>> Voiceover: Live from Las Vegas, it's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We are live in Las Vegas for IBM InterConnect 2017. This is IBM's Cloud show and, now, data show. This is theCUBE's coverage. I'm John Furrier with my cohost, Dave Vellante. Our next guest is Meg Swanson, VP of Marketing for Bluemix, the whole kit and caboodle, SoftLayer of Bluemix. Now you get to watch some data platform, IOT. The Cloud's growing up. How you doing? Good to see you again. >> It's good. Good to see you guys. Every time we get together, it's just huge growth. Every time, every month to month. Under Bluemix, we've pulled together infrastructure. The area that was called SoftLayer. And because we had developers that absolutely you need a provision down to bare metal servers, all the way up to applications. So we pulled the infrastructure together with the developer services, together with our VMware partnership, all in a single console. Continuing to work on, with clients, on just having a unified experience. That's why we have it under the Bluemix brand. >> You knew us when we were just getting theCUBE started. We knew you when you were kicking off the developer program, with Bluemix, was announced here in theCUBE. Seems like 10 dog years ago, which is about 50 years, no, that was, what, four years ago now? Are you four years in? >> I think so. Yeah, 'cause I remember running from the Hakkasan club, we had just ended a virtual reality session, and I had to run, and then I sat down, and we started immediately talking about Bluemix 'cause we just launched it. >> So here's the update. You guys have been making a lot of progress, and we've been watching you. It's been fantastic, 'cause you really had to run fast and get this stuff built out, 'cause Cloud Native, it wasn't called Cloud Native back then, it was just called Cloud. But, essentially, it was the Cloud Native vision. Services, microservices, APIs, things, we've talked about that. What's the progress? Give us the update and the status, and where are you? >> Yeah, obviously just massive growth in services and our partners. When you look at, we had Twitter up with us today, we've had continual growth in the technology partners that we bring to bear, and then also definitely Cloud Native. But then also helping clients that have existing workloads and how to migrate. So, massive partnerships with VMware. We also just announced partnership with Intel HyTrust on secure cloud optimization. When we first met, we talked so much about you're going to win this with an ecosystem. And the coolest thing is seeing that pay off every day with the number of partners that we've been so blessed to have coming to us and working together with us to build out this ecosystem for our clients. >> And what's the differentiator, because what's happening now is you're starting to see the clear line of sight from the big cloud players. You have you guys, you have Oracle, you see Microsoft, you see SAP, you all got the version of the cloud. And it's not a winner-take-all market, it's a multi-cloud world, as we're seeing. Certainly open-source is driving that. How do you guys differentiate, and is it the same message? What's new in terms of IBM's differentiators? What's the key message? >> That we're absolutely staying core to the reason we went into this business. We are looking at, what are the challenges that our clients are looking to solve? How do we build out the right solutions for them? And look at the technologies they're using today, and not have them just forklift everything to a public cloud, but walk with them every step of the way. It's absolutely been about uncovering the partnerships between on-premises and the Cloud, how you make that seamless, how you make those migrations in minutes versus hours and days. The growth that we've seen is around helping clients get to that journey faster, or, if they're not meant to go fully public Cloud, that's okay, too. We've been absolutely expanding our data centers, making sure we have everything lined up from a compliance standpoint. Because country to country, we have so many regulations that we need to make sure we're protecting our clients in. >> I want to ask you, and David Kenny referenced it a little bit today, talked about we built this for the enterprise, it didn't stem out of a retailer or a search. I don't know who he was talking about, but Martin Schroeter, on the IBM earnings call, said something that I want to get your comment on, and if we can unpack a little bit. He said, "Importantly, we've designed Watson "on the IBM Cloud to allow our clients "to retain control of their data and their insights, "rather than using client data "to educate a central knowledge graph." That's a nuance, but it's a really big statement. And what's behind that, if I can infer, is use the data to inform the model, but we're not going to take your data IP and give it to your competitors. Can you explain that a little bit, and what the philosophy is there? >> Yeah, absolutely. That is a core tenet of what we do. It's all about clients will bring their data to us to learn, to go to school, but then it goes home. We don't keep client data, that's critical to us that everything is completely within the client's infrastructure, within their data privacy and protection. We are simply applying our cognitive, artificial intelligence machine learning to help them advance faster. It's not about taking their insights in learning and fueling them into our Cloud to then resell to other teams. That, absolutely, it's great that you bring up that very nuanced point, but that's really important. In today's day and age, your data is your lifeblood as a company, and you have to trust where it's going, you have to know where it's