuse, right? And that's the kind of thing, so they look for that. >> That's scale basically, scale is critical. >> Right, they cannot have any issues of resiliency or availability and performance so A: they're usually going all flash, some of them will buy like a tape or the older all hard drive arrays as a backup store, ideal for IBM cloud object storage but again the main thing they focus on is flash because they're serving up that software. >> Let me ask you a question, so I know you've been in this business for a long time, storage you know everything about the speeds and fees but also you've been a historian too, you're on the front edge. IBM has got a killer strategy with cloud private, doing very well with Openshift and Redhat acquisition, you're now poised to essentially bring cloud scale across multiple clouds and with AI, it really puts storage at the center of the action. How is storage now positioned and how should customers think about storage, because scale is table stakes, enabling developers to program infrastructure as code, how does storage and how has it changed and how are you guys positioned to take advantage of that? How would you kind of explain that to a customer? >> Yeah so I think there's a couple of changes, first of all you're looking for a storage vendor which should be us, but you're looking for a storage vendor that is always making sure, for example when micro services first came out and containers, okay great except when containers came out and it's still a problem, you don't have storage consistency whereas in a VM ware or a hyper V or you know KVM environment, you do. So when you move things around, you don't lose the dataset, well we have persistency storage. So the key thing that you want to look for is a storage vendor that will stay on that leading edge as you move. Our copy data manager has an API so the developers can spit up their own environments but use real data, so as you guys know well from your pasts that the last thing you want to do is have the dev ops guy be developing things on faux datasets, try to put it in production, and then the real dataset doesn't work, at the same time if they put it out to a public cloud provider you could have a legal or security breach, right? So by being able to take modern data protection, as an example, and not just to have grandfather, father son back up, we all remember that I remember it better than you guys since I'm older, but that's back up right? It's not back up any more, it's modern data protection. You need to be able to take the snapshot, the replica or the back up dataset and use it for development, so you want a storage vendor that's going to be on the leading edge of that. We've done that at IBM on the Kenner side, the modern data protection side, and we'll continue to the do that. The whole multi cloud thing, IBM as you know is now all about multi cloud, what Redhat's been in, the storage division of IBM has been working with Redhat for 15 years. Going to the Redhat summit every year, I know you guys do theCUBE from there sometimes. >> You're on, but this is software defined so at the end of the day a software defined bet with arrays have paid off. >> Yes. >> You'd say that would be kind of a key linchpin. >> I would argue that, while there's some hardware aspects to it, so for example our flash core modules give us a big differentiator from a flash perspective, in general the number one differentiator for a strong, powerful array vendor is actually the underlying software code. The RAID stack, what you can wrap around it, file block and object support, what could you enhance, our Spectrum discover, allowing you to use metadata about unstructured data whether that be in the file space of the object store. That allows the data scientist to dramatically reduce the time it takes to prep the data when they're doing either AI or an analytic workload, so we just saved them money but we're really a storage company that came up with something that a data scientist could use because we understand how storage is at the central foundation and how you could literally use the metadata for something actually valuable, not to a storage person because a data scientist is not the storage guy of course. >> Yeah and Eric I would love to get your feedback, what are some of those key discussions you're having with customers here at the show? We've been talking a lot this week digital transformation, AI into everything there, are those some of the themes? What are the struggles that really the enterprises of today are facing and how your group's helping them? >> So one of the big things is understanding that it's going to be multi cloud and so because we've already been the Switzerland of the storage industry and working with every cloud provider, all the big ones, including ones that compete with our own sister division, but all the little small ones too, right? And all the software as service vendors we work with that we're the safe bet, you don't have to worry about it. Because whoever you pick, or for a big enterprise, in fact I had Aetna on stage with me and he said he's using seven different clouds, one of which is their private cloud and then six different cloud providers they use, and he said not counting salesforce.com and I forgot the other name, so really if you count the softwares there, she really got like nine clouds. She said I use IBM cause I know it's going to work with whoever, and you're not going to say oh I don't work with this one or that one. So that's been obviously making sure everyone realizes that, the whole company is embracing it as you saw and what we're going to do obviously with Redhat and continue for them to participate with all of their existing customer base that they've been doing for years. >> So you see multi cloud and sweet spot, that highlights your value proposition, would you say that to be true? >> I would say that and then the second one is around AI. All the storage vendors including us have had AI sort of inside, what I'll call inside of the box, inside of the array and use that to make the array better, but now with AI being ubiquitous from a work load perspective, you have to have the right foundation underneath that, again performance resiliency availability, if you're going to use AI in a giant car factory, and it's going to run all of those machines, you better make sure the thing never fails because then the assembly line goes down and those things are hundreds of millions of dollars of build every day. So that's the kind of thing you got to look for, so AI's got to have the right platform underneath it as well. >> Eric you have some reporting from the field as you're out in the, doing a lot of talks a lot of customers, give it a couple of anecdotal examples of where the leading edge is in storage and where are use cases that would be a good tell sign of where this kind of multi cloud is going. Can you just give some examples of the use cases, situation, and kind of why is that relevant for where everyone will be going? Where is the puck going to be, so I can skate to where the puck is, as they say. >> So from a multi cloud perspective, A: you've got to deal with how your company is structured, if you have a divisionalized company or one that really lets the regions make their own buy decisions, then you may have NTT Cloud in Japan, you may have Ali Baba in China, you may have IBM Cloud Australia, and then you might have Amazon in Latin America. And as IT guys you got to make sure you're dealing with that, and embrace it. One of the things I think from an IT perspective is why I'm wearing the Hawaiian shirt, you don't fight the wave, you ride the wave. And that's what everyone's got to realize so, they're going to use multi cloud, and remember the cloud was the web was the internet, it's actually all the same stuff from a long time ago, the mid 90's, which also means now procurement's involved and when procurement's involved, what are they going to say to you? Did you get a bid from IBM Cloud, did you see that bid from Amazon and Microsoft? So it's changed the whole thing of, I can just go to any cloud I want to, now procurement's involved that even mid-size companies procurement says you did get another bid right, did you not? Which for server, storage, and network vendors that's been the way it's been for 35, 40 years. >> The bids are changing too, so what are the requirements now? Amazon has a cloud, they have storage, you have storage, but people have on premise they have multiple environments. If the world is one big data center, with multiple regions and locations, this is the resilience you spoke of, what's the new requirements as procurement gets involved because procurement isn't dictating the requirements, they're getting the requirements from the application work loads and the infrastructure, so what are the new requirements that you see? >> So I think the thing you're seeing is if you take cloud just a couple years ago, I'm going to put my storage out there, okay great, I need this kind of availability, ooh that's extra money, sorry Mr. Wikibomb, Mr. CUBE we got to charge you a little extra for that. Oh we need a certain amount of performance, oh that's a little extra. And then for heavy transactional work loads the data's constantly moving back and forth, oh we forgot to tell you that we're charging you every time you move the data in and every time you move the data out. So as you're putting together these RFPs you needs to be aware of that. >> Those are hidden costs. >> Those are hidden costs that are, I think the reason you're seeing such the ride of the hybrid is people went to public cloud and then someone in finance, or maybe even in the IT group sat down with a spread sheet and said "Oh my god, we could've just bought an IBM array "or someone else's array" and actually had less money even counting support, because all every time we're moving the data, but for archive, for back up we don't move the data around a lot, it's a great solution for anything. Then you have the whole factoring of software as a service, so part of that is the software itself, if you're going to go up against salesforce.com then whoever does, they better make sure the software's good, then on top of that again you negotiate with the software vendor, I need it globally, okay what's the fee for that? So I think the IT guys need to understand that with the ubiquity of the cloud, you've got to ask way more questions, in the storage array business, everyone's got five nines and almost everybody's got six nines, well way back when it was four nines then it was five and now it's six, so you don't ask anymore because you know it just changes right? And the cloud is still new enough and the whole software as a service is a different angle, and a lot of people don't even realize software as a service is cloud, but when you say that they go, what are you talking about, it's just I'm getting it over a service. Where do you think it comes from? A cloud data center. >> Well the trend is software defined, you guys are on that early. Congratulations, and don't forget the hardware, the high performance hardware as well, arrays and what not. So great job. Eric thanks for coming on, appreciate it. >> Great, thank you very much. >> CUBE coverage here, I'm John Furrier, Stu Miniman. Day four of our live coverage here in Moscone North, in San Francisco for IBM Think 2019. Great packed house here at IBM Think, back for more coverage after this short break. (electronic outro music)

