>> Announcer: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think brought to you by IBM. >> Hi, there, this is Dave Vellante. We're back at the IBM Think 2020 Digital Event Experience are socially responsible and distant. I'm here in the studios in Marlborough, our team in Palo Alto. We've been going wall to wall coverage of IBM Think, Kit Chee here is the Vice President, and general manager of Cloud and Enterprise sales at Intel. Kit, thanks for coming on. Good to see you. >> Thank you, Dave. Thank you for having me on. >> You're welcome, and Evaristus Mainsah, Mainsah is here. Mainsah, he is the general manager of the IBM Cloud Pack Ecosystem for the IBM Cloud. Evaristus, it's good to see you again. Thank you very much, I appreciate your time. >> Thank you, Dave. Thank you very much. Thanks for having me. >> You're welcome, so Kit, let me start with you. How are you guys doing? You know, there's this pandemic, never seen it before. How're things where you are? >> Yeah, so we were quite fortunate. Intel's had an epidemic leadership team. For about 15 years now, we have a team consisting of medical safety and operational professionals, and this same team has, who has navigated as across several other health issues like bad flu, Ebola, Zika and each one and one virus then navigating us at this point with this pandemic. Obviously, our top priority as it would be for IBM is protecting the health and well being of employees while keeping the business running for our customers. The company has taken the following measures to take care of it direct and indirect workforce, Dave and to ensure business continuity throughout the developing situation. They're from areas like work from home policies, keeping hourly workers home and reimbursing for daycare, elderly care, helping with WiFi policies. So that's been what we've been up to Intel's manufacturing and supply chain operations around the world world are working hard to meet demand and we are collaborating with supply pains of our customers and partners globally as well. And more recently, we have about $16 Million to support communities, from frontline health care workers and technology initiatives like online education, telemedicine and compute need to research. So that's what we've been up to date. Pretty much, you know, busy. >> You know, every society that come to you, I have to say my entire career have been in the technology business and you know, sometimes you hear negative toward the big tech but, but I got to say, just as Kit was saying, big tech has really stepped up in this crisis. IBM has been no different and, you know, tech for good and I was actually I'm really proud. How are you doing in New York City? >> Evaristus: No, thank you, Dave, for that, you know, we are, we're doing great and, and our focus has been absolutely the same, so obviously, because we provide services to clients. At a time like this, your clients need you even more, but we need to focus on our employees to make sure that their health and their safety and their well being is protected. And so we've taken this really seriously, and actually, we have two ways of doing this. One of them is just on to purpose as a, as a company, on our clients, but the other is trying to activate the ecosystem because problems of this magnitude require you to work across a broad ecosystem to, to bring forth in a solution that are long lasting, for example, we have a call for code, which where we go out and we ask developers to use their skills and open source technologies to help solve some technical problems. This year, the focus was per AVADA initiatives around computing resources, how you track the Coronavirus and other services that are provided free of charge to our clients. Let me give you a bit more color, so, so IBM recently formed the high performance computing consortium made up of the feYderal government industry and academic leaders focus on providing high performance computing to solve the COVID-19 problem. So we're currently we have 33 members, now we have 27 active products, deploying something like 400 teraflops as our petaflop 400 petaflops of compute to solve the problem. >> Well, it certainly is challenging times, but at the same time, you're both in the, in the sweet spot, which is Cloud. I've talked to a number of CIOs who have said, you know, this is really, we had a cloud strategy before but we're really accelerating our cloud strategy now and, and we see this as sort of a permanent effect. I mean, Kit, you guys, big, big on ecosystem, you, you want frankly, a level playing field, the more optionality that you can give to customers, you know, the better and Cloud is really been exploding and you guys are powering, you know, all the world's Clouds. >> We are, Dave and honestly, that's a huge responsibility that we undertake. Before the pandemic, we saw the market through the lens of four key mega trends and the experiences we are all having currently now deepens our belief in the importance of addressing these mega trends, but specifically, we see marketplace needs around key areas of cloudification of everything below point, the amount of online activities that have spiked just in the last 60 days. It's a testimony of that. Pervasive AI is the second big area that we have seen and we are now resolute on investments in that area, 5G network transformation and the edge build out. Applications run the business and we know enterprise IT faces challenges when deploying applications that require data movement between Clouds and Cloud native technologies like containers and Kubernetes will be key enablers in delivering end to end data analytics, AI, machine learning and other critical workloads and Cloud environments at the edge. Pairing Intel's data centric portfolio, including Intel's obtain SSPs with Red Hat, Openshift, and IBM Cloud Paks, enterprise can now break through storage bottlenecks and have unconstrained data availability in the hybrid and multicloud environments, so we're pretty happy with the progress we're making that together with IBM. >> Yeah, Evaristus, I mean, you guys are making some big bets. I've, you know, written and discussed in my breaking analysis, I think a lot of people misunderstand IBM Cloud, Ginni Rometty arm and a bow said, hey, you know, we're after only 20% of the workloads are in cloud, we're going after the really difficult to move workloads and the hybrid workloads, that's really the fourth foundation that Arvin you know, talks about, that you and IBM has built, you know, your mainframes, you have middleware services, and in hybrid Cloud is really that fourth sort of platform that you're building out, but you're making some bets in AI. You got other services in the Cloud like, like blockchain, you know, quantum, we've been having really interesting discussions around quantum, so I wonder if you can talk a little bit about sort of where you're allocating resources, some of the big bets that, that you're making for the next decade. >> Well, thank you very much, Dave, for that. I think what we're seeing with clients is that there's increasing focus on and, and really an acceptance, that the best way to take advantage of the Cloud is through a hybrid cloud strategy, infused with data, so it's not just the Cloud itself, but actually what you need to do to data in order to make sure that you can really, truly transform yourself digitally, to enable you to, to improve your operations, and in use your data to improve the way that you work and improve the way that you serve your clients. And what we see is and you see studies out there that say that if you adopt a hybrid cloud strategy, instead of 2.5 times more effective than a public cloud only strategy, and Why is that? Well, you get thi6ngs such as you know, the opportunity to move your application, the extent to which you move your applications to the Cloud. You get things such as you know, reduction in, in, in risk, you, you get a more flexible architecture, especially if you focus on open certification, reduction and certification reduction, some of the tools that you use, and so we see clients looking at that. The other thing that's really important, especially in this moment is business agility, and resilience. Our business agility says that if my customers used to come in, now, they can't come in anymore, because we need them to stay at home, we still need to figure out a way to serve them and we write our applications quickly enough in order to serve this new client, service client in a new way. And well, if your applications haven't been modernized, even if you've moved to the Cloud, you don't have the opportunity to do that and so many clients that have made that transformation, figure out they're much more agile, they can move more easily in this environment, and we're seeing the whole for clients saying yes, I do need to move to the Cloud, but I need somebody to help improve my business agility, so that I can transform, I can change with the needs of my clients, and with the demands of competition and this leads you then to, you know, what sort of platform do you need to enable you to do this, it's something that's open, so that you can write that application once you can run it anywhere, which is why I think the IBM position with our ecosystem and Red Hat with this open container Kubernetes environment that allows you to write application once and deploy it anywhere, is really important for clients in this environment, especially, and the Cloud Paks which is developed, which I, you know, General Manager of the Cloud Pak Ecosystem, the logic of the Cloud Paks is exactly that you'll want plans and want to modernize one, write the applications that are cloud native so that they can react more quickly to market conditions, they can react more quickly to what the clients need and they, but if they do so, they're not unlocked in a specific infrastructure that keeps them away from some of the technologies that may be available in other Clouds. So we have talked about it blockchain, we've got, you know, Watson AI, AI technologies, which is available on our Cloud. We've got the weather, company assets, those are key asset for, for many, many clients, because weather influences more than we realize, so, but if you are locked in a Cloud that didn't give you access to any of those, because you hadn't written on the same platform, you know, that's not something that you you want to support. So Red Hat's platform, which is our platform, which is open, allows you to write your application once and deploy it anyways, particularly our customers in this particular environment together with the data pieces that come on top of that, so that you can scale, scale, because, you know, you've got six people, but you need 600 of them. How do you scale them or they can use data and AI in it? >> Okay, this must be music to your ears, this whole notion of you know, multicloud because, you know, Intel's pervasive and so, because the more Clouds that are out there, the better for you, better for your customers, as I said before, the more optionality. Can you6 talk a little bit about the rela6tionship today between IBM and Intel because it's obviously evolved over the years, PC, servers, you know, other collaboration, nearly the Cloud is, you know, the latest 6and probably the most rel6evant, you know, part of your, your collaboration, but, but talk more about what that's like you guys are doing together that's, that'6s interesting and relevant. >> You know, IBM and Intel have had a very rich history of collaboration starting with the invention of the PC. So for those of us who may take a PC for granted, that was an invention over 40 years ago, between the two companies, all the way to optimizing leadership, IBM software like BB2 to run the best on Intel's data center products today, right? But what's more germane today is the Red Hat piece of the study and how that plays into a partnership with IBM going forward, Intel was one of Red Hat's earliest investors back in 1998, again, something that most people may not realize that we were in early investment with Red Hat. And we've been a longtime pioneer of open source. In fact, Levin Shenoy, Intel's Executive Vice President of Data Platforms Group was part of COBOL Commies pick up a Red Hat summit just last