going, and you have to trust that those machine learnings aren't going to be helping other clients that are possibly on the same cloud. >> Is it your contention that others don't make that promise, or you don't know, or you're just making that promise? >> We're making that promise. It's our contention that the data is the client's data. You look at the partnerships that we've made throughout Cloud, throughout Watson, it's really companies that have come to us to solve problems. You look at the healthcare industry, you look at all these partnerships that we have. Everything that we've built out on the IBM Cloud and within Watson has been to help advance client cases. You rarely see us launching something that's completely unique to IBM that hasn't been built together with a client, with a partner. Versus, there are other companies out there in this market where they're constantly providing infrastructure to run their own business, maybe their own retail store, and their own search engine. And they will continue to do that, and they absolutely should, but at the end of the day, when you're a client, what do you want to do? Are you trying to build somebody else's business, or do you want someone who's going to be all in on your business and helping you advance everything that you need to do. >> Well, it seems like the market has glombed on to public data plus automation. But you're trying to solve a harder problem. Explain that. >> When you look at the clients that we're working with and the data that we're working with, it's not just information that's out there to work in a sandbox environment and it's available to anyone, baseball statistics or something that's just out there in the wild. Every client engagement we're in, this is their critical data. You look at financial services. We just launched the great financial services solutions for developers. You look at those areas, and, oh my word, you cannot share that data, yet those clients, you look at the work we're doing with H&R Block, you have to look at, that is absolutely proprietary data, but how do we send in cognitive to help us learn, to help teach it, help teach them alongside, for the H&R Block example, the tax advisor. So we're helping them make their business better. It's not as if we ingested all of the tax data to then run a tax solution service from IBM. It's a nuance, but it's an important nuance of how we run this company. >> So seven years ago, I met this guy, and he said, the 2010 John, you said, "Data is the new development kit." And I was like, "What are you talking about?" But now we see this persona of data scientist and data engineer and the developer persona evolving. How are you redefining the developer? >> Yeah, it's a great point, because we see cognitive artificial intelligence machine learning development in developers really emerging strong as a career path. We see data scientists, especially where as you're building out any application, any solution, data is at the core. So, you had it 10 years ago, right? (laughs) >> (mumbles) But I did pitch it to Dave when I first met him in 2010. No, but this is the premise, right? Back then, web infrastructure, web scale guys were doing their own stuff. The data needs to be programmable. We've been riffing on this concept, and I want to get your thoughts on this. What DevOps was for infrastructurous code, we see a vision in our research at Wikibon that data as code, meaning developers just want to program and get data. They don't want to deal with all the under-the-hood production, complicated stuff like datasets, the databases. Maybe the wrangling could be done by another process. There's all this production heavy lifting that goes on. And then there's the creativity and coolness of building apps. So now you have those worlds starting to stabilize a bit. Your thoughts and commentary on that vision? >> Yeah, that's absolutely where it has been heading and is continuing to head. And as you look at all the platforms that developers get to work in right now. So you have augmented reality, virtual reality are not just being segmented off into a gaming environment, but it's absolutely mainstream. So you see where developers absolutely are looking for. What is a low-code environment for? I'd say more the productivity. How do I make this app more productive? But when it comes to innovation, that's where you see, that's where the data scientist is emerging more and more every day in a role. You see those cognitive developers emerging more and more because that's where you want to spend all your time. My developers have spent the weekend, came back on Monday, and I said, "What'd you do?" "I wrote this whole Getting Started guide "for this Watson cognitive service." "That's not your job." "Yeah, but it's fun." >> Yeah, they're geeking out on the weekends, having some beer and doing some hackathons. >> It's so exciting to see. That's where, that innovation side, that's where we're seeing, absolutely, the growth. One of the partnerships that we announced earlier today is around our investment in just that training and learning. With Galvanize. >> What was the number? How much? >> 10 million dollars. >> Evangelizing and getting, soften the ground up, getting people trained on cognitive AI. >> Yeah, so it's really about making an impactful investment in the work that we started, actually a couple years ago when we were talking, we started building out these Garages. The concept was, we have startup companies, we starting partnering with Galvanize, who has an incredible footprint across the globe. And when you look at what they were building, we started embedding our developers in those offices, calling them Garages because that is your workshop. That's where you bring in