>> Announcer: Live from Las Vegas, it's theCUBE, covering IBM Think 2018, brought to you by IBM. >> Welcome back to theCUBE. We are live on Day One of our three days of coverage of IBM Think, the inaugural single event from IBM. I'm Lisa Martin with Dave Vellante. We're at the Mandalay Bay in beautiful sunny Las Vegas, and we're excited to welcome to theCUBE for the first time, Steve Hill, the Global Head of Innovation at KPMG. Welcome. >> Thanks for having me here. >> So you are giving a talk Wednesday, you said, at the event. >> Yes. >> I want to get a little bit into your role at KPMG, as well as your session. So talk to us a little bit about what your role as the Global Head of Innovation. >> So Innovation is an overused word. I don't particular like the word innovation, but in the context of my role, it really is taking a look at our business and our clients, and saying what it is that our clients need for their futures. What's going to create relevance for our clients as we go forward, and how does our portfolio of services relate to that relevance? And if we have gaps where we see our services not serving them best, or not going to serve them best in the future, my job responsibility is to help for strategy purposes and for investment purposes, bring those points to bear, and to get either investment into those areas, right, or changes in the business as appropriate to make KPMG more relevant to our clients, and to their relevance to their clients, right, that's the whole idea. >> So, Lisa and I talk to a lot of people in theCUBE, and we talk lots about invention, startups inventing something or new technology that gets invented, but innovation to us, and I think KPMG is at the heart of this is taking an invention and actually applying it to effect change, getting it adopted, >> That's right. >> and changing a business, a societal change potentially, is that-- >> That's right, I mean, our short phrase for it is idea to cash for our clients, right. I mean at the end of the day, and I think this is profound in certainly corporate governance evolution, right. We've seen the advent of lots of escrow changes of how companies have been managed, enterprise has been managed, right. The Dutch started with the East Indian Trading Company, one of the first large global enterprises, and since that time we've seen the maturation, the new roles. The CIO role didn't exist much prior to 1950, right. Today we're starting to see innovation to be a very important skill and capability for all corporations, all enterprises, including government, right. And I think we're starting to see a maturation of corporate capability, I would say, in the innovation space, because the pace of change is so fast today, the political, economic, technological, social trends are so complex that you've got to get something in your muscle memory that helps you change your business as much as operate it effectively. >> I'd love to know who you're talking to within organizations. You mentioned CIO role, the CISO role, chief data officer. >> Steve: Right. >> Who are the minds that you're helping to bring together so that an enterprise that needs to digitalize to be competitive will survive, right, really survive these days? How do you help them really embrace a culture of innovation as really there's no other choice? How do you get these minds collectively agreeing, yes, this is the direction we need to go in? >> Yeah, I think, I mean first of all, this is a C-suite conversation and a board conversation in many cases, but the reality is when you start to look at the lack of innovation in an organization, right, and when the environment changes, competitors start to change, and the more complex it is, it's harder and harder for companies to pivot and to reinvent themselves. And we're seeing a lot of unbundling of businesses in today's environment, whether it's a company that moves packages, right, or a professional services firm, or a company that used to distribute videos, right. I mean things change and some of the irony is that sometimes the innovation in companies like Kodak, Steve Sasson invented digital camera, it took eight minutes to go from a snap to a picture, but they invented digital technology from cameras, and that the distribution of digital videos is that it actually would help to, further the demise of that organization. So that notion of how do you take change going on in the environment that you're working at, and more importantly your customers and clients, how does that convert into your business, that's a C-suite conversation, and I think innovation can be embodied in a person to help build process, meaning how do you take an idea, how do you look at the marketplace and get sensory input, convert that to ideas for strategy and for investment, and the investments have to be deployed to the field to the business, and that relationship, that whole lifecycle of innovation requires a lot of people from the enterprise to be involved in it. And I would argue the culture has to evolve because until recently most people, in fact, I would say, including current times, most people in organizations are rewarded for doing what they do well, not breaking what they do, not rethinking what they do. And the more you get into that operational mindset, that I want to wring all the efficiencies out of this process that I can. Right, the more you're wed to the status quo, the more somebody comes in from the side and takes you out. >> So I love this conversation 'cause Steve you're able to take the long view and then I want to sort of shorten it up, and then maybe put it into a longer term context. So over our, your guys 20-plus-year careers, mine a little longer, most of this industry has marched to the cadence of Moore's Law, that's where innovation came from. >> Yes. >> How do you take advantage of Moore's Law? How do you go to client server software, whatever it was, the innovation equation is changing now. It seems to be a function of, these guys have been hearing me say this all day but data that's not siloed, but data that you have access to, applying machine intelligence-- >> Yep. >> And then getting cloud, scale, economics and network