week, you should definitely go listen to that session, but in summary, together Intel and Red Hat have made commercial open source viable and enterprise and worldwide competing globally. Basically, now we've65 used by nearly every vertical and horizontal industr6y. We are bringing our customers choice, scalability and speed of innovation for key technologies today, such as security, Telco, NFV, and containers, or even at ease and most recently Red Hat Openshift. We're very excited to see IBM Cloud Packs, for example, standardized on top of Openshift as that builds the foundation for IBM chapter two, and allows for Intel's value to scale to the Cloud packs and ultimately IBM customers. Intel began partnering with IBM on what is now called Pax over two years ago and we 6are committed to that success and scaling that, try ecosystem, hardware partners, ISVs and our channel. >> Yeah, so theCUBE by the way, covered Red Hat summit last week, Steve Minima and I did a detailed analysis. It was awesome, like if we do say so ourselves, but awesome in the sense of, it allowed us to really sort of unpack what's going on at Red Hat and what's happening at IBM. Evaristus, so I want to come back to you on this Cloud Pack, you got, it's, it's the kind of brand that you guys have, you got Cloud Packs all over the place, you got Cloud Packs for applications, data, integration, automation, multicloud management, what do we need to know about Cloud pack? What are the relevant components there? >> Evaristus: I think the key components is so this is think of this as you know, software that is designed that is Cloud native is designed for specific core use cases and it's built on Red Hat Enterprise Linux with Red Hat Openshift container Kubernetes environment, and then on top of that, so you get a set of common services that look right across all of them and then on top of that, you've got specific both open source and IBM software that deals with specific plant situations. So if you're dealing with applications, for example, the open source and IBM software would be the run times that you need to write and, and to blow applications to have setups. If you're dealing with data, then you've got Cloud Pack to data. The foundation is still Red Hat Enterprise Linux sitting on top of with Red Hat Openshift container Kubernetes environment sitting on top of that providing you with a set of common services and then you'll get a combination of IBM zone open, so IBM software as well as open source will have third party software that sits on top of that, as well as all of our AI infrastructure that sits on top of that and machine learning, to enable you to do everything that you need to do, data to get insights updates, you've got automation to speed up and to enable us to do work more efficiently, more effectively, to make your smart workers better, to make management easier, to help management manage work and processes, and then you've got multicloud management that allows you to see from a single pane, all of your applications that you've deployed in the different Cloud, because the idea here, of course, is that not all sitting in the same Cloud. Some of it is on prem, some of it is in other Cloud, and you want to be able to see and deploy applications across all of those. And then you've got the Cloud Pack to security, which has a combination of third party offerings, as well as ISV offerings, as well as AI offerings. Again, the structure is the same, REL, Red Hat Openshift and then you've got the software that enables you to manage all aspects of security and to deal with incidents when, when they arise. So that gives you data applications and then there's integration, as every time you start writing an application, you need to integrate, you need to access data security from someplace, you need to bring two pipes together for them to communicate and we use a Cloud Pack for integration to allow us to do that. You can open up API's and expose those API so others writing application and gain access to those API's. And again, this idea of resilience, this idea of agility, so you can make changes and you can adapt data things about it. So that's what the Cloud Pack provides for you and Intel has been an absolutely fantastic partner for us. One of the things that we do with Intel, of course, is to, to work on the reference architectures to help our certification program for our hardware OEMs so that we can scale that process, get many more OEMs adopt and be ready for the Cloud Packs and then we work with them on some of the ISV partners and then right up front. >> Got it, let's talk about the edge. Kity, you mentioned 5G. I mean it's a really exciting time, (laughs) You got windmills, you got autonomous vehicles, you got factories, you got to ship, you know, shipping containers. I mean, everything's getting instrumented, data everywhere and so I'm interested in, let's start with Intel's point of view on the edge, how that's going to evolve, you know what it means to Cloud. >> You know, Dave, it's, its definitely the future and we're excited to partner with IBM here. In addition to enterprise edge, the communication service providers think of the Telcos and take advantage of running standardized open software at the Telco edge, enabling a range of new workloads via scalable services, something that, you know, didn't happen in the past, right? Earlier this year, Intel announced a new C on second generation, scalable, atom based processes targeting the 5G radio access network, so this is a new area for us, in terms of investments going to 5G ran by deploying these new technologies, with Cloud native platforms like Red Hat Openshift and IBM Cloud Packs, comm service providers can now make full use of their network investments and bring new services such as Artificial Intelligence, augmented reality, virtual reality and gaming to the market. We've only touched the surface as it comes to 5G and Telco but IBM Red Hat and Intel compute together that I would say, you know, this space is super, super interesting, as more developed with just getting started. >> Evaristus, what do you think this means for Cloud and how that will evolve? Is this sort of a new Cloud that will form at the edge? Obviously, a lot of data is going to stay at the edge, probably new architectures are going to emerge and again, to me, it's all about data, you can create more data, push more data back to the Cloud, so you can model it. Some of the data is going to have to be done in real time at the edge, but it just really extends the network to new horizons. >> Evaristus: It does exactly that, Dave and we think of it and which is why I thought it will impact the same, right? You wouldn't be surprised to see that the platform is based on open containers and that Kubernetes is container environment provided by Red Hat and so whether your data ends up living at the edge or your data lives in a private data center, or it lives in some public Cloud, and how it flows between all of them. We want to make it easy for our clients to be able to do that. So this is very exciting for us. We just announced IBM Edge Application Manager that allows you to basically deploy and manage applications at endpoints of all these devices. So we're not talking about 2030, we're talking about thousands or hundreds of thousands. And in fact, we're working with, we're getting divided Intel's device onboarding, which will enable us to use that because you can get that and you can onboard devices very, very easily at scale, which if you get that combined with IBM Edge Application Manager, then it helps you onboard the devices and it helps you divide both central devices. So we think this is really important. We see lots of work that moving on the edge devices, many of these devices and endpoints now have sufficient compute to be able to run them, but right now, if they are IoT devices, the data has been transferred to hundreds of miles away to some data center to be processed and enormous pass and then only 1% of that actually is useful, right? 99% of it gets thrown away. Some of that actually has data residency requirements, so you may not be able to move the data to process, so why wouldn't you just process the data where the data is created around your analytics where the data is spread, or you have situations that are disconnected as well. So you can't actually do that. You don't want to stop this still in the supermarket, because there's, you lost connectivity with your data center and so the importance of being able to work offline and IBM Edge Application Manager actually allows you so it's tournament so you can do all of this without using lots of people because it's a process that is all sort or automated, but you can work whether you're connected or you're disconnected, and then you get replication when you get really, really powerful for. >> All right, I think the developer model is going to be really interesting here. There's so many new use cases and applications. Of course, Intel's always had a very strong developer ecosystem. You know, IBM understands the importance of developers. Guys, we've got to wrap up, but I wonder if you could each, maybe start with Kit. Give us your sense as to where you want to see this, this partnership go, what can we expect over the next, you know, two to five years and beyond? >> I think it's just the area of, you know, 5G, and how that plays out in terms of edge build out that we just touched on. I think that's a really interesting space, what Evaristus has said is spot on, you know, the processing, and the analytics at the edge is still fairly nascent today and that's growing. So that's one area, building out the Cloud for the different enterprise applications is the other one and obviously, it's going to be a hybrid world. It's not just a public Cloud world on prem world. So the whole hybrid build out What I call hybrid to DoD zero, it's a policy and so the, the work that both of us need to do IBM and Intel will be critical to ensure that, you know, enterprise IT, it has solutions across the hybrid sector. >> Great. Evaristus, give us the last word, bring us home. >> Evaristus: And I would agree with that as well, Kit. I will say this work that you do around the Intel's market ready solutions, right, where we can bring our ecosystem together to do even more on Edge, some of these use cases, this work that we're doing around blockchain, which I think you know, again, another important piece of work and, and I think what we really need to do is to focus on helping clients because many of them are working through those early cases right now, identify use cases that work and without commitment to open standards, using exactly the same standard across like what you've got on your open retail initiative, which we're going to do, I think is going to be really important to help you out scale, but I wanted to just add one more thing, Dave, if you if you permit me. >> Yeah. >> Evaristus: In this COVID era, one of the things that we've been able to do for customers, which has been really helpful, is providing free technology for 90 days to enable them to work in an offline situation to work away from the office. One example, for example, is the just the ability to transfer files and bandwidth, new bandwidth is an issue because the parents and the kids are all working from home, we have a protocol, IBM Aspera, which will make available customers for 90 days at no cost. You don't need to give us your credit card, just log on and use it to improve the way that you work. So your bandwidth feels as if you are in the office. We have what's an assistant that is now helping clients in more than 18 countries that keep the same thing, basically providing COVID information. So those are all available. There's a slew of offerings that we have. We just want listeners to know that they can go on the IBM website and they can gain those offerings they can deploy and use them now. >> That's huge. I knew about the 90 day program, I didn't realize a sparrow was part of that and that's really important because you're like, Okay, how am I going to get this file there? And so thank you for, for sharing that and guys, great conversation. You know, hopefully next year, we could be face to face even if we still have to be socially distant, but it was really a pleasure having you on. Thanks so much. Stay safe, and good stuff. I appreciate it. >> Evaristus: Thank you very much, Dave. Thank you, Kit. Thank you. >> Thank you, thank you. >> All right, and thank you for watching everybody. This is Dave Volante for theCUBE, our wall to wall coverage of the IBM Think 2020 Digital Event Experience. We'll be right back right after this short break. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017, brought to you by VMware and its ecosystem partners. (upbeat techno music) >> Welcome back to theCUBE's continuing coverage of VMworld 2017. Nearing the end of day two. Lots of topics going on, my goodness. I'm Lisa Martin with my co-host, Dave Vellante. And we're excited to have Ed Walsh, General Manager of IBM Storage, back on theCUBE. Welcome back. >> Thank you very much, it's nice to be back. >> Yeah, so two really strong quarters of IBM Storage revenue growth. >> Ed: Shh, don't let that get out. (laughs) It'd make my job too easy. But thank you for noticing. >> You didn't bring your crystal ball for the third quarter? >> But I do appreciate it. We do like to quietly just do it. But thank you. >> All right. So, what are some of the big trends? What's coming down the pike for you at IBM? >> I think if you look at, one the reason we're doing so well is, I think, the innovation we're driving the market now which will take you into the future. But also, just how we're approaching clients is kind of resonating. And it does play into future trends. And we can talk about especially on the show floor. But I think clients are just challenged right now with all the complexity innovation. We could talk about it until the nth degree or bring in dev ops environment. But it's the complexity of IT, and all the change they're dealing with. In fact, if anything, because all the competition like the Uber my Business coming into my industry and disrupting me. But we find all of our customers are on the heels a little bit in technology. Instead they need to kind of lean in. And so the trend that we're seeing is people trying to simultaneously modernize their traditional application environment, which is how do you free up your people and time through automation and agility so that you can move those people and resource and start transforming the business on higher value type of thing? So we see that consistently. So you see a lot of API type of automation tools. That just frees up the current team to do other things. You'll see that in our portfolio. One of our big themes is to modernize the traditional application environment. It's what we do on true private cloud, allow you to have all the capabilities of public cloud in a hybrid cloud environment. So, bring everything you do in the public cloud on prem. It's the same automation capabilities, same dev ops tools, and use it on prem. And then go to the cloud when you need to in hybrid cloud. That's all about automation, API temp automation, it's all about freeing up your team. So, what kills the team as far as the automation around dev ops or test dev? Also, things just like backup protection. So, how do you backup your environment? It can be just a complete manual task that really doesn't add a lot of value. Or if you use a new technology and innovate, you can actually use it to drive newer innovations, and drive new use case for that secondary storage. So, we see those trends happening. That's where I would say our clients kind of responding to the innovation we're bringing to market. And that's where you see us growing above market. >> Dave: You know I want to pick up on the growth and talk about you're clearly gaining share. The numbers were high single digits, right? >> Ed: Yeah, 7% in key one, 8% with growing margins. So, expanding margins. That's dramatically over market. And the market's growing at low to mid-single digits? >> 1%. >> Dave: Yeah, okay. Basically, flat. >> Ed: Yep. >> So, that's significant gains. But one could say, okay, if IBM has been losing share and sort of hitting off the bottom and now it's gaining share, we'll see if you can sustain that. But I'm more interested in the attributes of a leader in the storage business. I'm just listing them here. Certainly, you've got to be relevant. I want to come back to that. You got to have a complete portfolio. You got to have strong product cycles. You got to have a great go-to-market, strong leadership, and maybe a little bit of luck. I don't know. I'm probably missing some things there. Not a bad list. >> Ed: Yeah. So, relevance. I want to go back, I said to Eric when he was on, that interview that you did with Peter Burris at our studio. And you were talking about digital transformation and data and storage being an active element. It seemed like a very relevant conversation for the C-suite. >> Ed: Sure. >> And as Eric was pointing out, C-level executives don't like to talk about storage cause it's just a cost. >> Ed: Right, right. >> So, you've got the relevance piece going for you cause you're IBM. >> Ed: Sure. >> Talk about some of those other ones. Complete portfolio, end of product cycles have been very important in the past. And IBM hasn't had that as an advantage but it seems like you brought that to IBM and others. So, talk about that a little bit, that cadence. >> So I've talked about coming here 12 months ago, it was to really bring innovation and drive growth for division. I had the hypothesis, and we talked about this, so, I think clients are challenged. They're looking for a partner to help them out. And I think where IBM's unique, which gets to your question, one, we have the right vision. How do you talk cloud and cognitive? How do you leverage your data? Whatever metaphor you use to get more out of your data, leverage it for decisions, that's what we do both in hybrid cloud and public clouds. But we also help people through these multiple eras, and IBM's very unique. Very few of our competitors actually say they can go through multiple eras. IBM's been through every era in compute, and we calmly go through it. And clients give us credit for that. You mentioned the broad portfolio. When I first started here, people said, "You're portfolio's board." And I kind of say, if you really want to be meaningful, and help people modernize, get from where they are to where they need to get to, you need a broad portfolio to do exactly that. And IBM has the broadest portfolio in the industry for storage. And then, last but not least, which is innovation, I actually think my secret weapon is, not because I'm the storage company, but if I could ever get the rest of IBM, all the innovation, all the capabilities from Watson or analytics and cloud into my portfolio, all of a sudden now I kind of distanced myself from other storage. In fact, I would say it's a big boy or big girl environment where you need to actually, it's not about the next array, which I'll provide, it's actually how do you actually help people get from here to there. And a single product company just can't do that. Although, it's easier to market, it's the complexity. I think it is the innovation. In each segment we're in, we're either number one or number two in all the segments. So, number two in overall storage, number one in software, number one in software-defined, number two in data protection, number one in analytics. Each one of those is highly competitive and you need to drive innovation. And what we do is, we leverage not only our development expense or developers, but we have probably the only company in storage left that has primary research. So, we have our classic IBM research doing fundamental what's going to be next, and that's what we're bringing to market. >> So, what have you learned, second time around at IBM, what have you learned about your ability to leverage those other pieces of IBM? Cause every general manager talks about all the great things at IBM, but few have been able to bring that in. I remember when IBM bought Storwize, I was so frustrated. It was like two years before you took this secret weapon. You know, put it in! Do it! Ship it! And it took too long. What have you learned about how to leverage those innovations? >> So, I think the power of IBM is you do have, I've said a couple times, I'm honored to be the one they chose to help drive this transformation of storage. But also, I'm kind of blown away by the team. So, in very short order, we relaunched an entire new portfolio. We refreshed the entire portfolio, hardware and software, late last year. That's where you're seeing the growth. We're also launching new product that are really hitting this innovation. We're also, as you said how do we leverage research, is think about what we're just doing on flash. So, everyone's talking about NVMe. Well, because we're already doing primary research we already have NVMe capabilities. So NVMe is a way to do an IO, and you're cutting through the IO path. And your latencies go down in order of magnitude. So everything's faster. >> Dave: Eliminating all that over head of the scuzzy stack of just the simplify the-- >> So, we already have that in all our storage products. And also we've just did it in the mainframe. So, we have the ability to do mainframe storage. It does 12 microseconds access time. Which if you think about it that's NVMe performance. But that's exactly what we're bringing from research into our product line. You'll see more, so we'll bring in Watson. So the one thing my predecessors, how do you bring Watson in everything you do? Cognition or AI should be in your product's hardware/software on prem, in the cloud as a service. How about how you do services support? You call, chatbot should be able to help you out. Do an analytics on the different data patterns. So, that's exactly what we're doing. And all that's really from either research or from IBM Greater. I'll give you one just a tactical thing. People are trying to back up to the cloud. It's just hard. How do you get all that data through a little straw. Well, I can either re-architect Spectrum Protect, which we did a lot of re-architect. We just announced a big part here today. But instead, I just leveraged a product called Aspera. IBM had this company they acquired called Aspera that does a lot of, basically, file transfer to the cloud. By doing an API integration in 30 days, my Spectrum Protect clients now do 10 times faster back up to the cloud. That's a good example of just leveraging the Greater IBM. And it was just literally asset sitting there. But how do I bring it to bear for the benefits of my clients? >> So, how did that happen? That was really, if I recall, a cloud acquisition to be more competitive with AWS. And same thing with the Cleversafe acquisition, really fit into that portfolio, round out the Bluemix Cloud. How did you go about leveraging that? Was it just knocking on a colleague's door or was it as simple as picking up a phone call? Or did you have to get people in a headlock and give them a noogie? >> So, I think it's more collaborative than that. I think the the past there were maybe sharp elbows. But I think this is more collaborative. The cloud and the AI team inside IBM is wildly collaborative with me because I bring capabilities that I can bring to them as far as what we can do around storage. So, I think that collaboration's working. It's probably me more helping them out initially to make sure I'm building that bridge, but then it's reciprocated. It's very easy. And the key thing is also being able to understand all that capability from research, and actually try to bring it into offering management team. So getting my offering management team to be more open. to be outside-in. Outside-in from the industry but also from outside-in from the rest of IBM in. And if I leverage these pieces, all of a sudden my portfolio and all of my development expense just gets multiplied. It's a force multiplier that I'm bringing in. And that's where the clients are really responding to it. >> Dave: That's great. >> Lisa: Eric Kurzog >> talked about that, the outside-in, as did Steve. So, it sounds like quite a cultural shift has happened. shift probably isn't a strong enough word, within IBM. You talk about, and everyone has talked about, this message of clients want simplicity. So, as a GM how are you simplifying this