companies that want to start building applications quickly. And you saw a number of the clients we had on stage today consistently, started in the Garage, started in the Garage, started in the Garage. >> Yeah, we had one just on theCUBE earlier. >> Yeah, exactly, so they start with us in the Garage. And then we wanted to make sure we're continuing to fuel that environment because it's been so successful for our clients. We're pouring into Galvanize and companies in training, and making sure these areas that are really in their pioneering stages, like artificial intelligence, cognitive, machine learning. >> On that point, you bring up startups and Garage, two-prong question. We're putting together, I'm putting together an enterprise-readiness matrix. So you have startups who are building on the Cloud, who want to sell to the enterprise. And then you have enterprises themselves who are adopting Hybrid Cloud or a combination of public, private. What does enterprise-readiness mean to you guys? 'Cause you guys have a lot of experience. Google next, they said, "We're enterprising." They're really not. They're not ready yet, but they're going that way. You guys are there. What is enterprise-readiness? >> Yeah, and I see a lot of companies have ambitions to do that, which is what we need them to do. 'Cause as you mentioned, it's a multi-cloud environment for clients, and so we need clouds to be enterprise-ready. And that really comes down to security, compliance, scalability, multiple zones. It comes down to making sure you don't have just five developers that can work on something, but how do you scale that to 500? How do you scale that to 500,000? You've got these companies that you have to be able to ensure that developers can immediately interact with each other. You need to make sure that you've got the right compliance by that country, the data leaving that country. And it's why you see such a focus from us on industry. Because enterprise-grade is one thing. Understanding an industry top to bottom, when it comes to cloud compliance is a whole other level. And that's where we're at. >> It's really hard. Most people oversimplify Cloud, but it's extremely difficult. >> It is, 'cause it's not just announcing a healthcare practice for Cloud doesn't mean you just put everybody in lab coats and send out new digital material. It is you have to make sure you've got partnerships with the right companies, you understand all the compliance regulations, and you've built everything and designed it for them. And then you've brought in all the partner services that they need, and you've built that in a private and a public cloud environment. And that's what we've done in healthcare, that's what we're doing in finance, you see all the work we're doing with Blockchain. We are just going industry by industry and making sure that when a company comes to us in an industry like retail, or you saw American Airlines on stage with us today. We're so proud to be working with them. And looking at everything that they need to cover, from regulation, uptime, maintenance, and ensuring that we know and understand that industry and can help, guide, and work alongside of them. >> In healthcare and financial services, the number of permutations are mind-boggling. So, what are you doing? You're pointing Watson to help solve those problems, and you're codifying that and automating that and running that on the Cloud? >> That's a part of it. A part of it is absolutely learning. The whole data comes to school with us to learn, and then it goes back home. That's absolutely part of it, is the cognitive learning. The other part of it is ensuring you understand the infrastructure. What are the on-premises, servers that that industry has? How many transactions per second, per nanosecond, are happening? What's the uptime around that? How do you make sure that what points you're exposing? What's the security baked into all of that? So, it's absolutely, cognitive is a massive part of it, but it is walking all the way through every part of their IT environment. >> Well, Meg, thanks for spending the time and coming on theCUBE and giving us the update. We'll certainly see you out in the field as we cover more and more developer events. We're going to be doing most, if not all, of the Linux foundation stuff. Working a lot with Intel and a bunch of other folks that you're partnering with. So, we'll see you guys out at all the events. DockerCon, you name it, they're all there. >> We'll be there, too, right with them. >> Microservices, we didn't even get to Kubernetes, we could have another session on containers and microservices. Meg Swanson, here inside theCUBE, Vice President of Bluemix Marketing. It's theCUBE, with more coverage after this short break. Stay with us, more coverage from Las Vegas. (techno music)

>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's theCUBE! Covering Data Works Summit 2017. Brought to you by Hortonworks. >> Welcome back to theCUBE. I'm Lisa Martin with my co-host George Gilbert. We are live on day one of the Data Works Summit in San Jose in the heart of Silicon Valley. Great buzz in the event, I'm sure you can see and hear behind us. We're very excited to be joined by a couple of fellows from IBM. A very longstanding Hortonworks partner that announced a phenomenal suite of four new levels of that partnership today. Please welcome Asad Mahmood, Analytics Cloud Solutions Specialist at IBM, and medical doctor, and Linton Ward, Distinguished Engineer, Power Systems OpenPOWER Solutions from IBM. Welcome guys, great to have you both on the queue for the first time. So, Linton, software has been changing, companies, enterprises all around are really looking for more open solutions, really moving away from