effects, and then applying it to your business. >> Bingo. >> So talk about how you see the new wave of innovation in this world of digital or however you phrase it. >> Well, it's interesting, I mean, I don't hear a lot of people phrase it the way you do which I think is spot on which is, and my words are, ubiquitous access to technology which is cloud, data, and that's a huge question mark and a big C-suite conversation. Having a lot of data isn't the key, having the right lot of data is the key. Right so Dyson is moving into auto-making today, right. They have a lot of data and it's very different from what the incumbents have. Is it better or worse? We're going to see, right. And then of course smart computers which is the machine intelligence, right. Those three elements, I think they're fundamentally changing labor productivity. And what I would say is to your question is that innovation is really important here because if all you do is take those three elements and you just digitize a status quo process, you might get marginal benefits, you might get some labor productivity enhancement, you may get some marginal improvement, you may change an outsourcing agreement to an onshore RPA deal, but if that's all you do, you're setting yourself up for a disappointment because what's really going to happen with thinkers, i.e., those that have innovations, they're going to rethink the process. Most of our analog systems are created around people checking people, so you may have nine steps, I'm making it up, in a process, that in a digital world only requires one or two or zero when launching in some cases. And so if you can rethink that process to go from a nine-step to a zero-step process or a one-step that's a nano second long, that changes the dynamic of the process. In fact that's not even nirvana, right, the real nirvana is can you change your business model, right? And I would use IBM, since we're here, as an example of going from a big box with a lot of people running around it, called IBM of the past, Watson, to an API engine that David Kenny has helped to build that says, we're going to have a platform business model leveraging network effects, and I want to have a supply and a demand curve that are much faster growing than my sort of organic ways of growing a network could be, right, through people point clicking. That's innovation. >> IBM is an interesting company because it is a company with a lot of legacy, but I think gets, as you just described it, but you look at the top five companies by market value today, they're six, 700-billion dollar market companies, they are data companies not just with a lot of data, but they've put data at the core, so it's Amazon, it's Apple, it's Facebook, it's Google, et cetera. They've put data at the core whereas most organizations, I'm sure many that you deal with, they have human expertise built around other assets that aren't data. It might be factories, it might be the bottling plants, et cetera. So there's a gap, I don't know, machine, AI gap between sort of those that are innovating today, now granted the stock market can change and, >> Sure. >> Who knows, maybe the oil companies will be back involved, not to drop but how do you deal, how do you advice your clients on how to close that gap? That seems like a huge challenge. >> Well it is a huge challenge, and I think, going back to the three elements, it would be very easy for you to dive bomb into a transformation effort and say, I'm going to go and get some smart computers and hire a bunch of people that know machine intelligence and natural language process, and all that stuff, and put them in a room, and go create some applications, the bottom line is, that's not unimportant. You got to get your hand on the mountain and start climbing, but the data piece, I mean, if you don't understand how data is going to be relevant to your business and to your clients and their clients, right, in the future, you lose. And the reason why those five that you talked about earlier are so successful is they think a couple of steps ahead on the data strategy, right, and they're not thinking about, most organizations by the way, they'll say we want a data strategy and then they'll relegate the strategy thinking part to their businesses which are bifurcated, and they look at the world in silos. And they're doing exactly what they should do which is take care of those businesses, but when you step back into those five companies you've talked about, they step back from those silos and say, what is the enterprise implications, and how do I create new businesses with correlations of data that I didn't have before? I think that requires a whole different level of strategy. It's C-suite and board that has to guide those kinds of decisions. You don't see a lot of people really getting their hands dirty around intense forward-thinking data strategies at the enterprise level like we're talking about here. >> You believe we are entering or going to enter shortly a productivity renaissance. >> I agree, yes. >> That's sort of I'm talking about our off-camera conversation. Explain why you think that, compare it to sort of the Industrial Revolution. Take us through your scenario. >> Sure. So, I mean, when you think about labor, I mean, what are the things that I think those three elements will give us as a society, as a global community, is a pretty big S curve jump in labor productivity. In fact we have at KPMG some efforts to quantify what that might be, looking at what we call frontier firms, and applying those practices back to incumbents. 