cross-function collaboration? What are like the top three things you'd recommend to other GM's to bring the simplicity that clients want internal to be able to get to market faster and iterate. >> So one, you have to look for what's in the industry. The other thing is really listen to clients. The clients will talk about simplicity but then it's the nuances, so really the details of what they mean by that. We use NPS, Net Promoter Score. But we actually get feedback on every service call or inside our own offerings. So you actually get the feedback but more importantly you actually get detailed feedback of what they want to change. So one, you can listen to that. You bring the outside in. That's directly from the clients. We use the term feedback's a gift. Sometimes it's not... But we respond within 24 hours to each and every one of those customers, and that gets you into a nice circle of feedback. The other thing is bring in the right team. So, on my team of about 50% of them has changed. So I brought in basically professional storage team from the inside and outside of IBM so that we can actually have our own outside-in inside. And then really, you need to align your organization to be what the clients need to see. For instance I did a reorganization as far as how I did offering management and go to market that they were aligned. So, instead of being product line driven, what people are purchasing. So people are doing distributed storage. That should be one offering management line, one owner instead of maybe three or four. Cause I have three different, four product lines. And that allows you to simplify what happens in the market. So if you can align your organization to what you actually need to leverage that's pretty easy. >> Lisa: Fantastic. >> Dave: So, I got to ask you. >> So, you're a unique executive. I call you a five tool player. >> Okay. >> You've technical chops. >> A lot of people don't know that about you. I do. You can go toe to toe, which probably scares the crap out of a lot of guys that work for you. You've got financial acumen. >> Ed: Sure. >> You've got a really strong network. You're a visionary and you can inspire people both with that vision but you can also push them. >> Ed: Oh, thanks. >> Hard. >> You know, I've seen that. And that's kind of your reputation, and people have a great deal of respect. So, you've got that sort of perspective. I want to get your perspective on what's happening in the world of VMware, generally in data protection specifically. >> Ed: Okay, sure. >> You've had a lot of experience in that area. You were the CEO of Avamar. You sold that company. Spectrum Protect is a big focus of your business today. What's your sense as to what's happening here? Why is data protection exploding so much? I've been asking this question all week. And I'm still not sure I understand why. Maybe this is a cloud effect. But what's your take on it? >> So, you mentioned simplicity. It fits into the modernizing, how do you free up your people from all these manual tasks? I would say backup is one of those crazy manual tasks that's more of an insurance policy. And then recovery was always a very big challenge. So, what you're seeing is new technologies come out that not only solve all the manual processes. So, what we did in Spectrum Protect is really dramatically simplify what you do, you set up an SLA and it literally self-monitors and keeps track and literally backs up to SLA, and recoveries are instantaneous. That used to be hours of work, every single day by someone. And recoveries would take days or hours days. Could be a long time. And now you're easily be able to come up and running. But also now you have the secondary storage which is a cost. What else can I do with that which has always been the dream. Now what you have is scale architecture's maybe all flash. And what you're doing on prem in the cloud, you have an image copy of everything in your environment for recovery purposes, availability. What else would you do with it? One, you want to make it easy, so the ease thing. SLA management for recovery. So you can do instant availability for if there is an outage. But all of a sudden what else would you do with it? Now what you're able to do, with orchestration, you're able to take that secondary copy, it's instantaneously mountable or bootable copies. All of a sudden you can take a snapshot of that. You can do data masking of that. Now you have this gold copy you can use for test dev or dev ops. It could also be the way that you gather on premises and you get a data copy into the cloud. And backup is a very good way to keep a history of data on a day to day basis or a couple times a day. So now you actually have an index of how your environment is changing over time, that you can use for analytics for other things. So, what's really happening is, it's going from a cost center that used to be a manual headache to everyone. And you're taking the exact same requirement. You have to do that anyway. And know you're making an asset and also you're freeing up your team for doing it. So I think you're going to see a huge investment. In fact, I would say probably the number one area people are investing. But it's past dedupe. So Avamar was the last, well dedupe was the last thing that really changed backup recovery because it was just you can't be moving all that data. Just change the amount of data you're moving so you can do it faster easier. You know, check. But now it's more about the agility. In a secret way, your backup's now the best way to feed test dev. Or the best way to feed dev ops. It's the best way to feed a cloud, if done correctly. So that's what we now suspect in Protect Plus. Literally, just do a backup and we can give you all these other use cases by leveraging the same investment. If you're trying to modernize, and free up your team, and get more for what you already have to do, it's probably the biggest low hanging fruit for clients. >> Yeah, so I mean I think that's the answer. Backup has been historically been crappy insurance that's not cloud-like. The industry's demanding, the customers are demanding a change. >> And then also, how do you do dev ops and test dev, and use production data in a data mass format. You don't want to do that in your primary. You want to take a copy. Backup does that. And you want to use that data. So, it actually solves another area of agility for the same dollars. >> Wow, fantastic. Thank you so much for joining us and sharing your insight. And we'll look for those next quarter results. And hope that trend >> We'll be back. keeps going-- >> We like them. >> Outstanding. Well, for Ed Walsh and my co-host Dave Vellante, I'm Lisa Martin. You've been watching the CUBE's continuing coverage of day two from VMworld 2017. Stick around, we'll be right back with the show wrap. (upbeat techno music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We're live here at the Mandalay Bay in Las Vegas for the wrap-up of IBM InterConnect 2017. I'm John Furrier. My co-host this week, my partner in crime, co-CEO, co-founder of SiliconANGLE Media Inc. with myself, Dave Vellante. Dave, it's been a great week. I just feel like I have been Watsonized and Blockchained and cloud all week. As we wrap up InterConnect, I want to get your thoughts on IBM, the cloud business, the big data marketplace, some of the things that we're seeing at the 100 of events we go to. We've got our events coming up, we're going to be in Munich next month, we got DockerCon, but a lot of developer events coming up, but in general, we get to see the landscape, in some cases, that others don't see. Let's talk about that, so before we get into the landscape, let's about IBM, IBM's prospects. This show, just quick stat, almost double the online traffic we're seeing on IBMGO than World of Watson, which was the biggest show we've ever done with theCUBE that we've seen. So, an interest, it's a data point. Unpack the data, you can see that there's a lot of global interest in what IBM is doing right now with the cloud and with Watson, and certainly with Blockchain you add another disruptive enabler potentially to what will either be a brilliant IBM strategy or a complete crash and burn. I think this is an IBM go big or go home moment with Ginni Rometty. I love her messaging, I love her three pillars, enterprise strong, data first, cognitive to the core. That is solid messaging, all three pillars. To me, it's clear. IBM is at a reinvention moment, it's all coming together, but it's a go big or go home moment for them. >> Well, you know, John, I mean, Ginni when she took over, sorry, she was running strategy before she became CEO, I mean, IBM had a choice, they could go double down on infrastructure and go knock it out with Dell and EMC and HP, or they could go up the value chain. And my ongoing joke is Dell bought EMC, IBM buys some other company, and that to me underscores the differentiation in thinking. Oracle, I think, is a little different, but Oracle and IBM are somewhat similar, I think you'd agree, in that they've got a big SaaS portfolio, they're trying to vertically integrate, they're trying to drive high value margin businesses. The difference is IBM's much more services oriented than, say, an Oracle, and that's still, as I say, a big challenge for IBM. But I'm more, I'm a bull on IBM. >> Why is that? >> I think the strategy is, number one, they're relevant. We talked for years about how we weren't that excited about Microsoft because they weren't relevant. Satya Nadella came in, all of a sudden, they're relevant again. I think IBM is highly relevant in the minds of CEOs, CIOs, CCOs, CDOs, all the C-suite, IBM is super relevant there, just as are Accenture and Ernie Young and all the big SIs. But IBM's got tons of products beneath it, number one. Number two, despite the fact that, you called it out several years ago, they bought software for 2.4 billion, it was a bare metal hosting company, alright, but IBM's turning that into >> Bluemix. >> a cloud business with Bluemix, right. And they're building, bringing in acquisitions like Cleversafe, like Aspera, like Ustream, and others, where they're bringing services that are differentiated. You can only get Watson on IBM's cloud, you can only get IBM's Blockchain on IBM's cloud, so they're bringing in value-added services, and there's only one place you can get them, and I think that's a viable strategy that's going to throw off a lot of cash, and it's going to lead to success. >> And by the way, they're also continuing to invest in open source. So, again, that's-- >> That's the other piece. I wanted to talk to you, and this is your wheelhouse. IBM's open source mojo is not just lip service, alright. They have deep-rooted DNA in open source and their strategy around it, and they've proven that they can monetize open source. What's their model, I mean, explain the model because I think it's instructive. >> I mean, open source, there's a lot of different models. Red Hat-- >> For IBM, I mean. >> IBM's model of open source is very clear. If you look at what they've done with just Blockchain as a great example, they have mobilized their company, and they did it with Bluemix as well with the cloud, once they said, "We want to get in the cloud game," once, "We want to do Blockchain," they go open source at the core, then they get their entire brain trust workin' on it. It's not just a hand wave, some division, they're kind of reorganizing on the fly, they're kind of agile organization, which some may read as chaotic, but to me, I think that's just good management practice in this day and age. They get an open source project, and they drive that home, and they have people contributing and giving that to the community, and then adding value on top and differentiating. It's just classic 101, create some value, and create some differentiation with your products, and by the way, if you don't want to use our products, build your own, or hey, use the open source code. That's pretty much an over-simplified version of open source. >> But Blockchain's a great example of this, right? So, they see the leverage in open source project, they put all these resources in, and they say, okay, now let's build our product on top of that, let's get the open source community leverage and this is, let