proprietary. Talk to us about the OpenPOWER Foundation before we get into the announcements today, what was the genesis of that? >> Okay sure, we recognized the need for innovation beyond a single chip, to build out an ecosystem, an innovation collaboration with our system partners. So, ranging from Google to Mellanox for networking, to Hortonworks for software, we believe that system-level optimization and innovation is what's going to bring the price performance advantage in the future. That traditional seamless scaling doesn't really bring us there by itself but that partnership does. >> So, from today's announcements, a number of announcements that Hortonworks is adopting IBM's data science platforms, so really the theme this morning of the keynote was data science, right, it's the next leg in really transforming an enterprise to be very much data driven and digitalized. We also saw the announcement about Atlas for data governance, what does that mean from your perspective on the engineering side? >> Very exciting you know, in terms of building out solutions of hardware and software the ability to really harden the Hortonworks data platform with servers, and storage and networking I think is going to bring simplification to on-premises, like people are seeing with the Cloud, I think the ability to create the analyst workbench, or the cognitive workbench, using the data science experience to create a pipeline of data flow and analytic flow, I think it's going to be very strong for innovation. Around that, most notable for me is the fact that they're all built on open technologies leveraging communities that universities can pick up, contribute to, I think we're going to see the pace of innovation really pick up. >> And on that front, on pace of innovation, you talked about universities, one of the things I thought was really a great highlight in the customer panel this morning that Raj Verma hosted was you had health care, insurance companies, financial services, there was Duke Energy there, and they all talked about one of the great benefits of open source is that kids in universities have access to the software for free. So from a talent attraction perspective, they're really kind of fostering that next generation who will be able to take this to the next level, which I think is a really important point as we look at data science being kind of the next big driver or transformer and also going, you know, there's not a lot of really skilled data scientists, how can that change over time? And this is is one, the open source community that Hortonworks has been very dedicated to since the beginning, it's a great it's really a great outcome of that. >> Definitely, I think the ability to take the risk out of a new analytical project is one benefit, and the other benefit is there's a tremendous, not just from young people, a tremendous amount of interest among programmers, developers of all types, to create data science skills, data engineering and data science skills. >> If we leave aside the skills for a moment and focus on the, sort of, the operationalization of the models once they're built, how should we think about a trained model, or, I should break it into two pieces. How should we think about training the models, where the data comes from and who does it? And then, the orchestration and deployment of them, Cloud, Edge Gateway, Edge device, that sort of thing. >> I think it all comes down to exactly what your use case is. You have to identify what use case you're trying to tackle, whether that's applicable to clinical medicine, whether that's applicable to finance, to banking, to retail or transportation, first you have to have that use case in mind, then you can go about training that model, developing that model, and for that you need to have a good, potent, robust data set to allow you to carry out that analysis and whether you want to do exploratory analysis or you want to do predictive analysis, that needs to be very well defined in your training stage. Once you have that model developed, then we have certain services, such as Watson Machine Learning, within data science experience that will allow you to take that model that you just developed, just moments ago, and just deploy that as a restful API that you can then embed into an application and to your solution, and in that solution you can basically use across industry. >> Are there some use cases where you have almost like a tiering of models where, you know, there're some that are right at the edge like, you know, a big device like a car and then, you know, there's sort of the fog level which is the, say, cell towers or other buildings nearby and then there's something in the Cloud that's sort of like, master model or an ensemble of models, I don't assume that's like, Evel Knievel would say you know, "Don't try that at home," but sort-of, is the tooling being built to enable that? >> So the tooling is already in existence right now. You can actually go ahead right now and be able to build out prototypes, even full-level, full-range applications right on the Cloud, and you can do that, you can do that thanks to Data Science Experience, you can do that thanks to IBM Bluemix, you can go ahead and do that type of analysis right there and not only that, you can allow that analysis to actually guide you along the path from building a model to building a full-range application and this is all happening on the Cloud level. We can talk more about it happening on on-premise level but on the Cloud level specifically, you can have those applications built on the fly, on the Cloud and have them deployed for web apps, for moblie apps, et cetera. >> One of the things that you talked about is use cases in certain verticals, IBM has been