live from San Francisco it's the cube covering IBM thing 2019 brought to you by IBM good afternoon and welcome back to the cubes continuing coverage of IBM think 2019 I'm Lisa Martin and sake San Francisco with Dave Volante hey Dave hey Lisa we're staying dry though we are the most part exactly there are there looks like the Moscone notices maybe having a few little areas of improvement I think just running water through the pipes as we would say is a little trial that's true so we're welcoming back to the queue but guess that hasn't been with us for a while Paul is a couple of vice president of Big Data at cognitive systems at IBM Paul welcome back oh thank you and thanks for get my name right that was good so you are an accomplished author I talked to you on Twitter 19 books ever 350 articles I know you do a lot of speaking you've been IBM a long time this events massive great 30,000 people or so yesterday was standing room only in fact they shut the doors to Judy's keynote because there were so many people I'm curious some of the announcements that came out with cognitive yesterday what are some of what are some of the things that you saw yesterday that kind of piqued your interest well the Watson the Watson anywhere was I person have said that's a long time coming and they come on we got to have Watson on any cloud right not just the IBM cloud so that was I thought a big deal and then there were a bunch of announcements around enabling hybrid I think there were 20 plus services so you know it's not kind of vogue you know we're in this multi cloud world I need a way to get to hybrid so those are two standouts so your group's been busy basically that's right that's right I mean you hit it right Watson anywhere cloud everywhere so it's about AI in that drink I have to tell you that when I hear all the announcements there's tons of them right one of my favorite ones probably doesn't go as notice and it was Watson machine learning accelerator and that is really about looking at the journey for AI and clients over the next course of the years on that journey see most clients are just getting started there's some clients in the middle phase and there's some clients now that are hitting what I call the enterprise worthiness stage of AI right and so when we look at our announcements they're actually taking you from just getting started all the way to enterprise hardened explainable and algorithms and how to manage that because we're gonna go from this world where AI is sitting in the corner offices for the privileged few we have to democratize for the many but today it's like here's a little data science team they have their own server here's our programmer on their laptop you know and hanging out working there we want to bring this all together for enterprise so things like workload management which is what watching machine learning accelerated really does is how do I get everything together and working in a concurrent environment as organizations go from having 10 20 algorithms to trying to deploy thousands of them that's all they'll define themselves well you know when you get a bunch of data scientists in the room and you talk about citizens data scientists they kind of look at he like me there's no such thing but the fact is that if you can operationalize a you can open it up to a lot more people you know as a line of business person you'd much rather not have to go to a data scientist every time you want to do something with a because otherwise you're just kind of repeating the old decision-support world cells right what do you guys do when to operationalize yeah so it's a great question we're trying to taking the friction and so a lot of people will come and say oh gee p you acceleration so yeah it's about training stuff faster it's an open architecture and power and so you've seen the work with NVIDIA and that's unique to what Nvidia can do with with our cognitive systems is to accelerate the CPU GPU communications but there's a broader pipeline when you go to as the say I journey and we want to flatten that curve so one is how do I get up and running I don't know if you remember open source changes all the time so we're Enterprise hardening back testing getting you ready for here's the platform to deploy built on open source and where 80% of a data scientist time is spent right now is in what I call data preparation wrangling data labeling data gets stuff together now none of that is data science like none of that is data science at all and that's where the time and once I get the data ready I train the model ok so you've heard a lot about that and then the next thing I do is have to optimize the model so I think about where data scientist should be spending their time and that's on stage for we call that exploring the hyper parameter space another thing that Watson machine learning accelerator is all about how do we make the model perform now for data science geeks perform means how well is it classifying or how accurate is Hardware people often think performance means how fast you go right and then finally go to inference so we're looking at all five of those stages and one of them the biggest one is that 80% sink time we're trying to drop that to 20% and open it up for the rest of the enterprise so how do you democratize AI you mentioned that a lot of enterprises are really at the beginning of that journey yeah but when you're out talking with customers is there some sort of paralysis there where they're like Paul where do we start right right I think there's two areas where I see inertia or friction and so one is where do we start so let me say that start with the data you have you don't have to step up to the plate and hit a homerun you just get started and it's the things the little things you do every day not the big things you do once in a while and we always hear about disruption disruption you hear about uber and airbnb as the disruptors I actually believed they were the disruptors of yesterday I think right now we're in this list shift rift or cliff moment the disruptors of tomorrow will be those at the head of the analytics Renaissance that work with the data they have we know the outcomes we call that supervised learning and that's where you get started and the other piece is how do I get more people to participate talk about the lift shift rift or cliff intersection I saw that you've seen talked about that on social media can you break that down a little bit more and also talk to us about how you're helping customers actually kind of break through that or maybe it's avoid that altogether yeah well I mean you want to take two of those four and not take the other two right and I think that we do this lift if cliff moment in two ways one is as individuals so the people in the audience to people watching here all of us as practitioners we have got to get our skills moving forward I always say skill years are like dog years right like they age instantly and so you should be waking up every day like a newbie in this world and learning every single day and if you do that you'll have nothing to worry about as an individual and as organizations you had better put analytics at the forefront that means from the boardroom that means we encourage the culture of analytics everywhere and so those that's what I mean by lift chef rift or cliff moment so what comes back to sort of opening it up for average everyday line of business people you got a you got a demo yeah I'm gonna see what can I show to you all right so you know you were talking about the data scientist and citizen data scientist so I'm gonna propose to you this thing I call the wisdom of the crowd right today data scientists have to build things they're not domain experts imagine if I could invite the many to participate in this storyline and in this story line everyday line of business people could create an application based on an idea or a model and maybe we'd have thousands of them and out of those thousands we might vet I don't know 50 or hundred and out of that we would team up with data science deploy ten or twenty into production and then do the whole thing over again so let me show you how I could create this application here without building a single line of code and I actually use you Dave as an example because I wanted to see how much face time you get on the cube when John is up here with you doing this I get the short end of the stick the data tell the truth right so I had this intuition as a line of business user and I went to explore this so you can see here that we'll have two videos here and on the first video see where I put this here will say host screen time that's actually gonna measure the amount of time that you're on screen and I will be like that yeah and I actually built that in this modern way that democratizes for the many I'll just start it out here and on the bottom I built it the old-fashioned way so you can see we got John in there and they start out pretty good to start right there both recognizing both of them so let me go in pause these now the first thing you should notice is I've got a timer on the bottom I got a timer on the bottom cuz actually I had time to build that my dev ops team kind of put that in there for me so we'll continue this move it over here and let these things run now look at the accuracy of these models do you notice on the top you guys are both identified increasing this green counter and on the bottom I can't see you so in computer vision is very interesting if I wanted to teach a computer to tell me what the number eight was I could show it a picture of an eight but no more when I moved it sideways it would have no idea what it was I need to train it with lots and lots of data and so the bottom is the way the data scientists work so what did I have to do to do that I had to go collect some video had to reformat it had to put it down to a 480 and I had to write some code fire away and you see the code there now in order to get just to MVP so this model clearly doesn't score well Dave turns his head and it doesn't know who it is anymore all I said is your Dave Valente and if you're not then you're John so what do you do if you've got a third person in there all right and this is where we democratize it so this is our power I vision we've been talking a lot about this and I want to kind of invite everybody to take part in this kind of data science Renaissance all you do is you would go and upload some video here and you go capture some frames we could auto capture those frames every five seconds and let's say I wanted to add a new person like Arvin into this list here so I want to go develop and figure out how the algorithm can find out Arvin is now my last demo I showed you that was a linear classifier that wasn't easy here we'll go type in Arvind add Arvind and then I'm just gonna highlight it and box Arvind and now I've started to train the model there's no code at all you just train them all you just said this is Arvind when I see this so I'm leaving the model and then I'd have to go set it off to training and I'll look I'll do one other thing for you here I'll go and say well here's the think logo and maybe I want to track some logo detection that's it that's how I built the model now it's all about how much supervised label data you have so I asked I said who are the disruptors of the future and it's all about the compute power and the workload management power to train this stuff so economy systems is really all about both so we obviously know about the power in the workload management how do I go and actually generate the data so once I train this model I could click auto label it'll actually go through the rest of the video and go and find out from what it saw but here's where things get beautiful and everything I've showed you is someone writing lines of code now replaced with a clicker so I click on mint data we call these morphological operations I want you to notice something we have a hundred nineteen images labeled of Dave and John so as I click here I'm gonna apply these morphological operations Gaussian blurs sharpening blur that all means stuff to data scientists now I have four thousand two hundred and forty nine data points and I will generate that automatically that's all driven by line of business and finally we can come over here and go actually look at the model here's my model this model is actually scoring pretty really well but even if it wasn't scoring well and that's seventy percent this is now when I pass it to the data scientist team to do what their exceptional at the the hyper parameter tuning for the performance score the algorithm and so here I'll just finish this off by I think I had a picture of you I'll just drag it in here and now it's actually going out and scoring it we're scoring at 96% okay accuracy and I can expose this as a rest of API with the click of a button so I just have one thing the way I found out with the AI for you Dave at the end of it from what I can see John is getting about 50% more screen time than you and it's all good actually yeah oh you thought it was worse and if you notice your name here is Dave dapper Volante because we can't help but notice funny we can't even always tell well-dressed you a scientist you're well-dressed and it's pretty accurate but you're not getting the ROI on those outfits that you need for screen time that's what we found with it stuff with my business partner John but that's that's pretty good now you're saying you wrote the code right to identify either John or Dave and and at what point did you bring the data scientist in yeah so I didn't write any code on the top right on the bottom which the model did not perform well when he turns Ivy conceived that's the code we wrote now would take iterations iterations there was no code written there we built the model and then we brought a dev person in to try to build us a timer it was a couple lines of code took him about half an hour and in this case I didn't really bring the data scientist in yet because I'm scoring at 96% but I can easily pass it on into workflow and that's the story it's a pipeline workflow across so I'll pull the data scientists and I need to but 96% accuracy without a data scientist presence pretty good so a more complex use case you know you might not get 96 percent accuracy you might be at 50 percent forty percent more than 70 percent now you bring the data scientists in for the last mile absolutely let's