me ask you this, does IBM, so several years ago when IBM announced Bluemix, you were pretty critical. >> John: I was very critical. >> IBM has to win the developer audience or it's cooked in this game. >> That's what I said. >> How is it done, how would you grade them? >> I think they're doing very well. I think IBM is, again, to use your word, they're not putting lip service in it. So, I was joking with Meg Swanson last night, I saw Adam Gunther when they interviewed on theCUBE, and I was critical. I didn't say that their cloud was bad, I was just saying it's just not as, just got a lot of work to do, Amazon's kickin' ass, which we now know that happened, right. But they've done well. They've done well, they've ran hard, they've gone the table stakes on the enterprise. I still think they got some more work to do, we can analyze, I'm putting out my cloud ratings matrix, I'm going to put IBM on that list, I have Google and Amazon done. I'm going to add Microsoft Azure and IBM onto the mix in the comparison matrix. But IBM has done good with the developers. They've just invested 10 million in this announcement, and they're ramping up. I wouldn't say they're throwing just money at it, they got people, so I would give them, I'd give them a B-plus, A-minus score because they're hustlin', they're doing it. Are they totally blowing it out of the water? No, I don't think they're pushing hard enough there. I think they could give it some more gas, I think they could do more with it, personally thinking. But you know, Dr. Angel Diaz was on earlier today. They're going at their own pace. >> But you agree they're in the game. >> Oh, totally. >> Making good progress. >> They're totally, IBM is totally in the cloud game, and they don't get a lot of credit for it. Either does Oracle, by the way. Somehow, people seem to talk about Azure and Google. Google is so far behind, in my opinion, they're not even close. I think it's Amazon, Azure, IBM and Oracle and Google all kind of in that-- >> Juxtapose Oracle's developer cred, even though it owns Java, with IBM's. How would you compare the two? >> Very similar, I think. Different approaches, but again, to your point, IBM's relevant, Oracle's relevant. We had this question about VMware when they did the deal with AWS. They have customers and they have cash, so they're not going anywhere. It's not like IBM's a sinking ship. It's not like Oracle's a sinking ship. Now, that being said, there's a huge shift in the business, and I would say in that scenario, Google is in a very good position, so I've been very critical on Google only because they're trying to be acting like they're an enterprise flag. They're not, I mean, Google's got great tech, TensorFlow, machine learning. Google has great cloud tech, but in that game, they're up in the number one, two spot. But in the enterprise side, they're not close. They're workin' on that. So, that's my critique of Google. Microsoft has got the DNA for the enterprise, so Microsoft and Oracle to me are more similar than comparing IBM and Oracle. I'd say IBM is a lot more like Google and Amazon, kind of in-between, but Oracle and Microsoft look the same to me. Big install base, highly differentiated, stacks aren't perfect, but it looks good on paper, and they're gettin' business. And Oracle's earnings, by the way, were very explosive due to the cloud growth. >> Another question I like to ask sometimes is, okay, what would you have done differently if you had a choice? Like when Gerstner was running IBM, he chose to consolidate the company, essentially, not consolidate, but focus on services, one throat to choke, single-faced IBM. Great customer service and build the services business, buy-in, PWC, et cetera, that was the key. What could you have done differently that could've said, well-- >> John: For IBM? >> Yeah, at the time, you could have said, "We're spin out different product groups. "We're going to be the best at microprocessors, "or disk drives, or database, or software." >> I think IBM moved too slow. >> That's a historical example. Given what IBM's doing today, what would you have done differently if you were Ginni Rometty five or six years ago? >> I would've done what they're doing now three years ago. We were, when we started working with them with CUBE, IOD events, and Pulse. >> Dave: Information on Demand. >> You had a lot of silence. I think, if I had to go back and get a mulligan, if I was Ginni Rometty, I would've moved faster. >> Dave: Done that faster. >> Hindsight's 20-20 on that, but it wasn't that clear. But again, it's the big aircraft carrier, it can only move so fast. I think what they're doing now is good strategy, and they're price strong, data force, cognitive to the core is a good strategy. Now, cognitive is words for AI, and that's their word, cognitive 'cause of Watson, but essentially, machine learning and AI is going to be a big pillar there, and then, the data first is more of an architectural component that's very good. But in general, Dave, the cloud is, this is what's going on in my find. It's so obvious to me. The big data marketplace that was we defined by Cloudera and Hadoop and Hortonworks just never panned out. It morphed into a bigger picture, and so, Hadoop is part of, now, a bigger ecosystem. Cloud was growing very fast. Those two worlds are coming together and growing very rapidly independent with big data, with machine learning, AI, and IOT. They're coming together. The intersection of the big data and the cloud. >> Cloud-mapping data. That was Yuri Burton from 2005. >> But it's coming together really fast, and the IOT is the real business driver. I know there's not a lot of stuff shipping yet in the sim stuff out there, but merging IOT into IT into business process and into developer mindset, whether it's an Indiegogo up to full-on developers is the accelerant that's going to fuel the AI value. To me, that's the intersection point of big data and cloud, and that is the home run, that's the holy grail, and that's going to be disrupting some preexisting decisions by big vendors who made bets, and I'm talkin' about bets made in the past five years, not like bets made 20 years ago or 10 years ago. I think the IOT is going to really shape the game. The other thing I worry about now, in my opinion, is a lot of AI-washing. People say, "Oh, AI." You see people on the stage, "Oh, we did this with AI." There's no AI, it's augmented intelligence, which is basically predictive analytics. You know, true AI is not yet here, it's a little bit hyped up, not that I mind that. I think that the machine learning is the real meat on the bone right now, I think that's the core enabler. Machine learning is by far the most important trend in the computer science world today as it relates to integrating that capability into cloud native, microservices, and overall application. >> I agree, I mean, AI is still a heavy lift, but to me, the key, I go back to something you were saying, is developers. That's the lever that's going to give you the ability to move large mountains. If you don't have that developer community, and you don't have open source chops, you're going to struggle a little bit. You're going to be either in a swim lane like Oracle with its database and its red stack, and maybe you can break out of that, but I'm not sure it wants to. Or you're going to be stuck in infrastructure lane. >> Yeah, but the developers are driving all the action right now. My point about machine learning, if you look at the shows just recently, and certainly we have the history of the past year, machine learning is the sexiest trend in every show. Last show was Google Next, machine learning with TensorFlow, both open source. Machine learning's not new, it's just now accelerating the developer. The developers want to move faster, and I think things like machine learning, things like cognitive that IBM puts out there, are great catalysts. That's going to be a big thing we're going to watch, obviously, we have a big developer community at SiliconANGLE, so something to watch. >> What's next? We've got a chief data scientist summit next week in Silicon Valley, we're going to be at the-- >> Let's look at my Friday show this week. Every Friday I do the Silicon Valley Friday show with me and guests, we got that goin' on, so always check that out on soundcloud.com/johnfurrier, or check out my Facebook feed, facebook.com/johnfurrier. But in terms of CUBE events, we've got DataWorks in Munich on April 2nd, DockerCon in Austin, Oracle Marketing Sum Experience, Red Hat, Dell EMC World, Service Now, Open Stack, Big Data in London. >> It's going to be a busy spring. >> Lot of stuff going on. Great stuff. >> Deb, we'll see you in July. >> In bumper sticker, Dave, this show, encapsulate your thoughts. >> Well, I think it's all about cloud, data, and cognitive coming together in a way that allows business value and differentiation through the end customer. That's what this show is about to me. It's not about infrastructure, cloud and infrastructure, that's kind of table stakes. It's all about differentiation up the stack, creating, enabling new business models. >> My encapsulation is the enterprise strong, data first, cognitive to the core message that Ginni said, that translates into IBM's shoring up their base products and putting an innovation strategy around Blockchain and soon to be cognitive computing at a whole 'nother level, and I think they're going to have a real innovation strategy and continue to use what they did with Watson, the winning formula. Put something out there that's a guiding principle and draft the company behind it. I think that, to me, is my big walk away, and I think Blockchain will potentially level, has game-changing capabilities, and if that plays out like Watson's playing out, then IBM could be in great shape on both shoring up the base in cloud and their business and having an innovation strategy that extends them out. That to me is the reason why I'm bullish on them. So, great show, Dave Vellante. Thanks to the guys, thanks for everyone watching. That's it for us here in theCUBE. I'm John Furrier, Dave Vellante wrapping up IBM InterConnect 2017. Thanks for watching, stay with us, and follow us at theCUBE on Twitter and siliconangle.tv on the web. Thanks for watching. (electronic keyboard music)

(electronic music) >> Announcer: Live from Las Vegas, it's theCUBE, covering Interconnect 2017. Brought to you by IBM. >> Welcome back to Interconnect 2017 everybody, this is theCUBE, the leader in live tech coverage. Russ Kennedy is here. He's the Vice President of Product Strategy and Customer Success at IBM. Russ, good to see you again. >> Good to see you, Dave. >> So Russ, of course, you and I have known each other for years. >> Yes. >> From the Cleversafe. You guys came in from the Cleversafe acquisition-- >> Right. >> A phenomenal move for you guys. Great exit, awesome move for IBM. >> Yep. >> So we're now well over a year in. >> Umm-hmm. >> So the integration, you've been long past Blue Washing (laughing) you're now in, and you're integrating with other services. >> Right. >> You're embedded in the cloud, still selling on prem-- >> Right. >> Hybrid messaging, so give us the update. What's happening at Interconnect? >> Sure, well, thanks for having me on. >> Dave: You're welcome! >> It's great to see you again. And you're absolutely right. Things have been moving very rapidly since the acquisition. It's about 15 months since we've been part of IBM now. And we still have a very robust on prem business that was our heritage in the Cleversafe days, but now that we're part of IBM we're well entrenched in the cloud. We've got cloud services, object storage services in the cloud, and a variety of different flavors there. We announced a couple of new things this week that I think are very exciting for clients. I'm sure we'll get into that as we go through this discussion. And we have a hybrid combination, so if clients want to have some of their data on prem, some of their data in the cloud, we offer that hybridity as well. And I think that's very exciting for enterprises that are looking to figure out where their workloads run best, and be able to have that flexibility to move things back and forth if they need to. >> We were talking off-camera, I remember I was saying