very strong and vertically focused for a very long time, but you kind of almost answered the question that I'd like to maybe explore a little bit more about building these models, training the models, in say, health care or telco and being able to deploy them, where's the horizontal benefits there that IBM would be able to deliver faster to other industries? >> Definitely, I think the main thing is that IBM, first of all, gives you that opportunity, that platform to say that hey, you have a data set, you have a use case, let's give you the tooling, let's give you the methodology to take you from data, to a model, to ultimately that full range application and specifically, I've built some applications specific to federal health care, specifically to address clinical medicine and behavioral medicine and that's allowed me to actually use IBM tools and some open source technologies as well to actually go out and build these applications on the fly as a prototype to show, not only the realm, the art of the possible when it comes to these technologies, but also to solve problems, because ultimately, that's what we're trying to accomplish here. We're trying to find real-world solutions to real-world problems. >> Linton, let me re-direct something towards you about, a lot of people are talking about how Moore's law slowing down or even ending, well at least in terms of speed of processors, but if you look at the, not just the CPU but FPGA or Asic or the tensor processing unit, which, I assume is an Asic, and you have the high speed interconnects, if we don't look at just, you know what can you fit on one chip, but you look at, you know 3D what's the density of transistors in a rack or in a data center, is that still growing as fast or faster, and what does it mean for the types of models that we can build? >> That's a great question. One of the key things that we did with the OpenPOWER Foundation, is to open up the interfaces to the chip, so with NVIDIA we have NVLink, which gives us a substantial increase in bandwidth, we have created something called OpenCAPI, which is a coherent protocol, to get to other types of accelerators, so we believe that hybrid computing in that form, you saw NVIDIDA on-stage this morning, and we believe especially for deploring the acceleration provided for GPUs is going to continue to drive substantial growth, it's a very exciting time. >> Would it be fair to say that we're on the same curve, if we look at it, not from the point of view of, you know what can we fit on a little square, but if we look at what can we fit in a data center or the power available to model things, you know Jeff Dean at Google said, "If Android users "talk into their phones for two to three minutes a day, "we need two to three times the data centers we have." Can we grow that price performance faster and enable sort of things that we did not expect? >> I think the innovation that you're describing will, in fact, put pressure on data centers. The ability to collect data from autonomous vehicles or other N points is really going up. So, we're okay for the near-term but at some point we will have to start looking at other technologies to continue that growth. Right now we're in the throws of what I call fast data versus slow data, so keeping the slow data cheaply and getting the fast data closer to the compute is a very big deal for us, so NAND flash and other non-volatile technologies for the fast data are where the innovation is happening right now, but you're right, over time we will continue to collect more and more data and it will put pressure on the overall technologies. >> Last question as we get ready to wrap here, Asad, your background is fascinating to me. Having a medical degree and working in federal healthcare for IBM, you talked about some of the clinical work that you're doing and the models that you're helping to build. What are some of the mission critical needs that you're seeing in health care today that are really kind of driving, not just health care organizations to do big data right, but to do data science right? >> Exactly, so I think one of the biggest questions that we get and one of the biggest needs that we get from the healthcare arena is patient-centric solutions. There are a lot of solutions that are hoping to address problems that are being faced by physicians on a day-to-day level, but there are not enough applications that are addressing the concerns that are the pain points that patients are facing on a daily basis. So the applications that I've started building out at IBM are all patient-centric applications that basically put the level of their data, their symptoms, their diagnosis, in their hands alone and allows them to actually find out more or less what's going wrong with my body at any particular time during the day and then find the right healthcare professional or the right doctor that is best suited to treating that condition, treating that diagnosis. So I think that's the big thing that we've seen from the healthcare market right now. The big need that we have, that we're currently addressing with our Cloud analytics technology which is just becoming more and more advanced and sophisticated and is trending towards some of the other health trends or technology trends that we have currently right now on the market, including the Blockchain, which is tending towards more of a de-centralized focus on these applications. So it's actually they're putting more of the data in the hands of the consumer, of the hands of the patient, and even in the hands of the doctor. >> Wow, fantastic. Well you guys, thank you so much for joining us on theCUBE. Congratulations on your first time being on the show, Asad Mahmood and Linton Ward from IBM, we appreciate your time. >> Thank you very much. >> Thank you. >> And for my co-host George Gilbert, I'm Lisa Martin, you're watching theCUBE live on day one of the Data Works Summit from Silicon Valley but stick around, we've got great guests coming up so we'll be right back.