say I was only scoring 50% and you don't think that's impressive I think it's pretty impressive that I did that in a half an hour and now this is engineer from the wisdom of the crowd I'm a line of business user and I'd like to know what kind of screen time you're getting maybe that's not a sporting event and I'd actually like a new business model where I charge Toyota by the second that they show up on the screen that's my idea data scientist never gonna think that I get it started and then they join the Renaissance that's how you democratize AI for the money yeah so maybe you could talk a little bit about how what was the compute power behind this the infrastructure behind this and then maybe we could talk about power and how you're applying that for AI infrastructure yeah that's a great great question so the bottom video actually trained on my laptop it ran for about a day and a half just so you know who's saying it is my laptop on the top of the video we actually leveraged our para AI architecture and ran that through with Watson machine learning accelerator and I gotta tell you the models train in about 30 minutes and in fact we had trained a model on your last show with your last guest in the amount of time it when you finish to when I came on stage 20 loads yeah so I mean that's the that's the accelerated compute and it's not and I hope what you're seeing here this isn't just a hardware component tree story this is a kind of coexistence in an almost synergy of software and hardware together and that's what's needed in the AI era well it's interesting I know when when you guys change the name of the you know power systems group to cognitive systems they had you know and I inferred of course we got a guy running it who used to run the software business so the different software component so it is clearly more than than software what are some of the sort of more interesting use cases that you guys are seeing with with clients specifically in terms of operationalizing this yeah for sure so in use cases of AI is I think it's we're in this world of precision so we're in precision agriculture precision risk or underwriting precision finance precision retail so the use cases are everywhere and it's really taking in all this kind of data in the operationalizing I think that we're helping people on all the levels you think about it I almost see three segments the first segment is we're not really sure what to do this AI and everyone says they're doing AI reminds me of the Hadoop days and the big data Lake and you know all that stuff turned out so how do we get you started so you can get down the path and build kind of MVPs and that's what I just showed you is the MVP the next group of people are the folks that have maybe one or two models deployed and now they're trying to say how do we scale out to hundreds and thousands of models what is the path now to make this bigger because we got it moving here and then the final phase with few people are at are those who are getting the challenge of I'm getting to a thousand algorithms deployed and now how do I get all this stuff running and so that entire path goes like this and our story line goes across that entire path how unique is this in the marketplace I'm interested in your commentary on IBM's competitive advantage is this so you guys have only guys who can do this and and how you know why are you winning in the market how how differentiable is this yeah so I think I'll answer that in two ways one is from the brand in which I participate in a larger company called IBM in terms of the acceleration there's nobody doing what we're doing and the reason is you took this kind of power processor and created the open power project and just like software evolved through open innovation that's what hardware is done so you look at Mellanox and Nvidia so I'll give you an example Dave the NV link exists on Intel and exists on power but they operate in two very different ways and nobody realizes that so envy link accelerates GPU to GPU communications does that an Intel does that on power but because of open power Envy link also allows the GPU to talk to the CPU so GPUs accelerate ai training because there's thousands of cores there right but they still got to talk to the CPU on top of that they don't have much memory so there's an example that's completely unique in the industry to make you train faster I think our workflow model is completely unique the tools that I showed you and around the workload management and then you look at the bigger part of IBM and how I can mix this with API calls to clouds clouds based Watson services or local but on top of that is now it's about how do you build the data that you can trust and how do you look at things like the explained ability of the model with their Watson open scale and that kind of stuff so it's a bigger story and nobody else has that end end story well and it's showing up in the in the in in the results we saw last quarter the your line of business was a bright star you know we're seeing some momentum obviously there's a lot of activity going on in Linux clearly you know cognitive is a big play there so congratulations that's it's exciting to see and of course maybe a lot of people don't realize it when you guys did the work to bring in little-endian compatibility and you know and tire you know software Suites now that it's you know it's not just this sort of niche proprietary platform anymore it's mainstream and so it's starting to show up in the business results so that's great to see yeah when I say democratize for the many I mean for the people for the enterprise and across the entire spectrum so well Paul thank you for confirming my suspicions here that John is my partner John Ferrier is sucking up all the camera time John I'm gonna have to elbow my way in a lot more so appreciate that having the data John's very data-driven so appreciate that yeah to have you on yeah as I see you again all right take deep right there everybody we'll be back with our next guest we're live from IBM think 2019 you're watching the cube

>> Live, from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to theCUBE's coverage of IBM Think 2019. Here in Moscone, we're talking so much multi clouds. It's been raining all day, really windy. To help us wrap up our third day, what we call theCUBE Insights, I have our co-CEO, Dave Vellante. I'm Stu Miniman and happy to welcome back to the program. It's been at least 15 times on the program, I think our counter is breaking as to how many you've been on, Ray Wang, who is the founder, chairman and analyst with Constellation Research, also the host of dDsrupTV who was gracious enough to have me on the podcast earlier this year, Ray. >> Little reciprocity there, Stu. >> Hey, we got to get you back on, this is awesome! Day three is wrap-up and this is going to be fun. >> Ray, as we say, theCUBE is everywhere, except it's really a subset of what you and the Constellation Research team do, we see you all over the place so thanks for taking time to join us. Alright, so tell us what's going on in your world, Ray. >> So what we're seeing here is actually really interesting, we've got a set of data-driven business models that are being lit up, and you see IBM everywhere in that network. And it's not about Cloud, it's not about AI, it's not about security, it's not about Blockchain. It's really about companies are actually building these digital networks, these business models, and they're lighting them up. IBM-Maersk, we saw things with insurance companies, you see it with food trust, you see it with healthcare. It's happening, and it's the top customers that are doing this. And so it's like we see a flicker of hope here at IBM that they're turning around, they're not just selling services, they're not just selling software, they're actually delivering these business models to executives and companies, and the early adopters are getting it. >> Ray that was one of the questions we had, is what's the theme of the show and-- >> There is no theme! >> You're giving us the theme here of what it should be because we talk digital, we talk cognitive, we talk all these other big thought-y words because we need to think while we're here, right? >> We need to think, we need to think! No, but the thing is this is a theme-less show, people can't figure it out but the main thing is, look, I've got a problem, this digital disruption is happening, my business models are changing. Help me be part of that shift, or I may go away! And people realize that and that's what they're starting to get, and you see that in all the reference customers the people that were on stage. The science slams were also really great. I don't know if you had a chance to catch those but the science slams were kind of a flicker into research, IBM research which is the heart of IBM, is coming up. They're going from concept to commercialization so much faster than they used to be, used to be research would do a project people are like, that's kind of cool, maybe I'll adopt it. They're now saying hey, let's get this into the market, let's get into academia, let's get early adopters on board. >> So Ray, what do you make of the Red Hat deal? What does it say about IBM's strategy? Do you like the deal? What does it say about the industry at large? >> It's a great question. The Red Hat deal to me was overpaid, however, at 20x multiples, that's what PE firms are paying. So every vendor is now competing with PE firms for assets. Red Hat, at about 9x, 10x? Makes a lot of sense, at 20x? It's kind of like, okay, is this the Hail Mary or is this the future strategy or is this basically what the new company is? I would have rather taken that money and put it into venture funds to continue what they're doing with these network models. That would have been a better strategy to me but Red Hat's a great company, you get a great team, you get great COs you get great tooling. >> So you would've rather seen tuck-ins to actually build that network effect that you've been alluding to. Of course that would have taken longer you know, wouldn't have solidified Ginni's legacy. So, it's a big move, a big move on the chessboard. >> Well the legacy's interesting, last year the stock was down some 20-some percent, it's up 20% since January so we're going to see what happens, but it's a doubt component. >> Well I've always said she inherited a bag of rocks from Palmisano at the peak of 2012 and then it got hit hard and she had to architect the transformation. It took, I don't know, five years plus, so, you know, she was dealt a tough hand, in my opinion. >> She had a bad hand, but we've had seven years to play this. I think that's what the market's saying. >> So it's on her, is what you're saying. >> It's now on her. She's got to turn this around, finish the legacy, but you've got a great CEO in waiting with the Red Hat guy. >> Jim Whitehurst you're saying? >> Yeah, he's good >> So she's what, Ginni is 60, 61? Is that about right? >> She's past the retirement age. Normally IBM CEOs would have gone through. >> 61 to 63 I think, is that range maybe, hey, women live longer so maybe they live longer as the CEO of IBM, I don't know. >> She did get a bad hand, but I think when you execute the strategy that money, here's the tough part. Investors are saying, hey, we'd rather take your money, back away from you through stock buybacks, dividends and mergers and acquisitions, and we don't trust you to do the innovation. That's happening to every company, including all of IBM's customers. The problem is if you do that, they're hedging against those companies too. The same investors are taking 50, 100 million, giving it to three kids in a start-up anywhere in the world and saying, hey, go disrupt these guys, so they're betting against their own investments and hedging. So that's the challenge she's up against. >> We talked about in our open for the show here. It's developers, though, that's the business model. We saw IBM struggle for years to get any real traction there, there's little pockets there, they've got great legacy in open source, but Red Hat's got developers. Ray, you go and see a lot of shows, who's doing well with developers out there? >> Microsoft redid their developer network by going younger with GitHub, whole bunch of other acquisitions, this is a great developer buy in that percent. But the other piece that we noticed here was it's the partner developers that are coming in in force. It's not your average developer. I'm going to build a coding and do a mobile app, it's people that work for large system integrators, large networks, small midsize VARs, those are where the developers are coming from and now they have a reason, right? Now they have a reason to build and I think that's been a good turnaround. >> How about Salesforce with the developer angle, what's your radar say there? >> It's not about the developer angle on the Salesforce side, what's interesting about the Salesforce side is Trailhead. This