to you, Cleversafe was one of Wicky-Bon's first clients-- >> Umm-hmm. >> Back when we were tiny-- >> Umm-hmm. >> And you guys were just getting started and-- >> Right. >> I remember we were working with you guys, and sort of talking about some positioning and things like that, and I remember saying, Look, it's going to cloud! >> Russ: Right, right, right. >> It's all going there. And at the time, it was like, you guys were saying, Yeah, we think so, too, but it's just not here yet (laughing). >> Right. >> (laughing) And we're a small startup you got-- >> Yeah. >> And so, you have the conviction of belief that it's going to happen, but at the same time you have to survive-- >> Sure, sure! >> And you got investors and it's... >> Yep. >> But the growth of unstructured data and then all of a sudden the combination of that, plus cloud happened. And then boom that was a huge tailwind. >> Right. >> Talk about that. >> Right, right, no, you're exactly right. In the early days it was very, very difficult to get people to understand the value of object storage and understand the value of cloud. And we were out there pioneering discussions around this concept, but we knew that the wave was going to happen. The growth of unstructured data was already obvious. You had music services, you had video services, everything going online. People wanting to distribute information and share information, and so you knew that the wave was coming. It took a little bit longer than I think everybody thought. I think certainly success in other public cloud services like Amazon and Microsoft kind of helped drove that as well. But we were certainly there with leading technology, and as soon as people started to realize the benefits of object storage for storing large, unstructured data objects, it just took off. >> Well, you know, too, the cloud progression was really interesting. >> Umm-hmm. >> You're right. Amazon sort of popularized it. >> Yep. >> And then the downturn in 2007, 2008, caused a lot of CFOs to say, Hey, let's try this cloud thing. >> Exactly. >> And then they came out of it-- >> Russ: Exactly, yep. >> And said, Hey, this cloud thing's actually really cool. >> Russ: Umm-hmm, umm-hmm. >> Now, let's operationalize it (laughing). >> Right. >> And go mainstream. And so, and now you've got this big discussion going on around data value, right. >> Russ: Of course. >> Everybody's talking about the value of data and what it means-- >> Russ: Sure, sure. >> And moving conversations up the stack away from sort of bit slicing and-- >> Right, right (laughing). >> Object stores-- >> Yeah, exactly. >> And ups the data value. >> You're exactly right. >> What are you seeing here? >> I think that's another new interesting area that we're getting into. It's the value of information, and I think what's driven that is the tools and the technologies that are now available to analyze data in variety of formats, right. The whole analysis and analytics capability that exist in the marketplace today is giving organizations a reason to take a look at their data, and to leverage their data, and to use their data, to drive business outcomes, to be more competitive, to be more agile, to be more flexible. So they're using the information. They have tools now that can give them insight into all kinds of things, their own data, external sources of data, new data that's being generated through applications and those kinds of things. All that can come together and analysis can go on top of it, to give people really quick insights into how to drive their business. And I think that's the really exciting part about being part of IBM's cloud because IBM has all those tools. >> We've been having conversations now for... It's well over several months and going into years-- >> Umm-hmm. >> Where the CIO's not so much thinking about storage, and certainly not worried about the media. >> Right. >> But definitely talking about what services can I tap to enhance the value of my data? >> Sure. >> How do I monetize, not necessarily data itself, but how does data contribute to the monetization of my company? >> Umm-hmm. >> And you guys fit into that. >> Sure. >> So maybe talk about that a little bit-- >> Sure, well, we talked to clients all the time about the value of the data, regardless of what industry you're in, financial services, healthcare, manufacturing, all of those types of organizations have information and it's information that can help them be more productive. It can help them be more agile. It can help them win in the marketplace. All they need to do is open it up and use it, leverage it, analyze it, look at it, look at it from a variety of different sources, and it can help them do a lot of things more efficiently, so we talked to clients all the time about the value of data. Storage is certainly something that makes that value realizable, and it's the interfaces between applications and tools that make the data usable. And we open that up to clients with our storage system very easily, whether it's on prem or it's in the cloud, and that's what they like. Now, we heard David Kenny on stage the other day-- >> Umm-hmm. >> He announced IBM Cloud Object Storage Flex-- >> Yes. >> And he said, We do have a marketing department, and yes, they did come up with that name. (laughing) A funny tongue-in-cheek moment. >> Yes, yes. >> But talk about Flex. What is it? And why is it relevant? >> So a lot of clients that we've engaged with recently have talked about... They love the cloud model. They certainly love the simplicity and the ease of growth and those kinds of things that cloud gives them. But they're a little confused about the pricing and they're worrying about whether they're paying too much for the workload that they have in the cloud. So we designed Flex as a way to look at storing data. First of all, it's a very low cost entry point for storing the data. And then it's designed for data where the workload may be unpredictable. It may be cold for some period of time, and then it may become very active for a period of time, and then go back to being cold again. What Flex does is it ensures that you don't overpay when you actually utilize that data, when it's very active, very hot, maybe you're running some sort of analytics against that data. Maybe it's some sort of cognitive recognition analytics process that you're running against the data. It makes it very usable, but yet, you're not paying too much to access that data. So Flex is designed for those kinds of uneven, varied workloads, or workloads where it's very cold for some period of time and very hot. Traditional tiers are designed for hot workloads, mid-level workloads, and very cold workloads. Flex actually covers the whole gamut, and it ensures that you're not paying too much for storing and using your data. >> So that's a problem that people have because-- >> Umm-hmm. >> They don't really understand how to optimize cost-- >> Right, right. >> If they don't understand their workloads. >> Right. >> They get the cloud bill at the end of the month. They go, Whoa-- >> Yep, exactly. >> What just happened? >> Exactly. >> It's complicated for people, there's a lot of times it's different APIs for different services. >> Russ: Sure, sure. >> So talk a little bit more about how customers... How you see customers deploying that and what it's going to mean to... >> Sure. >> What's the business impact? >> Yeah, no it's a great question. So Flex, first of all, you only have to remember four numbers. There's a number to store the data, a cost to store the data, a cost to retrieve the data, a cost for what we call Class A Operations, which are write operations and then Class B, which are read operations. Four numbers you have to remember. You know that you're not going to pay over a certain amount, regardless of how often you use the data, so it's very simple for people to understand. It's one set of numbers. It doesn't matter what the workload is. You know you're not going to be overcharged for that workload. >> You set a threshold. >> Exactly, you set a cap, you set a threshold. >> Yeah. >> And you're not going to pay over that amount, so it's very simple for them to utilize. Then, so they start to use it, and let's say that over a six-month period of time they start to understand their workload, and they know it's a very active workload. They can then change that data into maybe our standard tier, and actually even save more money because it's consistent, it's predictable when it's active, they'll actually lower their cost. And we're very open with clients about that because we want to take away that complexity of using the storage, and certainly the complexity of billing, like you talked about. And give clients a very easy transition into the cloud, and make sure that they can use it and leverage it the way they need to be more productive. >> So the key to that is transparency. >> Russ: Yes, absolutely. >> And control. And that's an elastic sort of dial-up, dial-down-- >> Absolutely. >> As you need it. >> Russ: Very, very much so. Yes, definitely. >> I wanted to ask you, so we've been obviously watching... IBM made the SoftLayer acquisition, it was like, Okay, we're going to buy this bare metal hosting company. >> Umm-hmm. >> And then they bring in Bluemix, and then they start bringing in applications. >> Yes, yes. >> And then all of a sudden it's like, IBM does what IBM does (laughing), and boom! Now, you've got this machine going. >> Yes. >> And so, several acquisitions that are relevant here, Aspera. >> Yes. >> Clearleap. >> Yes. >> UStream fits there because we know Ustream because we broadcast on UStream-- >> Russ: Yes, yes, uh-huh. >> And, of course, Cleversafe. >> Umm-hmm. >> Are you beginning to leverage those acquisitions and potentially others through Bluemix-- >> Yes. >> To create services and new value for clients? >> Yeah, so we're fully integrated with all those technologies, right, the object storage system through our APIs. Every single one of those technologies can leverage and utilize the storage system underneath. I'll give you an example, Aspera, as you mentioned, a very, prominent product in the marketplace. I think just about every company in media and entertainment and certainly any company that's dealing with unstructured data objects knows and uses Aspera. They have a service now in the cloud where you can actually move data very rapidly over their protocol, into the cloud, and then store it in the object storage system. That's easy, that's simple. That makes it easy to start to leverage cloud. UStream the same way, Clearleap the same way. All of this comes together in Bluemix. Bluemix is the glue, so to speak, so if you're developing new applications you have all of the Bluemix tools that you can use, and then you got all these technologies that are integrated, including the object storage system, which is the foundation, everything's going to... All the data's going to reside in an object storage system. That makes it all usable for clients, very simple, very easy. They have a whole portfolio of things that they can do. And it's all tied together through APIs. It's very, very nice-- >> And has that opened up when you're small startup... (laughing) You don't have all these resources-- >> Right. >> How has it opened up new opportunities for you guys? >> So we see a lot of new startups coming on board, and taking advantage of the storage system-- >> Right. >> And all the different services that sit on top. Many companies today are born on the cloud, or they're new applications that are being born on the cloud, and so, they have access to, not only infrastructure, like you said within Bluemix, they also have access to other services, video services, high-speed data transfer services, object storage services. So they're able to take advantage of all those different services, build applications very quickly. Another thing that's interesting about IBM, they have this concept, you may have heard of it, this Bluemix Garage concept-- >> Dave: Yeah, I have. >> Which is a rapid deployment, rapid application development, using design thinking and agile methodologies, to quickly develop a minimum viable product that now uses object storage as part of the services, right. So as a new client, you can come in, sit in the Bluemix Garage, work