>> Narrator: Live, from Las Vegas, it's The Cube. Covering InterConnect 2017. Brought to you by IBM. >> Welcome back, everyone. Live here in Las Vegas, this is The Cube's coverage of IBM's Interconnect 2017. I'm John Furrier with my co-host Dave Vellante. Our next guest is Eric Herzog, Cube alumni, Vice President of Product Market at IBM storage. Welcome back to The Cube. Good to see you with the shirt on. You got the IBM tag there, look at that. >> I do. Well, you know, I've worn a Hawaiian shirt now, I think, ten Cubes in a row, so I got to keep the streak going. >> So, pretty sunny here in Vegas, great weather. Storage is looking up as well. Give us the update. Obviously, this is never going away, we talk about it all the time, but now cloud, more than ever, a lot of action happening with storage, and data is a big part of it. >> Yeah, the big thing with us has been around hybrid cloud. So our software portfolio, the spectrum family, Spectrum Virtualize, Spectrum Protect, our backup package, Spectrum Scale, our scale out NAS, IBM Cloud Object Storage, all will move data transparently from on-premises configurations out to multiple cloud vendors, including IBM Bluemix. But also other vendors, as well. That software's embedded on our array products, including our VersaStack. And just two weeks ago, at Cisco Live in Melbourne, Australia, we did a announcement with Cisco around our VersaStack for the hybrid cloud. >> So what's the hybrid cloud equation look like for you guys right now, because it is the hottest topic. It's almost like brute force, everywhere you see, it's hybrid cloud, that's what people want. How does it change the storage configurations? What's the solutions look like? What's different now than it was a year ago? >> I think the key thing you've got to be able to do is to make sure the data can move transparently from an on-premise location, or a private cloud, you could have started as a private cloud config and then decid it's OK to use a public cloud with the right security protocols. So, whether you've got a private cloud moving to a public cloud provider, like Bluemix, or an on-premises configuration moving to a public cloud provider, like Bluemix, the idea is they can move that data back and forth. Now, with our Cisco announcement, Cisco, with their cloud center, is also providing the capability and moving applications back and forth. We move the data layer back and forth with Spectrum Virtualize or IBM's copy data management product, Spectrum Copy Data Management, and with Cloud Center, or the ECS, Enterprise Cloud Sweep, from Cisco, you can move the application layer back and forth with that configuration on our VersaStacks. >> So this whole software-defined thing starts, it started when people realized, hey, we can run our data centers kind of the way the big hyper-scalers do. IBM pivoted hard toward software-defined. What's been the impact that you've seen with customers? Are they actually, I mean, there was a big branding announcement with Spectrum and everything a while back. What's been the business impact of that shift? >> Well, for us, it's been very strong. So if you look at the last couple quarters, according to the analysts that track the numbers, from a total storage perspective, we've moved into the number two position, and have been, now for the last two years. And for software-defined storage, we're the number one provider of software-defined storage in the world, and have been for the last three years in a row. So we've been continuing to grow that business on the software-defined side. We've got scale-up block configurations, scale-out block configurations, object storage with IBM Cloud Object Storage, and scale-out NAS and file with our IBM Spectrum Scale. So if you're file, block, or object, we've got you covered. And you can use either A, our competitor's storage, we work with all our competitor's gear, or you could go with your reseller, and have them, or your distributor provide the raw infrastructure, the servers, the storage, flash or hard drives, and then use our software on top to create essentially your own arrays. >> So when you say competitor's gear, you're talking about what used to be known as the SAN Volume Controller, and now is Spectrum Virtualize, right? Did I get that right? >> Yes, well, we still sell the SAN Volume Controller. When you buy the Spectrum Virtualize, it comes as just a piece of software. When you buy the SAN Volume Controller as well as our FlashSystem V9000, and our Storwize V7 and V5000, they come with Spectrum Virtualize pre-loaded on the array. So we have three ways where the array is pre-loaded: SAN Volume Controller, FlashSystems V9000, and then the Storwiz products, so it's pre-loaded. Or, you can buy the stand-alone software Spectrum Virtualize and put it on any hardware you want, either way. >> So, I know we're at an IBM conference, and IBM hates, they don't talk about the competition directly, but I have to ask the competitive questions. You've had a lot of changes in the business. Obviously, cloud's coming in in a big way. The Dell EMC merger has dislocated things, and you still see a zillion starups in storage, which is amazing to me, alright? Everybody says, oh, storage is dead, but then all this VC money still funneling in and all this innovation. What's happening in the storage landscape from your perspective? >> Well, I think there's a couple things. So, first of all, software-defined has got its legs, now. When you look at it from a