is, like, learning management meets gamification meets a whole LinkedIn training program in the back end. This is the way to actually take out LinkedIn without going after LinkedIn, by giving everyone a badge. There's a couple of million people actually on this thing. Think about this, all getting badges, all training each other, all doing customer support and experience, that's amazing! They crowd-source customer experience and learning right there. And they're building a community and they're building a movement. That's the thing, Salesforce is about a movement. >> Couple of others, SAP and Oracle, give us your update there. >> I think SAP's in the middle of trying to figure out what they have to do to make those investments. We see a lot of partnerships with Microsoft and IBM as they're doing the Cloud upgrades, that's an area. The acquisition of Qualtrics is another great example, 20x. 20x is the number people are now paying for for acquisitions and for assets on that end. And Oracle's going to be interesting to watch, post-Kurian to see how they come at it. They have a lot of the assets, they've got to put them together to get there, and then we've got all these interesting things like ServiceNow and Adobe on the other end. Like, ServiceNow is like, great platform! Awesome, people are building and extending the Cloud in ServiceNow, but no leadership! Right? I mean, you've got a consumer CEO trying to figure out enterprise, a consumer CMO trying to figure out enterprise, and they don't know if am I a platform or am I an app? You've got to figure that out now! People want to work with you! >> Well it is a company in transition at the top, for sure. >> But they can do nothing and still make a ton of money on the way out. >> And they've kicked butt since Donahoe came on, I mean just from a performance standpoint, amazing. >> Oh yeah, performance? You can do nothing and I think it's still going to coast but the thing is at some point it's going to come bite you, you got to figure that out. >> How do you think that Kurian will fit at Google, what's your take there? >> You know, early reactions on Kurian at Google is good, right? The developers are embracing him, he understands what the problems are. Let's be honest, I've said this many times to you guys in private and also in public, you know. It was a mess, it was a cluster before. I mean, you had three years, and you lost traction in the market, right? And it's because you didn't get enterprise, you couldn't figure out partners and, I mean, you paid sales people on consumption! Who does that? You're a sales rep, you're like, I'm not going to do this on consumption! Makes no sense! >> Ray, Kurian had been quoted that no acquisition is off the table, you know, they didn't buy GitHub, they didn't buy Red Hat, do you see them making a 10, 20 million dollar acquisition to get them into the enterprise space? >> Billion. >> Yeah, sorry, 20 billion. >> I think there's a lot that they go after. I know there's rumors about ServiceNow, there's a couple of other things. I think the first acquisition, if I were to make it would be Looker. I mean I love that thing that's on there and buy Snowflake too while you're at it. But we'll see what they do. I think the strategy is they've got to win back the trust of enterprises. People need to know, I'm buying your relationship, I have a relationship, I can count on you to be successful as opposed to, hey, you know, you can get this feature for less and if you do this on a sustained unit or, I want to know I can trust you and build that relationship and I think that's what they're going to focus on. >> Well, come on, isn't Google's business still ads? I mean, that's still where all their revenue is. >> It is, but the other category is $10 billion. That other category of devices and Cloud and all that? That's still a big category and that's where all the growth is. I mean look at this, it's a full frontal assault between Amazon and Google, Amazon Alexa versus Google Home, right? Amazon in ads, $10 billion in ads, going after Google's ad business. Amazon doing an AWS versus Google Cloud. Google's under assault right now! >> Give us the update on Constellation, your conference is really taking off, you've got great buzz in the industry, and congratulations on getting that off the ground. >> And the Tech for Good stuff, loved it. >> Thank you. We had great event, December 10th, talking about the future of the Internet. What it means in terms of, you know, digital rights, human rights in a digital age, was really that conference. Our big flagship conference is November 4th through 7th, it's at Half Moon Bay. We get about 250 CXOs together, about 100 vendors and tech folks that are visionaries and bring them together, that's doing well, and we do our healthcare summits. We brought on a new analyst, David Chou. David Chou, and if you've seen him before, he's like one of the top analysts for CIOs and chief data officers in the healthcare space, he's at HIMSS right now. >> He's awesome, we know him from Twitter. He's been on, he's great. >> Yeah, so we do healthcare summits twice a year and that's been picking up, some of the top thinkers in healthcare. We bring them in to Las Vegas, we do a brainstorming session, we work with them. They think about ideas and then we meet again, so. >> Alright, Ray, we want to give you the final word. We're halfway through IBM Think, what have you been thinking about this and any final musings on the industry? >> So I was very upset last year at how it was run. And I think this has run much better than last year. I think they did a good job. February in San Francisco? Never again, don't do that. I know it's May next year, is when this event's going to be. But I think the main thing is IBM's got to do more events than once a year. If you get enterprise marketing you realize it's at the beginning of the year, it's still sales kick-off and partners. March? March is like closing the quarter, so you do an event in April or May, and you do it in April or May but you have multiple events that are more targeted. This theme-less approach is not working. Right, partners are a little confused but they're here because it's once a year. But more importantly, build that pipeline over the quarters, don't just stop at a certain set of events, and I think they'll get very successful if they do that. >> Alright well, Ray, next time you come on the program, can you please bring a little bit of energy? We'll try to get you on early in the show when you're not so worn down. >> I know. >> Thanks as always. >> Appreciate you coming back on, man. >> Hey thanks, man, it's theCUBE! I love being on this thing.. >> Always a pleasure. >> Alright and, yeah, we always love helping you extract the signal from the noise. We're Dave Vellante, John Furrier, Lisa Martin. I'm Stu Miniman. Thanks for watching day three of theCUBE at IBM Think. Join us tomorrow, thanks for watching. (light music)

>> Live from San Francisco, it's TheCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to Moscone North at IBM Think 2019 I'm Stu Miniman, and my cohost for this segment is Dave Vellante. Happy to welcome two IBMers from the Z Group, we have Michael Jordan, distinguished engineer, everybody I'm sure in your family calls you the Michael Jordan? >> Nah, no, no >> Not the other one? >> I won't get into what they call me. >> Rohit Badlaney, who's a director of IBM Z as a service. So Rohit, we have to start there. We're very familiar with Z, you know, all the different pieces of it, but Z as a service, something new for this week, maybe help explain what the news is and-- >> Absolutely, so my mission in life is around Z and cloud. And this week you heard Jenny talk about Hyper Protect, and Hyper Protect is a family of services built in our IBM Cloud, on a cloud-ready systems, which are the ZR1 systems, in a multi-zone platform factor, so it provides the high availability disaster recovery. There are really four key services that we're announcing at this conference. One's around crypto and key management, provides the highest levels of security for our cloud. The second's around data as a service, which does traditionally really well on the platform, as a data-serving platform. The third's virtual servers, the fourth's containers that's going to be tied in to our Kubernetes Service. So we're bringing the breadth of our Z to our cloud. >> Yeah, you know, Michael, I show my age in the industry, I remember when we talked about security was, you know, lock the door on that rack that was in, or that mainframe that sat in the corner, we knew that that was secure. It's a little bit different when we talk about security and Z these days, it's cloud, it's global, >> Sure. >> It's all over the place. >> So-- >> But in fairness, right, I mean RACF was the gold standard of security, you know, before all this distributed systems stuff. You knew, you had full visibility on who did what, when, where, you know, very very detailed. Have you been able to carry that level of transparency and rigor into the cloud? >> Yeah, so some of this is what's old is new again, so one of the key areas that is a big focus for security in the cloud is encryption, right? You know encryption is going to a central part of being able to move data to the cloud, and the concepts of being able to bring your own key, is absolutely essential, and some of the capabilities that we've had on the Z platform for a very long time actually lend themselves extremely well to a cloud environment so for example, our cryptographic hardware can be virtualized, right? So each server can have 16 cryptographic cards, with 85 virtual domains per card, so you multiply that out it's, really serves cloud scale very well. And in addition to that, the cryptographic hardware is designed to meet the highest level of security certification standards, so a combination of security, and that virtualization really lends itself to offering a set of cloud services. >> If I think about the workloads that are running on Z, clearly there's no business case to move them off Z, into some commodity cloud, that would make no sense. You'd put your business at risk if you did that. But what's the business case of Hyper Protect, and Z as a service, could you talk about that a little bit? >> Yeah, so today our focus is primarily to elevate the security of our core and our cloud. If you look at what we are doing, it's around our Linux systems and not our traditional z/OS systems, and we're really focusing on where Z differentiates. It's around, you know Mike talked about key management, and key protection. It's around data protection, it's around scale. So the workloads, to your point, that do really well on the platform, are workloads that need that level of infrastructure characteristics. And it's not a well-known fact, but actually our Blockchain platform, and all the success IBM's had on Blockchain, has been running in our cloud, on our Z systems, over the last two years with 500 plus clients. Right, so those are the kind of workloads that benefit from the hardware characteristics, as well as the security characteristics. >> Just double-click on that, so you think Blockchain, often times you're thinking about distributed apps, you know, you think about transaction limits, et cetera et cetera, so what are the attributes of Z that lend itself well to those workloads? >> Oh that's a great question, so, several attributes, right? Definitely the key protection, and the data protection on Z, the sheer TPS, you know it's funny, I was actually with our BC doing a session today, and they were talking about the transaction per second they get by just running on Z versus commodity hardware. And they've had tremendous success, right? So those two, combined with you know, our Blockchain technology in our cloud runs on something called a Secure Services Container, which is an absolutely locked down container that no one can get access to. And those are the characteristics that, if you think about permissioned blockchain, that's where Z excels. So that's. >> One of the discussions we've been having is that, in a multi-cloud world I have different skillsets for the different environments. Can you give me a little compare/contrast how security fits in Z versus you know, x86, Linux, and public clouds? And also, how do I, as a customer, manage across those environments from a security standpoint? >> Sure, so a couple points on there. You know, one is, one of the benefits that we have with Z is we control a large portion of the stack, right? So we're able to integrate security into multiple layers of the stack. So Rohit mentioned the Secure Service Container, and that combines a number of capabilities that we've built in from the hardware, the firmware, the operating system, end to end. So for example, the Secure Service Container by default, all of the code and data associated with with one of these Secure Service Containers is encrypted. You don't have to do anything, it's, you