on the application, and have some really rapid prototyping going on, and leverage the storage system underneath. And that gets you started, gets you going. I can see a lot of new applications coming to market through that same-- >> So they're like seven garages, is that right around the world? >> Russ: Yes, yes. Yeah, they're around the world. And so, I didn't realize... So Cleversafe's a fundamental part of that, in the object storage. >> It is now. And we just announced it this week at Interconnect, but it is now. >> So what does that mean? So I go in and I can... It's basically a set of... Sets of best practices-- >> Correct, correct. >> And accelerance and-- >> Right. And obviously in the cloud world, you need a place to place your data, right. So the integration with Cloud Object Storage, Cleversafe now called Cloud Object Storage is now all part of that, so it's integrated into the app dev that's going on in those garages. And we're excited about that because I think we'll see a lot of new technologies coming through that methodology, and certainly ones that leverage our storage technology, for sure. >> What's it been like to go from relatively small Illinois-based startup. (laughing) And now you're in IBM. >> Right. >> What was the integration like (laughing)? Are you on the rocket ship now? You were kind of on it before, but now it's like, steep part of the S-curve-- >> Sure. >> With all these global resources. Describe that. >> Well, I think the biggest part that's happened to us as an organization is exposure to a number of different accounts that we as a small company may not have had access to, certainly in certain industries, IBM's in every part of the world, in every industry, and that exposure from IBM's go to market has been very, very exciting for us. And certainly, global now, right. As Cleversafe, we were only in North America and Europe, for example, and now we're all over the world, or had the chance to be all over the world, so that's been really exciting. And then on top of that the whole integration into the cloud, right, because IBM's cloud business unity is the one that drove the acquisition of Cleversafe because they wanted the technology in the cloud. And now that we're there, we can offer storage services, object storage services as a foundation to anyone all over the world. And I think that's really exciting, and it's the exposure to all kinds of different businesses that's been exciting since we've been part of-- >> Yeah, and the speed at which you can get to that object store as a service as opposed to-- >> Absolutely. >> As opposed to saying, Okay, knocking on-- >> Yes. >> All the cloud doors, (laughing) And, hey, do you want to buy my cloud? And like, Well, you know we got our own, or whatever it is. >> Right, right. >> And now it's just boom global-- >> It's shortened that sale cycle tremendously, right. People are up and running in a few days now, or even a few hours, whereas before it may take months or, even quarters, to get started. You can get started now just by going to the portal, signing up for object storage services, starting to write data into the cloud, starting to leverage these other services that we walked about. It's very simple-- >> And the commentorial effects of what we were talking about before with, like Aspera and UStream, and so fourth-- >> Russ: Umm-hmm, umm-hmm. >> Give you the ability to add even new services. IBM 's always been very good at-- >> Yes. >> Acquisitions. >> Yes. >> We forget that sometimes IBM... (laughing) >> Acquisitions are always hard-- >> Yeah. >> But we've been fortunate we've had a lot of support and a lot help in getting integrated into the various businesses, And I think it's been a good journey. >> So what should we look for? What kind of milestones? Can you show a little leg on futures (laughing)? What should we be paying attention to? >> Well, we're going to continue to do what clients are asking us to do. We're going to develop features and functions, both on prem and in the cloud. We're going to integrate with a lot of different technologies, both IBM technologies and other company technologies. You may have seen our announcements with NetApp and VERITAS this week. >> Yeah. >> So we're going to continue to expand our integration with other technologies that exist in the marketplace because that's what clients want. They want solutions. They want end-to-end solutions, both on on prem and in the cloud. So we're focused on that. We're going to continue to do that. We'll certainly integrate with other IBM services as they come to market in the cloud. That's a really exciting thing, so we're going to continue to focus on driving success for our clients. And that's exciting. >> Oh! Russ, belated congratulations on the acquisition, and going through the integration. I'm really happy for you guys, and excited for your future. Thanks for coming on theCUBE. >> Thank you. >> You're welcome. >> Thank you, Dave. >> Alright, keep right there everybody. We'll be back with our next guest. This is theCUBE, we're live from Interconnect 2017. Be right back! (electronic music)

>> Narrator: Live from New York, it's theCUBE. Covering the IBM Machine Learning Launch Event. Brought to you by IBM. Now, here are your hosts, Dave Vellante and Stu Miniman. >> Welcome back to New York City, everybody this is theCUBE, we're here at the IBM Machine Learning Launch Event, Rob Thomas is here, he's the general manager of the IBM analytics group. Rob, good to see you again. >> Dave, great to see you, thanks for being here. >> Yeah it's our pleasure. So two years ago, IBM announced the Z platform, and the big theme was bringing analytics and transactions together. You guys are sort of extending that today, bringing machine learning. So the news just hit three minutes ago. >> Rob: Yep. >> Take us through what you announced. >> This is a big day for us. The announcement is we are going to bring machine learning to private Clouds, and my observation is this, you look at the world today, over 90% of the data in the world cannot be googled. Why is that? It's because it's behind corporate firewalls. And as we've worked with clients over the last few years, sometimes they don't want to move their most sensitive data to the public Cloud yet, and so what we've done is we've taken the machine learning from IBM Watson, we've extracted that, and we're enabling that on private Clouds, and we're telling clients you can get the power of machine learning across any type of data, whether it's data in a warehouse, a database, unstructured content, email, you name it we're bringing machine learning everywhere. To your point, we were thinking about, so where do we start? And we said, well, what is the world's most valuable data? It's the data on the mainframe. It's the transactional data that runs the retailers of the world, the banks of the world, insurance companies, airlines of the world, and so we said we're going to start there because we can show clients how they can use machine learning to unlock value in their most valuable data. >> And which, you say private Cloud, of course, we're talking about the original private Cloud, >> Rob: Yeah. >> Which is the mainframe, right? >> Rob: Exactly. >> And I presume that you'll extend that to other platforms over time is that right? >> Yeah, I mean, we're going to think about every place that data is managed behind a firewall, we want to enable machine learning as an ingredient. And so this is the first step, and we're going to be delivering every quarter starting next quarter, bringing it to other platforms, other repositories, because once clients get a taste of the idea of automating analytics with machine learning, what we call continuous intelligence, it changes the way they do analytics. And, so, demand will be off the charts here. >> So it's essentially Watson ML extracted and placed on Z, is that right? And describe how people are going to be using this and who's going to be using it. >> Sure, so Watson on the Cloud today is IBM's Cloud platform for artificial intelligence, cognitive computing, augmented intelligence. A component of that is machine learning. So we're bringing that as IBM machine learning which will run today on the mainframe, and then in the future, other platforms. Now let's talk about what it does. What it is, it's a single-place unified model management, so you can manage all your models from one place. And we've got really interesting technology that we pulled out of IBM research, called CADS, which stands for the Cognitive Assistance for Data Scientist. And the idea behind CADS is, you don't have to know which algorithm to choose, we're going to choose the algorithm for you. You build your model, we'll decide based on all the algorithms available on open-source what you built for yourself, what IBM's provided, what's the best way to run it, and our focus here is, it's about productivity of data science and data scientists. No company has as many data scientists as they want, and so we've got to make the ones they do have vastly more productive, and so with technology like CADS, we're helping them do their job more efficiently and better. >> Yeah, CADS, we've talked about this in theCUBE before, it's like an algorithm to choose an algorithm, and makes the best fit. >> Rob: Yeah. >> Okay. And you guys addressed some of the collaboration issues at your Watson data platform announcement last October, so talk about the personas who are asking you to give me access to mainframe data, and give me, to tooling that actually resides on this private Cloud. >> It's definitely a data science persona, but we see, I'd say, an emerging market where it's more the business analyst type that is saying I'd really like to get at that data, but I haven't been able to do that easily in the past. So giving them a single pane of glass if you will, with some light data science experience, where they can manage their models, using CADS to actually make it more productive. And then we have something called a feedback loop that's built into it, which is you build a model running on Z, as you get new data in, these are the largest transactional systems in the world so there's data coming in every second. As you get new data in, that model is constantly updating. The model is learning from the data that's coming in, and it's becoming smarter. That's the whole idea behind machine learning in the first place. And that's what we've been able to enable here. Now, you and I have talked through the years, Dave, about IBM's investment in Spark. This is one of the first, I would say, world-class applications of Spark. We announced Spark on the mainframe last year, what we're bringing with IBM machine learning is leveraging Spark as an execution engine on the mainframe, and so I see this as Spark is finally coming into the mainstream, when you talk about Spark accessing the world's greatest transactional data. >> Rob, I wonder if you can help our audience kind of squint through a compare and contrast, public Cloud versus what you're offering today, 'cause one thing, public Cloud adding new services, machine learning seemed like one of those areas that we would add, like IBM had done with a machine learning platform. Streaming, absolutely you hear mobile streaming applications absolutely happened in the public Cloud. Is cost similar in private Cloud? Can I get all the services? How will IBM and your customer base keep up with that pace of innovation that we've seen from IBM and others in the public Cloud on PRIM? >> Yeah, so, look, my view is it's not an either or. Because when you look at this valuable data, clients want to do some of it in public Cloud, they want to keep a lot of it in the system that they built on PRIMA. So our job is, how do we actually bridge that gap? So I see machine learning like we've talked about becoming much more of a hybrid capability over time because the data they want to move to the Cloud, they should do that. The economics are great. The data, doing it on private Cloud, actually the economics are tremendous as well. And so we're delivering an elastic infrastructure on private Cloud as well that can scale the public Cloud. So to me it's not either or, it's about what everybody wants as Cloud features. They want the elasticity, they want a creatable