market perspective, last quarter ended up at almost 400 million, which put it on a, let's say, a 1.6 to 2 billion dollar trajectory for calendar 2017, out of a total software market of around 16 billion. So it's gone from nothing to roughly 2 billion out of 16 billion for all storage software of all various types, so that's hot. All flash arrays are still hot. You're looking at, right now, last year, all flash arrays end up at roughly 25% of all arrays shipped. They're now in price parity, so an all-flash array is not more expensive. So you see a lot of innovation around that. You're still seeing innovation around backup, right? You've got guys trying to challenge us with our SpectrumProtect with some of these other vendors trying to challenge us, even though backup is the most mature of the storage software spaces, there's people trying to challenge that. So, I'd say storage is still a white-hot space. As you know, the overall market is flat, so it is totally a drag out, knock-down fight. You know, the MMA and the UFC guys got nothing on what goes on in the storage business. So, make sure you wear your flak jacket if you're a storage guy. >> Meaning, you got to gain share to grow, right? >> Yes, and it's all about fighting it out. This Hawaiian shirt looks Hawaiian, but just so you know, this is Kevlar. Just in case there's another storage company here at the show. >> So what are the top conversations now with storage buyers? Because we saw Candy's announcement about the object store, Flex, for the cold storage. It changes the price points. It's always going to be a price sensitive market, but they're still buying storage. What are those conversations that you're having? You mentioned moving data around, do they want to move the data around? Do they want to keep it at the edge? Is it moving the application around? What are some of those key conversations that you're involved in? >> So we've done a couple innovative things. One of the things we've done is worked with our sales team to create what we call, the conversations. You know, I've been doing this storage gig now for 31 years. Seven start-ups, IBM twice, EMC, Maxtor and Seagate- >> John: You're a hardened veteran. >> I'm a storage veteran, that's why this is a Kevlar Hawaiian shirt. But no CIO's a storage guy, I've never met one, in 31 years, ever, ever, ever met a storage guy. So what we have to do is elevate the conversation when you're talking to the customer, about why it's important for their cloud, why it's important for machine learning, for cognitive, for artificial intelligence. You know, this about it, I'm a Star Trek guy. I like Star Wars, too, but in Star Trek, Bones, of course, wands the body. So guess what that is? That's the edge device going through the cloud to a big, giant server farm. If that storage is not super resilient, the guy on the table might not make it. And if the storage isn't super fast, the guys on the table might not make it. And while Watson isn't there, yet, Watson Health, they're getting there. So, in ten years from now, I expect when I go to the doctor, he's just like in Star Trek, waving the wand, and boy, you better make sure the storage that that wand is talking to better be highly resilient and high performing. >> Define resilient, in your terms. >> So, resilient means you really can't have more than 30 seconds, 50 seconds a year of down time. Because whoever's on the table when that thing goes down has got a real problem. So you've got to be up all the time, and if you take it out of the healthcare space and look at other applications, whether you look at trading applications, data is the new gold. Data is the new diamonds. It's about data. Yes, I'd love to have a mound of gold, but you know what, if you have the right amount of data, it worth way more than a mound of gold is. >> You're right about the CIO and storage. They don't want to worry about storage. They don't want to spend a lot of time thinking about it. A CIO once said to me, "I care about storage like this, "I want it to be dirt cheap, lightning fast, and rock solid." Now, the industry has done a decent job with rock solid, I would say, but up until Flash, not really that great with lightning fast, and really not that great with dirt cheap. Price has come down for the hardware, but the management has been so expensive. So, is the industry attacking that problem? And what's IBM doing? >> Yeah, so the big thing is all about automation. So when I talk about moving to the hybrid cloud, I'm talking about transparent migration, transparent movement. That's an automation play. So you want to automate as much as you can, and we've got some things that we're not willing to disclose yet that'll make our storage even more automated whether it be from a predictive analytics perspective, self-healing storage that actually will heal itself, you know, go out and grab a code load and put the new code on because it knows there's a bug in the old code, and do that transparently so the user doesn't have to do anything. It's all about automating data movement and data activity. So we've already been doing that with the Spectrum family, and that Spectrum family ships on our storage systems and on our VersaStack, but automation is the critical key in storage. >> So I wonder, does that bring up new KPIs? Like, I presume you guys dog food your own storage internally, and your own IT. >> Eric: Yes >> Are you seeing, because it used to be, OK, the light's green on the disc drive, and you know, this is our uptime or downtime, planned downtime, you know, sort of standard metrics that we've known for 30-40 years. With automation, are we seeing a new set of metrics in KPIs emerge? You know, self-healing, percentage of problems that corrected