deploy an application in of these containers, everything gets encrypted, in flight and at rest. And there's no configuration, no set up for that, it happens automatically. We validate, digitally sign and validate all of the firmware, the operating system, the application, and the entire package that gets loaded into one of these environments, to protect against introducing malware to that environment, and lastly is we block and restrict administrative access to prevent administrators from having uncontrolled access to the file system. So looking at that, right, since we own that stack and we can really integrate those security capabilities vertically through that stack to give the true value and the capabilities that you need in the cloud to protect both the application and the data. >> And that's always been the strength of the mainframe, is like you said, security's not a bolt-on, it's designed in from the very beginning. I mean when I started in the business, whatever IBM did with the 390, or whatever it was at the time-- >> You're dating yourself. >> Yeah, that's true. But the whole industry would focus on that. And then, frankly, IBM in the early '90s kind of lost it's way because it had that sort of install base, and it didn't really have to innovate. That's not the case today, you guys, well you have an install base who eats up, sort of every new cycle of Z. You've had to innovate, you've had to really invest in the roadmap, and stay current. Whether it's, you mentioned Blockchain, certainly Linux, et cetera. Now infusing AI as a service, so I wonder if you could talk a little bit about the sort of roadmap that you and your colleagues are on. Without obviously divulging futures, but there's a legacy there that you've invested in, and had to keep really current with some of the major industry trends to keep your clients happy. >> Yeah, and I'll weigh in and then Mike can jump in. I mean, the legacy of Z has always been scale, performance, hyper security, for the most regulated industries, for the most compliant industries, and our biggest enterprises. And that's going to continue, and the next generation of Z's going to continue down that theme. We are very focused on making Z part of the cloud. And so, there's a breadth of announcements, and I know we talked about Hyper Protect and the public cloud, but we're also expanding the Kubernetes orchestration on-premise with our IBM Cloud private product being supported fully on LinuxOne, and expanding it to Linux workloads, and z/OS workloads. And that is, you know, the cloudification of the platform is, I think, the next big step for us. >> But, so what's the real business driver for clients there? Is it just the notion of pay by the drink, and as a service? I mean obviously mainframe invented virtualization, and simplified management, and was always a key part of it, a key tenet. What's the real business driver for people to move to the cloud? >> I mean, in my view guys, it's the speed that they need to move at, right? I mean, you look at why we are standardizing on PaaS platforms, whether it's on the cloud or on-premise. The teams are constantly getting pushed to move faster, DevOps, now there's a new concept of DevSecOps, right? It's all about speed that's driving the need for the cloudification of the platform. The other reason is skills, right? Can I work with the mainframe in a way that I'm abstracting away the special skills needed, but I could still move with that speed in the DevOps cycle, right? So I think it's a combination of those both that's really driving this. >> And from a security perspective, I think a couple of the key points are looking ahead we're really focused on the data, right? How do we allow organizations, 'cause it's going to happen, right? Organizations will need to move data, whether it's temporarily, or longer term. They're going to need to move data to the cloud, that's just, it's a fact of life. So, how do we leverage and harness the capabilities that we have, that we've been talking about with the Z platform to enable clients to securely move their applications, pieces of applications, and data to the cloud so they can take advantage of the capabilities that Rohit was doing, with confidence that their data is not going to be compromised. And that includes a data-centric approach to protection of data, as well as protecting encryption keys and leveraging and taking advantage of the capabilities that we have on the platform for key protection, which is already a key part of the solution that we're bringing to market today. >> So the Z customer that bets his or her business on your platform, I mean, it's embedded, it's fundamental. What's the reaction been to Hyper Protect, you know, kind of feedback that you've had from clients? >> You know, everyone wants to be cloud today, right? So the reaction is actually been really positive. You know we've been working with our biggest Z clients, through what we call the Z Design Council, you know, validating the story. Because we want to help them on this enterprise-out journey. And the reaction has been good. Now, it's, it really depends on where they are on their cloud journey as well, right? Some are very much still want to be an on-premise shop, and some are aggressively moving to the public cloud. So our goal's really to intercept them wherever they are on that cloud journey. >> Yeah well many of them have a cloud mandate, right? >> Absolutely. >> Well, and I have clients come up to me on almost a continuous basis. When they look at what we, the capabilities that we've delivered with our z14 machine, and the cryptographic horsepower that we have with that machine, they're looking at it and saying hey, how do I harness this as a, you know, a crypto as a service for our enterprise? Which is kind of the precursor to what we're doing with the Hyper Protect services, but there is a keen interest from organizations to have a secure, performant, secure, stable environment for cryptographic services because, encryption is becoming ubiquitous, so providing that capability I think is significant. >> Yeah, and our goal, like Mike said, is really to make security easy, right? Whether it's in the public cloud and the enterprise developers don't have to worry about it. Can they get the levels of security that they need for their enterprises, or their enterprise workloads, but in an easy, cloud-native consumption model? That's really what Hyper Protect is. >> Yeah, I guess so final question is, what's the pricing implications of this new offering, and how do customers get started? Is this ready, shipping today? >> It's shipping in March. It's available today, that's the beauty of cloud, right? We went through what we call the experimental services, it's available in beta today. You could go to our IBM Cloud Catalog, access it, get it, try it. >> Great, give you a final word and takeaways you want people to have when it comes to security in the Z space. >> Yeah, so I think the main thing is that Z has a very proud tradition of security leadership and innovation, and what we're bringing to the market here is just another example of that security leadership and innovation. >> All right, well Michael and Rohit, thank you so much for bringing us the update-- >> Thanks, guys. >> Congratulations, on bringing the product to market. >> Thank you. >> Look forward to-- >> Good luck with it. >> Thank you. >> Thank you guys so much. >> All right, for Dave Vellante, I'm Stu Miniman, we'll be back to wrap up our day three of four days live, wall-to-wall coverage here, from Moscone North, IBM Think 2019, thanks for watching TheCube. (energetic techno music)

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Welcome back to Mosconi North here in San Francisco, California. I'm student of my co host, A Volante. We're in day three of four days live. Walter. Wall coverage here at IBM think happened. Welcome back to the program. Talk about one of our favorite topics. Cube alarm. Steve Hill, who's the global head of innovation. That topic I mentioned from KPMG, Steve, welcome back to the program. >> Seems to have made good to see you. >> All right. So, you know, we know that the the only constant in our industry is change. And, you know, it's one of those things. You know, I look at my career, it's like innovation. Is it a buzz word? You know? Has innovation stalled out of the industry? But you know, you're living it. You you're you're swimming in it. Talkinto a lot of people on it. KPMG has lots of tools, so give us the update from from last year. >> Well, I think you know, we talked about several things last year, but innovation was a key theme. And and when I would share with you, is that I think across all industries, innovation as a capability has become more mature and more accepted, still not widely adopted across all industries and all competitors and all kinds of companies. But the reality is, innovation used to be kind of one person's job off in the closet today. I think a lot of organizations or realizing you have to have corporate muscle that is as engaged as in changing the status quo as the production muscle is in maintaining the status quo has >> become a cultural. >> It's become part of culture, and so I think innovation really is part of the evolution of corporate governance as far as I'm >> concerned. What one thing I worry about a little bit is, you know, I see a company like IBM. They have a long history of research that throws off innovation over the years. You know, I grew up, you know, in the backyard of Bell Labs and think about the innovation a drove today, the culture you know, faster, faster, faster and sometimes innovation. He does sit back. I need to be able to think longer, You know? How does how does an innovation culture fit into the ever changing, fast paced you? No need to deliver ninety day shot clock of reality of today. >> Well, I think innovation has to be smart, meaning you have to be able to feed the engines of growth. So your horizon one, if you will, of investments and your attention and efforts have to pay off the short term. But you also can't be strategically stupid and build yourself into an alleyway or to our corner, because you're just too short term thought through. Right? So you need to have a portfolio of what we call Horizon three blended with Horizon one and Horizon two types investment. So your short term, your middle term and your longer term needs are being met. Of course, if you think about it like a portfolio of investments, you're going tohave. Probably a smaller number of investments that air further out, more experimental and a larger proportion of them going to be helping you grow. You could say, almost tactically or sort of adjacent to where you are today, incrementally. But some of those disruptive things that you work on an H three could actually change your industry. Maybe you think about today where we are. Azan Economy intangibles are starting to creep into this notion of value ways we've never seen before. Today, the top five companies in terms of net worth all fundamentally rely on intangibles for their worth. Five years ago, it was one or two, and I would argue that the notion of intangibles, particularly data we'll drive a lot of very transformative types of investments for organizations going forward. So you've got to be careful not to starve a lot of those longer term investments, >> right? And it's almost become bromide. Large companies can innovate, but those five companies just mentioned well alluded to Amazon. Google, etcetera Facebook of Apple, Microsoft there, innovators, right? So absolutely and large companies innovate. >> Yes, clearly, yeah, but you have to have muscle, but it doesn't happen by accident, and you do put discipline and process and rigor and tools and leadership around innovation. But it's a different kind of discipline than you need in the operation, so I'll make him a ratio that makes sense. Maybe ninety five percent production, five percent innovation in an organization. That innovation engine is always challenging that ninety five percent Are you good enough? Are you relevant enough? Are you fast enough? Are you agile enough? You need that in every corporate organization in terms of governance to stay healthy and relevant overtime. >> So it's interesting. You know, I was in a session that Jack Welch talk wants, and he's like, I hear big companies can innovate is like big companies made up of people. People are the things that can innovate absolute. But, you know, I've worked in large organizations. We understand that the fossilization process and the goto market that you have, you know, will often kill, you know, those new flowers that are blooming, what separates the people that can drive innovation on DH? You know, put those positive place and kind of the also rans that, you know get left behind window disruption. >> Well, there's several. There's a couple things that I would highlight of a longer list, one of them we culture. I