interface, they want the economics of Cloud, and our job is to deliver that in both places. Whether it's on the public Cloud, which we're doing, or on the private Cloud. >> Yeah, one of the thought exercises I've gone through is if you follow the data, and follow the applications, it's going to show you where customers are going to do things. If you look at IOT, if you look at healthcare, there's lots of uses that it's going to be on PRIMA it's going to be on the edge, I got to interview Walmart a couple of years ago at the IBM Ed show, and they leveraged Z globally to use their sales, their enablement, and obviously they're not going to use AWS as their platform. What's the trends, what do you hear form their customers, how much of the data, are there reasons why it needs to stay at the edge? It's not just compliance and governance, but it's just because that's where the data is and I think you were saying there's just so much data on the Z series itself compared to in other environments. >> Yeah, and it's not just the mainframe, right? Let's be honest, there's just massive amounts of data that still sits behind corporate firewalls. And while I believe the end destination is a lot of that will be on public Cloud, what do you do now? Because you can't wait until that future arrives. And so the place, the biggest change I've seen in the market in the last year is clients are building private Clouds. It's not traditional on-premise deployments, it's, they're building an elastic infrastructure behind their firewall, you see it a lot in heavily-regulated industries, so financial services where they're dealing with things like GDPR, any type of retailer who's dealing with things like PCI compliance. Heavy-regulated industries are saying, we want to move there, but we got challenges to solve right now. And so, our mission is, we want to make data simple and accessible, wherever it is, on private Cloud or public Cloud, and help clients on that journey. >> Okay, so carrying through on that, so you're now unlocking access to mainframe data, great, if I have, say, a retail example, and I've got some data science, I'm building some models, I'm accessing the mainframe data, if I have data that's elsewhere in the Cloud, how specifically with regard to this announcement will a practitioner execute on that? >> Yeah, so, one is you could decide one place that you want to land your data and have it be resonant, so you could do that. We have scenarios where clients are using data science experience on the Cloud, but they're actually leaving the data behind the firewalls. So we don't require them to move the data, so our model is one of flexibility in terms of how they want to manage their data assets. Which I think is unique in terms of IBM's approach to that. Others in the market say, if you want to use our tools, you have to move your data to our Cloud, some of them even say as you click through the terms, now we own your data, now we own your insights, that's not our approach. Our view is it's your data, if you want to run the applications in the Cloud, leave the data where it is, that's fine. If you want to move both to the Cloud, that's fine. If you wanted to leave both on private Cloud, that's fine. We have capabilities like Big SQL where we can actually federate data across public and private Clouds, so we're trying to provide choice and flexibility when it comes to this. >> And, Rob, in the context of this announcement, that would be, that example you gave, would be done through APIs that allow me access to that Cloud data is that right? >> Yeah, exactly, yes. >> Dave: Okay. >> So last year we announced something called Data Connect, which is basically, think of it as a bus between private and public Cloud. You can leverage Data Connect to seamlessly and easily move data. It's very high-speed, it uses our Aspera technology under the covers, so you can do that. >> Dave: A recent acquisition. >> Rob, IBM's been very active in open source engagement, in trying to help the industry sort out some of the challenges out there. Where do you see the state of the machine learning frameworks Google of course has TensorFlow, we've seen Amazon pushing at MXNet, is IBM supporting all of them, there certain horses that you have strong feelings for? What are your customers telling you? >> I believe in openness and choice. So with IBM machine learning you can choose your language, you can use Scala, you can use Java, you can use Python, more to come. You can choose your framework. We're starting with Spark ML because that's where we have our competency and that's where we see a lot of client desire. But I'm open to clients using other frameworks over time as well, so we'll start to bring that in. I think the IT industry always wants to kind of put people into a box. This is the model you should use. That's not our approach. Our approach is, you can use the language, you can use the framework that you want, and through things like IBM machine learning, we give you the ability to tap this data that is your most valuable data. >> Yeah, the box today has just become this mosaic and you have to provide access to all the pieces of that mosaic. One of the things that practitioners tell us is they struggle sometimes, and I wonder if you could weigh in on this, to invest either in improving the model or capturing more data and they have limited budget, and they said, okay. And I've had people tell me, no, you're way better off getting more data in, I've had people say, no no, now with machine learning we can advance the models. What are you seeing there, what are you advising customers in that regard? >> So, computes become relatively cheap, which is good. Data acquisitions become relatively cheap. So my view is, go full speed ahead on both of those. The value comes from the right algorithms and the right models. That's where the value is. And so I encourage clients, even think about maybe you separate your teams. And you have one that's focused on data acquisition and how you do that, and another team that's focused on model development, algorithm development. Because otherwise, if you give somebody both jobs, they both get done halfway, typically. And the value is from the right models, the right algorithms, so that's where we stress the focus. >> And models to date have been okay, but there's a lot of room for improvement. Like the two examples I like to use are retargeting, ad retargeting, which, as we all know as consumers is not great. You buy something and then you get targeted for another week. And then fraud detection, which is actually, for the last ten years, quite good, but there's still a lot of false positives. Where do you see IBM machine learning taking that practical use case in terms of improving those models? >> Yeah, so why are there false positives? The issue typically comes down to the quality of data, and the amount of data that you have that's why. Let me give an example. So one of the clients that's going to be talking at our event this afternoon is Argus who's focused on the healthcare space. >> Dave: Yeah, we're going to have him on here as well. >> Excellent, so Argus is basically, they collect data across payers, they're focused on healthcare, payers, providers, pharmacy benefit managers, and their whole mission is how do we cost-effectively serve different scenarios or different diseases, in this case diabetes, and how do we make sure we're getting the right care at the right time? So they've got all that data on the mainframe, they're constantly getting new data in, it could be about blood sugar levels, it could be about glucose, it could be about changes in blood pressure. Their models will get smarter over time because they built them with IBM machine learning so that what's cost-effective today may not be the most effective or cost-effective solution tomorrow. But we're giving them that continuous intelligence as data comes in to do that. That is the value of machine learning. I think sometimes people miss that point, they think it's just about making the data scientists' job easier, that productivity is part of it, but it's really about the voracity of the data and that you're constantly updating your models. >> And the patient outcome there, I read through some of the notes earlier, is if I can essentially opt in to allow the system to adjudicate the medication or the claim, and if I do so, I can get that instantaneously or in near real-time as opposed to have to wait weeks and phone calls and haggling. Is that right, did I get that right? >> That's right, and look, there's two dimensions. It's the cost of treatment, so you want to optimize that, and then it's the effectiveness. And which one's more important? Well, they're both actually critically important. And so what we're doing with Argus is building, helping them build models where they deploy this so that they're optimizing both of those. >> Right, and in the case, again, back to the personas, that would be, and you guys stressed this at your announcement last October, it's the data scientist, it's the data engineer, it's the, I guess even the application developer, right? Involved in that type of collaboration. >> My hope would be over time, when I talked about we view machine learning as an ingredient across everywhere that data is, is you want to embed machine learning into any applications that are built. And at that point you no longer need a data scientist per se, for that case, you can just have the app developer that's incorporating that. Whereas another tough challenge like the one we discussed, that's where you need data scientists. So think about, you need to divide and conquer the machine learning problem, where the data scientist can play, the business analyst can play, the app developers can play, the data engineers can play, and that's what we're enabling. >> And how does streaming fit in? We talked earlier about this sort of batch, interactive, and now you have this continuous sort of work load. How does streaming fit? >> So we use streaming in a few ways. One is very high-speed data ingest, it's a good way to get data into the Cloud. We also can do analytics on the fly. So a lot of our use case around streaming where we actually build analytical models into the streaming engine so that you're doing analytics on the fly. So I view that as, it's a different side of the same coin. It's kind of based on your use case, how fast you're ingesting data if you're, you know, sub-millisecond response times, you constantly have data coming in, you need something like a streaming engine to do that. >> And it's actually consolidating that data pipeline, is what you described which is big in terms of simplifying the complexity, this mosaic of a dupe, for example and that's a big value proposition of Spark. Alright, we'll give you the last word, you've got an audience outside waiting, big announcement today; final thoughts. >> You know, we talked about machine learning for a long time. I'll give you an analogy. So 1896, Charles Brady King is the first person to drive an automobile down the street in Detroit. It was 20 years later before Henry Ford actually turned it from a novelty into mass appeal. So it was like a 20-year incubation period where you could actually automate it, you could make it more cost-effective, you could make it simpler and easy. I feel like we're kind of in the same thing here where, the data era in my mind began around the turn of the century. Companies came onto the internet, started to collect a lot more data. It's taken us a while to get to the point where we could actually make this really easy and to do it at scale. And people have been wanting to do machine learning for years. It starts today. So we're excited about that. >> Yeah, and we saw the same thing with the steam engine, it was decades before it actually was perfected, and now the timeframe in our industry is compressed to years, sometimes months. >> Rob: Exactly. >> Alright, Rob, thanks very much for coming on theCUBE. Good luck with the announcement today. >> Thank you. >> Good to see you again. >> Thank you guys. >> Alright, keep it right there, everybody. We'll be right back with our next guest, we're live from the Waldorf Astoria, the IBM Machine Learning Launch Event. Be right back. [electronic music]