themselves, or- >> Well, and you're also seeing things like time spent. So if you go back to the downturn of seven, eight, and nine, IT was devastated, right? And, as you know, you've seen a lot of surveys that IT spend is basically back up to '08, OK, the pre-08 crash. When you open up that envelope, they're not hiring storage guys anymore, and usually not infrastructure guys. They're hiring guys to do devops and testdev, and do cloud-based applications, which means there's not a lot of guys to run the storage. So one of the metrics we're seeing is, how much guys do I have managing my storage, or, my infrastructure? I used to have 50, now I'm a big bank, can I do it with 25? Can I do it with 20? Can I do it with 15? And then, how much time do they spend between the networking, the storage, the facilities themselves. These data center guys have to manage all of that. So there are new metrics about, what is the workload that my actual human beings are doing? How much of that is storage versus something else? And there's way less guys doing storage as a full-time job, anyway, because what happened in the downturn? And, so automation is critical to a guy running a datacenter, whether he's a cloud guy, whether he's a small shop. And clearly in the Fortune, global 2500, those guys, where they've got in-house IT, they've cut back on the infrastructure team and the storage team, so it's all about automation. So, part of the KPIs are not just about the storage itself, such as uptime, cost per Gig, cost per transaction, the bandwidth, you know, those sorts of KPIs. But it's also about how much time do I really spend managing the storage? So if I've only got five guys, now, and I used to have 15 guys, those five guys are managing, usually, three, to four, to five times more storage than they did in 2008 and 2009. So now you've got to do it with five guys instead of 15, so there's a KPI, right there. >> So, what about cloud? We heard David Kinney talk today about the object store with that funny name, and then he talked about this cloud-tiering thing, and I couldn't stay. I had to get ready for theCube. How do you work with those guys? How do you sell a hybrid story, together, because cloud is eating away at the traditional infrastructure business, but it's all sort of one big, happy family, I'm sure. But how do you work with a cloud group to really drive, to make the water level higher for IBM? >> So, all of our products from the Spectrum family, not all, but almost all our products from the Spectrum family, will automatically move data to the cloud, including IBM Bluemix/SoftLayer. So our on-premises can do it. If you buy our software only, and don't buy our storage arrays, or don't buy a Storwize, or don't buy a flash system, you still can automatically move that data to the cloud, including the IBM cloud object store. Our Spectrum Scale product, for example, ScaleOut NAS, and file system, which is very highly used in big data analytics and cognitive workloads, automatically, by policy, will tier-data to IBM cloud object storage. Spectrum Protect can be set up to automatically take data and back it up from on-premises to IBM cloud object storage. So we've automated those processes between our software and our array family, and IBM cloud object storage, and Bluemix and SoftLayer. And, by the way, in all honesty, we also work with other cloud vendors, just like they work with other storage vendors. All storage vendors can put data in Bluemix. Well, guess what, we can put data in clouds that are not Bluemix, as well. Of course, we prefer Bluemix. We all have IBM employee stock purchase, so of course we want Bluemix first, but if the customer, for whatever reason, doesn't see the light and doesn't go to Bluemix and goes with something else, then we want to make sure that customer's happy. We want to get at least some of the PO, and our Spectrum family, and our VersaStack family, and all of our array family can get that part of the PO. >> You need versatility to be on any cloud. >> Eric: We can be on any cloud. >> So my question for you is, the thing that came out of our big data, Silicon Valley event last week was, Hadoop was a great example, and that's kind of been, now, a small feature of the overall data ecosystem, is that batch and real time are coming together. So that conversation you're having, that you mentioned earlier, is about more real time than there is anything else more than ever. >> Well, and real time gets back to my examples of Bones on Star Trek wanding you over healthcare. That is real time, he's got a phaser burn, a broken leg, a this and that, and then we know how to fix the guy. But if you don't get that from the wand, then that's not real time analytics. >> Speaking of Star Trek, just how much data do you think the Enterprise was throwing off, just from an IOT standpoint? >> I'm sure that they had about a hundred petabytes. All stored on IBM Flash Systems arrays, by the way. >> Eric, thanks for coming on. Real quick, in the next 30 seconds, just give the folks a quick update on why IBM storage is compelling now more than ever. >> I think the key thing is, most people don't realize, IBM is the number two storage company in the world, and it has been for the last several years. But I think the big thing is our embracing of the hybrid cloud, our capability of automating all these processes. When they've got less guys doing storage and infrastructure in their shop, they need something that's automated, that works with the cloud. And that's what IBM storage does. >> All right, Eric Herzog, here, inside theCube, Vice President of Product Market for IBM Storage. I'm John Furrier, and Dave Velante. More live coverage from IBM InterConnect after this short break. Stay with us. (tech music)