mean, I think innovation has been part of a culture. People in the institution have value innovation and want to be part of it. And there is, you know, a role that everyone can play. Just because you're in operations, if you will, doesn't mean you ignore change or you ignore the opportunity to improve the status quo. But you still have you get paid to operate what I find that is related to culture that gets a lot of people, you know, slow down or or roadblock is the disconnect between the operating part of the business and the innovative part of the business. If you try, if you build them to separately, what happens is you have a disconnection. And if you innovate the best idea in the world over here. But you can't scale it with production, you lose. So you have to make sure that, as as a leader overall, the entire enterprise you build those connections, rotations, leadership, You know, How do you engage the production, you know, engine into the innovation engine? It's to be very collaborative. It should be seamless. You know, everyone likes to say that, but that word, but relative seamlessness is, is heavy architecture. You've gotto build that, you know, collaboration into your model of of how you innovate >> and >> don't innovate in the vacuum. >> And it comes back to the cultural aspects we're talking about. Do you mentioned the ninety day shot? Clocks were here in the Bay Area. Silicon Valley. The most innovative place in the world. They've lived along the ninety day shot clock forever, and it seems to have not heard that so called short term thinking. Why is that? >> Well, there's so much start up here. I mean, at the end of the day, there is so much churn of new thinking and start up in V C. And there's so much activity that it's almost a microcosm, right? Not every place in the world smells, feels, looks like Silicon Valley, right? And the reason for it is in part because there's just so much innovation in what happens here. And these things change me. If you think about, uh, these unicorns that we have today. Today there's about three hundred ninety one unicorns. Just five years ago, there were one hundred sixty globally on before that. Hardly people didn't know they were hardly recognized. But that's all coming from pockets of innovation like Silicon Valley. So I'd argue that what you have here is an interesting amalgamation of culture being part of a macro environment region that that really rewards innovation and demonstrates that in in market valuations in capital raises, I mean, today one hundred million dollars capital raise is pretty common, especially for unicorns. Five, ten years ago. You never see me. It was very difficult to get a hundred million dollars capital, right? >> You mean you're seeing billion dollar companies do half a billion dollars raises today? I mean, it's >> all day, right? And some of them don't make a profit. Which is I mean, and that's kind of the irony, Which is, Are those companies? What did they get that the rest of us, you know, there was that live on Wall Street right out of in New York. What do we not see? Is that some secret that downstream there will be some massive inflow? Hard to say. I mean, look at Amazon is an example. They've used an intangible to take industries out that they were never in before they started selling books, and they leverage customer behavior data to move into other spaces. And this is kind of the intangible dynamic. And the infection >> data was the fuel for the digital disruption to travel around the world. You see that folks outside of Silicon Valley are really sort of maybe creating new innovation recipes? >> Yes. I think that what you see here is starting to go viral right on DH way that KPMG likes to share a holistic way to look at this for our clients. What is what we call the twenty first century enterprise. So the things that we used to do in the twentieth century to be successful, hire people, build more machines, right? You know, buy more assets, hard, durable assets. Those things don't necessarily give you the recipe for success in the twenty first century. And if you look at that and you think about the intangibles work that's been well written about there's there's all kinds of press on this today. You'll start to realize that the recipe for success in this new century is different, and you can't look at it in a silo to say, Okay, so I've gotta change my department or I've got a I've got to go change, You know, my widgets. What you've got to think is that your entire enterprise and so are construct called the twenty first Century prize. Looks at four things. Actually, it's five, and the fifth one is the technologies to enable change in the other four. And those technologies we talk about here and I have made him think which are, you know, cloud data, smart computers or a blockchain, etcetera. But those four pillars our first customer. How do you think about your customer experience today? How do you rethink your customer experience tomorrow? I think the customer dynamic, whether it's generational or it's technologically driven, change is happening more rapidly today than ever. And looking at that front office and the customer dementia, it is really important. The second is looking at your acid base. The value of your assets are changing, and intangibles are big category of that change. But do your do your hard assets make the difference today and forward. Or all these intangibles. Companies that don't have a date a strategy today are at peril of falling victim to competitors who will use data to come through a flank. And Amazons done that with groceries, right? The third category is as a service capabilities. So if you're growing contracting going into new markets are opening new channels. How do you build that capability to serve that? Well, there's a phenomenon today that we know is, you know, I think, very practised, but usually in functions called as a service by capability on the drink instead of going out and doing big BPO deals. Think about a pea eye's. Think about other kinds of ways of get access to build and scale very fucks Pierre your capabilities and in the last category, which actually is extremely important for any change you make elsewhere is your workforce. Um, culture is part of that, right? And a lot of organizations air bringing on chief culture officers. We and KPMG did the same thing, but that workforce is changing. It's not just people you hire into your four walls today. You've got contingent workforce. You have gig economy, workforce a lot of organizations. They're leveraging platform business models to bring on employees to either help customers with help. Dex needs or build code for problems that they like to solve for free. So when you talk about productivity, which we talked about last year and you start thinking about what's separating the leaders from a practical standpoint from the laggers from practically standpoint, a lot of those attributes of changing customer value of assets as a service growth and workforce are driving growth and productivity for that subset of our community and many injured. >> So when you look at the firm level you're seeing some real productivity gains versus just paying attention to the macro >> Correct, any macro way think proactive is relatively flat, and that's not untrue. It's because the bottom portion the laggards aren't growing. In fact, productivity is in many ways falling off, but the ones that are the frontier of those top ten percent fifteen hundred global clients we've looked at, uh, you know, you see that CD study show that they're actually driving growth and productivity substantially, and the chasm is getting larger. >> So, Steve, Steve, it's curious what this means for competition. I think about if I'm using external workforces in open source communities, you know, Cloud and I, you know, changes in the environment. A supposed toe I used to kind of have my internal innovation. Now I'm out in these communities s O You know, we're here than IBM show. You know, I think back the word Coop petition. I first heard in context of talking about how IBM works with their ecosystem. So how did those dynamics change of competition and innovation in this? You know, the gig. Economy with open source and cloud. May I? Everywhere. >> Big implications. I mean, I I think you know, and this is the funny point you made is nontraditional competitors, because I think most of our clients and ourselves recognized that we haven't incredible amount of nontraditional competitors entering our space in professional services. We have companies that are not overtly going after our space, but are creating capabilities for our clients to do for themselves what we used to do for them. Data collection, for example, is one of those areas where clients used to spend money for consultants coming in to gather data into aggregate data with tools today that's ah, a very short process, and they do it themselves. So that's a disintermediation or on bundling of our business. But every business has these types of competitive non Trish competitive threats, and what we're seeing is that those same principles that we talked about earlier of the twenty first century surprise applies, right? How are they leveraging there the base and how they leveraging their workforce? Are they? Do they have a data strategy to think through? Okay, what happens if somebody else knows more about my customers than I do? Right? What does that do to make those kinds of questions need to be asked an innovation as a capability I think is a good partner and driving that nothing I would say, is that eco systems and you made you mention that word, and I want to pick up on that. I mean, I think eco systems air becoming a force in competitive protection and competitive potential going forward. If you think about a lot of you know, household names relative Teo data, you know Amazon's one of them. They are involved in the back office in the middle ofthis have so many organizations they're in integrated in those supply chains. Value change, I think services firms, and particularly to be thinking about how do they integrate into the supply chains of their customers so that they transcend the boars of, you know, their four walls, those eco systems and IBM was We consider KPMG considers IBM to be part of our ecosystem, right? Um, as well as other technology. >> So they're one of one of the things we're hearing from IBM. Jenny talked about it yesterday, and her keynote was doubling down on trust. Essentially one. Could you be implying that trust is a barrier to ay? Ay adoption is that. Is that true? Is that what your data show? >> We we we see that very much in spades. In fact, um, you know, I I if you think about it quite frankly, our oppa has driven a lot of people to class to class three. Amalgamation czar opportunities. But what's happening is we're seeing a slowdown because the price of some of these initials were big. But trust, culture and trust are big issues. In fact, we just released recently. Aye, Aye. And control framework, which includes methods and tools assessments to help our clients that were working with the city of Amsterdam today on a system for their citizens that helped them have accountability. Make sure there's no bias in their systems. As a I systems learn and importantly, explain ability. Imagine, you know. Ah, newlywed couple going into a bank to get a house note and having the banker sit back and have his Aye, aye, driven. You know, assessment for mortgage applicability. Come up moored. Recommend air saying no. You Ugh. I can't offer you a mortgage because my data shows you guys going to be divorced, right? We don't want to tell it to a newlywed couple, right? So explain ability about why it's doing what it's doing and put it in terms that relate to customer service. I mean, that's a pretty it's a silly example, but it's a true example of the day. There's a lot of there's a lack of explain ability in terms of how a eyes coming up with some of its conclusions. Lockbox, right? So a trusted A I is a big issue. >> All right, Steve, Framework that you just talked about the twenty first century enterprise. Is there a book or their papers? So I just go to the website, Or do I need to be a client? Read more about, >> you know, absolutely. You can go to our website, kpmg dot com and you can get all the della you want on the twenty first century enterprise. It talks to how we connect our customers front to middle toe back offices. How they think about those those pillars, the technologies we can help them with. Make change happen there, etcetera. So I appreciate it that >> we'll check it out that way. Don't be left in the twentieth century. Come on. >> No, you can't use twentieth century answers to solve twenty first century challenges, right? >> Well, Steve, he'll really appreciate giving us the twenty first century update for day. Volante on student will be back with our next guest here. IBM think twenty nineteen. Thanks for watching you.

>> Live from San Francisco, it's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to theCube. Lisa Martin with Dave Vellante on our third day here at IBM Think 2019. The second kind of full day of the event. Dave, here we are with this beautiful San Francisco rain. Much needed in California >> I like being back in Moscone, its good. >> It is nice being back in Moscone. Speaking of being back, we are welcoming back to theCUBE Mary O'Brien, the general manager of IBM security. Mary, it's a pleasure to have you on the program. >> Thank you Lisa, Dave. >> Mary. >> So we were just talking before we went live, this event is massive, about 30,000 people. It was standing room only to get into Ginni Rometty's keynote yesterday. >> No you couldn't get in. >> Couldn't get in, >> They closed, they shut the doors out >> I think she said this is the closest that she'll ever be to an iPhone launch. That must be like rockstar status. Four campuses, 2,000 different sessions, there is here a Security and Resiliency campus. >> Yes there is. >> Which must be exciting for you, >> It certainly is. >> but talk to us about security is such a pervasive challenge that any organization faces. You were saying, there's nearly two million by the year 2020 nearly two million unfilled security roles. Talk to us about security at IBM and how you're using technologies, like AI, to help combat the problem, this prolific problem that cyber security is bringing. >> Okay, so I can start by saying security is everybody's problem. It's a problem faced by every business, everyday and as businesses modernize and they become more digital and move to the Cloud, there's cyber security nightmares and cyber security problems are only getting greater, okay? So, you know couple that with the fact that, as you say, by 2020, and ever body has a different variation of this statistic, but we're working on the basis that by 2020, there will be in the region of two million, unfilled, cyber security posts around the world. So at IBM Security, we're looking to understand how we can reduce the complexity, reduce the need for vast numbers of staff and augment our capabilities, all of our products and services, with artificial intelligence in order to relieve this gross skills gap. >> Well, I have to say, this is our 10th year now doing theCUBE Lisa and I was downstairs earlier and I saw, I guess I call him my friend, Pat Gelsinger, was walking into the keynote and a little high five and nine years ago I asked Pat Gelsinger on theCUBE, is security a do-over because of Cloud and he said flat out yes, it actually is. So I wonder, so much has changed in the last decade. You mentioned data, you mentioned artificial intelligence, the bad guys have gotten way more sophisticated, you have this new thing called The Edge and so I don't know if it's a do-over or evolving rapidly, but what are your thoughts on the changing nature of security? >> Well I think the security landscape is changing for sure and the attack surface is changing because you've got to remember that as all of our and more and more devices and all of our devices become smarter and become connected to the internet, we're basically just increasing the attack surface and increasing the opportunity for cyber attacks and cyber criminals to hack in and get into our networks. Okay, so you know as we move to the Cloud and we embrace an API economy, so we're using API's to access you know our applications then you know once again, we're opening up our capabilities. Open means open to us and to others and so the need to design security into everything we do and not append security as a perimeter around what we create is becoming more and more important. >> Well we can't do that just 'cause I think something also that you mentioned, sorry David, with the proliferation of devices, you know billions of devices, the perimeter is so amorphis, there's en clays on top of en clays on top of en clays >> Absolutely. >> I'm curious though, how is AI from IBM going to help companies protect themselves from their people, who might not be doing things necessarily maliciously, unintentionally, but that's one of the biggest common denominators I think in security that's the biggest, how do we protect from people? >> You nailed it. I mean I can not remember the stat, but I do know that more than 50% of breeches result from the inside and that's not necessarily people being malicious. I mean you have a combination of people who just don't adopt the best security policies, so they're not using strong passwords, they're clicking on links, they're answering phone calls, they're doing something that's a little bit sloppy or a little bit insecure and then of course you'll have the malicious insider. There aren't very many of them, but they do exist. So the way the security industry is evolving to protect ourselves against the insider is firstly to look at access to our crowned jewels and to make sure that only the people who need access to our crowned jewels and to the most important assets within our businesses have that access. Okay, firstly, now secondly, we are developing capabilities that we call user based analytics, user behavioral analytics. So we actually profile, what is the normal behavior of a user. So a user, in their job role, who works the pattern that is normal for that user. You know, what is a normal behavior for that user so that we allow the machine and the algorithms to learn that normal behavior so that when that behavior becomes different or when that user does something anomalous, that we can trigger an action, we can trigger an alert, we can do something about it. So user behavior analytics is the way we apply machine learning, artificial intelligence, to the problem to keep us safe from the insider fumbling, yes. >> Another big change and I want to make a comment, is the way in which organizations approach security at the board level. It's become a board level topic. The conversation between whether its the CSO or the CIO and the board has evolved from really one of, oh yeah, we're doing everything we possibly can to we're going to get breached, it's all about our response to that breech and here's the response mechanism and so I wonder if based on your conversations Mary, with executives, what you're seeing, what are they asking from IBM, just in terms of helping them specifically respond to the inevitable breaches? >> Okay, so there's a wide range of responses to that question. And it depends where you are on the globe, how sensitized the board is to security situations. They're all sensitized, but there are some parts of the globe where a breach of a regulation can put a board member in prison. So you know, there's a motivation to >> They're paying attention >> They're paying attention okay, but you know across the board, we're seeing that the board has evolved their attention, based on the fact that security used to be driven by compliance. It used to be driven by ticking a box to say you had a database protection in place and you had x, y, z in place. People became more sensitized to the next attack so what was the next threat, what was the next attack on their, the next piece of malware, the next piece of ransomware, but now people have really got to the point and the board have really got to the point where they really realize that this isn't about when an adversary gets into your network or gets into your enterprise or your business. They get in. It's about how you respond to it, how you find them, how you remove them, how you respond to the breach so at IBM security, we put a huge focus on training boards and their teams in how to respond to an instance because we've got to get to a point where the response is muscle memory so that everybody knows their role, they know how they behave and we're back to the people discussion again because everybody, from the person who is at your reception desk, who may be the first person to meet the media as they come in your doors after an event, to the CSO who has responsibility to the President or CEO, needs to understand their role and when they parttake or when they back away and let the experts partake during the course of an incident. >> One of the things too that's been widely known is it's taken upwards of two to 300 days before breaches are detected. How is IBM helping infuse AI into, not just the portfolio, but also the practices and behaviors to start reducing that so it doesn't take as long to identify a breach that can cost millions of dollars? >> So yes, what were doing here is we're working to reduce the complexity in peoples cyber programs. So if you consider that in many of our clients shops, we will find up to 80 different security products from 40 different vendors and that's an average that has been taken over time and we use that statistic all the time. Basically you have all of these tools and all of these products that have been bought to solve a security threat djure over several decades and they're all residing, all of these products, not talking to one another. So at IBM Security, what we're doing is we're applying technology and our capabilities to bring together the insights from all of these tools and to ensure that we can actually knit them together, correlate those insights, to give a more holistic view, a faster view, of what's relevant, what's pertinent to you in your industry, in your geo, in your business. So we look for the insights that are indicative of the most significant threat to you to help you get there, sort it, eradicate it, quarantine, or whatever you need to do to eliminate it. >> How about the skills gap? We talk about that a lot on theCUBE. There's more security professionals needed than are out there. What can you do about that? Is machine intelligence a possible answer? Helping people automate a response? What do you see? >> Absolutely So there's a number of different responses. Absolutely, infusing artificial intelligence and finding ways of reducing the amount of the amount of security data, the amount of security alerts that need to be responded to. So firstly you need to reduce the noise so that you can find the needle in the haystack and our capabilities with machine learning and artificial intelligence and the various different algorithms we build into our products help along the way there. So you have that. In addition to that, you always have a need for the people, for the experts so making sure that we infuse all of our practices, the people who are foot soldiers on the street, our consultants, our practitioners, to make sure that we hire the best, the brightest and we put them around the geo so that they are distributed and able to help our clients. And then you heard Ginni yesterday talk about various different means of accelerating our ability to bring more people into the workforce using our P-TECH initiative within IBM, so we're looking to go out to schools, where you wouldn't necessarily have a feed or kids with an opportunity, to find jobs in the cyber security space or in many professional spaces. Finding them, training them, tapping them, encouraging them and we've seen several people come through the P-TECH schools into the cyber security space and we've also embraced the return to work for people who have taken career breaks either to mind elderly relatives or to bring up kids or whatever, so we have a number of programs running in various parts of the world where we're introducing people back into the workforce and training them to become cyber experts. >> I got to ask you, as a security executive, does Quantum keep you up at night? >> Um, Quantum does not keep me up at night because IBM are the leaders in this space and as leaders in this space, we work with the researchers and developers in the IBM research labs, to ensure that our security practices are keeping in lock-step with Quantum and our algorithms are changing so that we can stay ahead of the Quantum race. >> It's in the hands of the good guys right now. >> It certainly is >> Let's keep it that way if we can. >> Last question Mary, there is, as I mentioned in the very beginning, four campuses here where the 30,000 plus attendees can learn. What are some of the things that you're excited that the attendees here, customers, perspective customers, partners, analysts, press are going to see, touch and feel from the Security and Resiliency Campus? >> At the Security and Resiliency Campus, the people here can see some of our latest innovations and capabilities and they can see our new platform. Our new security platform is called IBM Security Connect and this is you know, our capability that we just launched to actually reduce the complexity in people's cyber programs and help bring lots of these products, these siloed products and the insights from them together, to give a much sharper view of the threat to your business. So there's a very good demonstration of that. You can see a very good demonstration of the breath of our portfolio. You can talk to some of our consultants. Talk to our instant response specialists, you know, you can be scared about what's out there and see that your security is in good hands if you work with us. >> It sounds like a security candy store down there. We should go check it out. >> Yeah >> It sure is. >> Check out the flavors. >> Exactly. Thanks so much for stopping by >> Thank you. >> Sharing with us what's new >> Great to see you again Mary. >> In IBM security and also how you guys are helping to influence behavior. I think that's a really important element. We thank you and we look forward to talking to you again. >> Thank you very much. >> We want to thank you for watching theCUBE. Lisa Martin with Dave Vellante, live IBM Think 2019 on theCUBE. Stick around, we'll be right back shortly with our next guest. (tech music)

>> Live from San Francisco it's theCUBE. Covering IBM Think 2019, brought to you by IBM. (electronic beat music) >> Welcome back to theCUBE. We are live at IBM Think 2019 in soggy San Francisco. I'm Lisa Martin, with Dave Vellante. Dave, I hope you brought a big umbrella today. >> Well luckily the Marriott lent me one, so-- >> I got one from my hotel, too. And what a perfect day to day have the hybrid, multi-cloud open upon us, shower San Francisco with rain, and talk about weather with an IBM expert. Mark Gildersleeve, welcome to the Cube. You are Vice President, Head of Business Solutions, and Watson Media, The Weather Company. >> Thank you for having me. >> Our pleasure, so, we think IBM, this is the second annual IBM Think. There's about what, 30,000 people here, 2,000 plus business and technical sessions. There is a lot, a broad spectrum, no pun intended, of topics to cover, but excited to talk with you today about what IBM is doing in the agriculture industry. Let's talk about it from the growers perspective first, and we'll cover some other, other outlets. But, what are some of the challenges that growers are facing in 2019? >> So, first of all, if you think about it, this is a really sporty industry for growers to be in. They've got to worry about things that they can't have any control over: the weather, pest and disease, government regulation, trade, commodity pricing, there's a lot that they can't control. To make matters worse, they have very slim margins, okay, and they had to learn all these various aspects of technology to try to become better. And so, they're almost drowning in data, trying to figure out what do I do about it to get more yield, to get more profitability, to get better quality? There's a lot of challenges that they're wrestling with today. (people chattering) >> Well this is a huge problem, because the, the amount of farmable land isn't growing. It's essentially flat. >> It's flat. >> Maybe it's even shrinking. >> It's flat. >> They're talking with a multi-decade, 20, 30-year time frame. Population growth, we're talking about another two, two and a half billion people over the next three decades. So, something's got to give. What does the data say? >> So you're exactly correct, the estimates of population growth are 2.3 billion between now and 2050. That's 30% population growth. With zero incremental air-able lands, so, huge yeah. So we have to get yields, at least 30% higher. Okay, so if you think about that problem we're not going to get that yield increase status quo. We're not going to get that yield increase without having a much more data and an AI driven approach to agriculture, and that's exactly what we're doing. Our solution right now has 14 different AI and analytic capabilities inserted into it. Just to try to help growers, for one, make sense of their data and make better decisions to try and get their yield up, their profit up, their quality up. >> And is there enough in your estimation markers, is there enough head room actually to accommodate that population growth, given the constraints? >> Absolutely, taking a simple example of being a corn grower in the U.S. The average corn grower gets 175 bushels per acre, but the 70th percentile gets like 250, okay? So, if we got in, in the example of corn, every person that's at the 50th percentile, up to the 70th percentile, which is extremely doable. You can, you are, by definition, increasing the yield 30% in that case. So, it's doable, and we can see examples of growers doing it today. But what you have to understand is that 70% of the differences in performance between growers are just their farming practices. So, we have to get a handle on what farming practices drive better yield. We have to get those people at 50% to 70%. The people at 30% up to 50. We just have to get them about 20 points better in the benchmarking, and we will actually solve this problem from a U.S. perspective, then we have to do different things for other parts of the world. >> Now there's a multi-variable problem here as well though, because you got consumer patterns changing, people want, you know, more sustainable. You go into the grocery store now, you see all grass-fed, or free-range, and, so that takes up more land. Do consumer, how do consumer preferences, and the shifting consumer preferences factor in? >> It's the biggest change I think that's happened in this industry in the last 20 years. If you look at 20 years ago, 30 years ago, the tech chains were being driven kind of more from the ag-input side, and that's kind of the people that are selling to the growers. Now, we have the food companies hearing from consumers that they want sustainable, they want better quality, they want more nutrition, they want to understand how to have less chemicals going into their food. Okay, now we have the buyers of the growers, pushing on those growers to say you need to give me a better product. This change of consumers, and this ripple through the food eco-system is the big change. And the food companies are at the center of this revolution. And it's actually really interesting, and I think it actually will knit together this whole ag-eco-system, so that you now have to worry about the ag input people, the growers, the food companies, and the retailers, the bankers and the insurers, all kind of understanding, and coming together to figure out how to get better product to the consumers, and also, by the way, increase the yield so they can solve the food production problem. >> So, where do you start? Are you talking, what's the lowest hanging fruit? Is it going to the large-scale growers that have more resources, potentially resources that understand technology enough to start at that source? What about the smaller scale farmer growers? >> So, I think that, we have IBM clients that are interested in solving every aspect of the kind of size of foreign problem. So, I met with one organization from Africa today. In Africa, it's all a small farmer problem, right? And, and the vast bulk of growers in the world are small farmers, okay? But when we're looking at kind of solving the problem overall, we want to start with the food companies, and the people in finance. Because, right now, food companies, when they're trying to deal with their growers, they're trying to manage these growers with spreadsheets. Even though these are very sophisticated companies, very sophisticated. We need to help those food companies better understand what's going on the field. What chemicals that are going onto the land? When was the crop planted? When is it going to be harvested? When can I expect it in my storage facility? And they really want to understand, what are the farmers doing that are giving them the best quality crop? And how can they learn from the data, to get best practices for all the rest of their growers? If we start with the food companies, and have them work with their growers and the agronomists, that's going to be the best way to introduce change into this sector, I believe. >> And they're kind of the the pivot point between the consumer, they understand the consumer demand, they can feed that back to the farmers. Of course, they're ultimate goal is to make a profit. But look at it, if you give the people what they want, there's going to be a way to make money here. It's just, it's not going to be the same way that they've made money for the past 50 years. >> Exact, exactly right. But you know, take an example, in my house, we buy organic milk, okay? We're paying a premium for organic milk. We're willing to pay a premium. >> Happy to do so, yup. >> Happy to do it. We feel like it tastes better. We feel good about also the quality of it. So, I think in many cases, food companies are willing to pay a premium to growers to deliver a very specific crop to them. And so, this issue of food companies having more growers under contract, and working with those growers to deliver a better product, is of high interest to virtually every food company, every beer company that we've talked to. Every retailer that's worrying about the supermarket shelves. They're all worried about trying to get better product to the shelf, 'cause that's what the consumers are asking for. There is money, in this system, if you get the quality up. So that's really what we're focusing on with the food companies. >> People happy to pay for that and this eco-system is actually quite interesting. You talk a bit about, you talked about the banks. They're, even health care is part of the eco-system. >> It's the other constituent. >> They've said that people start making better food choices. It could ripple through to health effects. So, maybe you're paying more, as a consumer, for an individual product, but you could be living longer, having better health, maybe having lower health care costs. >> One analogy that I think you might find interesting, is that, just as all of us have an electronic medical record, that has all the images that would have been taken of our body, like an MRI, or our health history, our hospitalizations, what surgeries we've had. We're now, as IBM, bringing the electronic field record, which is an exact analogy to the electronic medical record, but it's about the field. What's been grown there? What have been the yields? What are the chemicals? When was the crop planted? What kind of tillage practices are being used? And we're trying to, essentially build that database of the electronic field record as the cornerstone for all the analytics for the AI that we're building, and running against, to help figure out benchmarks for all the corn growers in U.S.A., or the potato growers in the Netherlands. And beyond the benchmarks, best practices, so that we can say, what are the people that are 70th percentile doing, that the people that are 30th percentile aren't doing? We can bring all those people up. It's very cool. >> So we're talking about IBM, the computer company, right? So, what's the big picture of IBM's role? Obviously, there's a data angle. But what's the IBM story here? The holistic story. >> So, first pillar is data. Every piece of data coming off of a combine or a sprayer, so the equipment data, the machine data. All the environmental data, remotely-sensed data, soil-sensed data, stuff that's going on to the field, as well as the farm practices. So, there's a whole data story that, who better than IBM to handle massive amounts of data? Secondly, AI and analytics, right? So, we've got 13 or 14 different analytics and AI products embedded in our decision platform. All intending to give that grower a better first guess, a better recommendation of, here's what the data tells us about your field. It's still up to the grower and the agronomist to make the final call, but we can give them a much better guess than they have just based on their own personal fields experience. Then lastly, it's decisions that we can help that grower make. So, an example would be: we can help a banker understand exactly what crop is being grown on a piece of land without having the banker have to send somebody out and look at it. So, they can understand compliance-wise, Was a loan that I wrote being used in the purpose that was intended? But there are many enterprise examples of that. So it's data, AI, decisions. And that's then connected across the eco-system. It's a great IBM story 'cause we've been in business, we've been serving the USDA for 91 years. We've been in agriculture a long time. Lots of people in IBM don't know it, but we've been at this a long time. >> And if we look at the growers for a second, this is really kind of where it all starts, right? I understand this triangulation, and the constituents that are involved from the food companies, to the retailers, to the bankers. But, if we look at the growers, what are some of the benefits? Do you have a favorite success story where, whether it's a large-scale grower or something smaller, where their, maybe their loan terms are better? Or they have lower costs? Or they're actually making a better impact on the environment? What's your favorite grower impact story? >> There are lots actually, but let's pick a few. The first is, we have a lot of aspects of crop protection, where we can use satellite imagery to figure out where a crop is under stress. Where, what part of the field is under stress. Help them go out and scout that field. Take a picture with their smart phone and have Watson tell you what the disease is that's infecting that crop. And, essentially, be able to take faster action. When you're faster with crop protection, you are saving a lot of your crop. You get better yield, that's money in the bank. So crop protection is one. A second example is, with best practices, showing some of these growers what the 70th percentile growers are doing, that the 50th percentile guys are not doing. You can say, here are the four things that these 70th percentile guys are doing. You should try those four things. Or you might want to try two of them this year, two of them next year. But best practice is a huge impact. The last impact is, we help people with yield. So, we can now say okay, this is the projected yield that you're going to have at the end of the season. Here's what you can sell at the middle of the season. Here's what you're going to be able to sell at the end of the season. And we help them with market timing. Trading profitability can be easily 20, 30 bucks of incremental profit per acre. So, there's kind of a financial angle, there's a best practices angle, and there's a protecting your field angle, as the three examples I give you. >> Well, and that's huge from the standpoint of the debt loads that farmers face around the world. Over a trillion dollars in debt, in just, you know, a few countries. What does the future hold from that standpoint? What are the implications of that debt load? Obviously there's an imperative to improve yields and improve profitability, but your thoughts? >> So, first of all, you're correct that debt is a really enormous issue. So, for example, there's an article in the Wall Street Journal last week. Bankruptcies are at the highest level in the U.S. since the crash of 2008. So, this debt load, and the debt service is a really large problem. Here's how I'd like to try to focus it. Many growers have been taught to worry about better yield. When we should have been focusing more on better profit per acre. There are two ways you can get out that profit per acre. One is, you can do things with new chance fertilization, seed type, plant date, that can drive your yield better. But the other aspect is, there are parts of your land that are going to be lower productivity potential. Your smartest move is to put less inputs on those portions of the land and double down on the inputs on the highest productivity areas of the land. Because most farmers don't understand that there's 25% of their land, where they're actually losing money, and they'd be better to actually not be planting. But instead the idea is, plant at a lower population rate, put less input costs in, and then you can even make that area of less productive land profitable. If we improve the profitability of these growers, they can afford the debt service, and that's kind of the way to do it. The other aspect is that, everybody that's doing contract growing for a given food company is getting a premium on their crop. Oftentimes, 10%, or even 15% premium. That 10%, or 15%, solves the problem of the debt service for almost every grower, in the U.S. that's doing zero crops. >> That focus on profitability versus pure yield per acre. That's potentially involves a a different crop? And a shifting strategy? >> Usually it's a different farming practice. So, it's applying variable rate technology. It's essentially understanding how to treat each aspect of your field differently so that you're not treating it homogeneously. But you're actually saying, I'm going to do this practice, and with this level of input costs down over here, in this section of the land. And do a different practice over here. Because, every piece of land has low productivity areas, high productivity areas, and areas that are either high or low, depending on the weather. Understanding how the land varies is a huge data insight that we give growers with our data insights using AI. >> And that can drop right to the bottom line, obviously. >> It's all bottom line, baby. >> Last question before we have to wrap, this is, I feel like we're scratching just the surface here, of such an interesting topic of, and the massive global implications of IBM and agriculture can have on all of us. Where can people go on the IBM website for example, to learn more about this? >> You can go to the, well, so at the Think, there are a number of sections actually that we have right now. Talks that we're giving later on Friday morning. All related to the Watson Decision Platform for Agriculture. And there's material at the Think exhibit stuff that you can go to. We're also exhibiting in the Watson Media and Weather section downstairs. We'd ask everybody to come there. >> Excellent, well Mark, thanks so much for joining Dave and me on the program today, really interesting conversation. >> Great story. >> Thank you for having me. >> Our pleasure. We want to thank you for watching the Cube, I'm Lisa Martin, with Dave Vellante. Live, from IBM Think 2019. Stick around, we'll be right back shortly with our next guest. (electronic music beat)