90% of most industry players is saying what are those differences that we can model. The fact of the matter is when you go back to the Mechanical Revolution, the Industrial Revolution, people did everything by hand prior, right. Equipment helped them do things whether it was, even the printing press saw changes in society and labor, but when you start to getting into heavy manufacture in the Industrial Revolution, productivity was enhanced dramatically, and instead of putting all of these people who were doing things by hand out of business and out of work, it actually created more jobs, a lot more jobs, and a lot more wealth for society. I think we're heading for a similar S-curve change with smart computers, cloud, and with data. And that the roboticism of people is going to be automated, and people are going to be allowed to practice and use what's between their ears a lot more. That's going to create value, insight, new questions to be asked. I mean, how many times have you ever heard this? Every time you answer a question on something that's very important, you want to understand there's two more questions to be asked. Medicine is that way for sure. But you're going to start to see massive advancement in areas where people have had to use a lot of cognitive skills, right. It's severely under-leveraged because they were doing so much roboticism and doing things that computers can start to do now. So I think you're going to start to see a renaissance, if you will, of people using their nogers in ways we haven't seen before, and that's going to change the dynamics of productivity and labor in a way that's going to create wealth for everyone. >> And it's going to change industry. So, okay, so I got a bunch of questions for you then. >> Steve: Yep. >> Here we go. And I asked this earlier but I didn't really get an answer. Will machines? >> Steve: From me or from somebody else? >> No, from somebody else. >> Steve: Okay. >> Will machines make better diagnoses than doctors and when? >> I mean, what's the regression line? I mean, the samples said, I think today you'll find machines giving better diagnoses than doctors in some cases. >> Dave: Okay. >> I don't know where the regression line sits today, but if you look at the productivity of doctors going a hundredfold, and the morals scattering around lung cancer, it's impressive. >> Dave: Yeah. >> And do you want a doctor involved? Yes, you do, because part of it is in an orthodoxy of trust which by the way ten years ago, you wouldn't put your credit card online to buy anything, right. It's the same kind of orthodoxy. But I do think that machines can read so much more data, interpolate so many more correlations than people that when you add that to an oncologist for example and cancer, you have a super oncologist capabilities which is really what you're looking for. We're not looking to replace the oncologist per se, what we're looking to do is get the productivity of the oncologist from two to 200. >> I was talking about diagnoses. So you would say yes, okay. >> Yep. >> Will large retail stores mostly disappear in your opinion? >> No, I think they'll change. I think that the customer experience is still, we're still people, we need physical space, and we need physical things to touch, smell, and feel. I think those things will change, but we'll still need experiences. >> I'm going to keep going 'cause Steve's playing along. Will driving and owning your own car become an exception? >> Yes. >> Okay. >> I can elaborate if you want. >> Please, yeah, go ahead. >> So, I mean, the first, I mean, we actually did at KPMG a study called islands of autonomy which modeled LA and San Diego, Atlanta and Chicago, and we modeled how do people move. And we did this for a reason because autonomous vehicles are often times amalgamated as one thing. Oh well autonomous vehicle is coming so you better sell your sports cars and your SUVs, not so fast. The reality is mobility is very different based on where you are. If you're in the middle of Kansas or something, you're going to need a truck to run around in your farm, but if you're in LA or Atlanta or Chicago, you're going to move with autonomy, with autonomous vehicles, and then you're going to really enable mobility as a service very clearly, but differently. The way people move in these cities is different, and if the US auto industry understands those differences, and extrapolates those to a global marketplace, they're going to be very advantaged as mobility as a service becomes real, but the first car that goes, I hate all of the viewers that love this category, but sedan is the first cars to go. I would say sports cars, I race cars, so I love sports cars. People still ride horses today but they don't need them for transportation. And SUVs, right, specialty vehicles that you may, it may not, the economies may not be there, but as we know transportation and car ownership, it's going to change fundamentally, and that's going to have a massive effect on FS, right, insurance companies, banks that are doing loans today. It's going to have a big effect on healthcare. Mobility as a service is going to transcend to healthcare, mobile healthcare in ways that we can't see. >> You got great perspective. I got one more for you, maybe a couple more. Do you think traditional banks will lose control over payment systems? >> Well, a lot of them are already nervous about that, wouldn't you think? >> Yeah, but it hasn't happened yet though. >> I understand, the bottom line is no 'cause I think the traditional banks are getting smarter and they're leveraging their own innovation horsepower to understand things like Blockchain, and how to incorporate those things into their business models. So the answer is I think the way they do, look, banks exist because of one reason, trust. They have trusted brands, right. As long as they can stay current enough to be relevant to your banking needs, you're going to stay with that trusted brand. I think the trick for banks is how do they move fast enough, leverage the technologies that make your life easier, and not waiting three or four days for bank clearing of a check, for example. >> That's they say if you're-- >> And get to that trust in a new way. >> Unless you're a Bitcoin millionaire or a billionaire. >> You still need a bank. >> Maybe somewhere down the line. >> Yeah. >> Okay, last one, I promise. Will robots and maybe even RPA reverse offshore manufacturing advantages? >> Yes. >> Can you elaborate and give us a sense of-- >> I think, first of all, if you really look at what RPA is doing in many ways, is disintermediating the value of geographic location in many ways, right. So where I may have had, again this is important that you understand, so I can still go offshore today and get labor arbitrage and get margin, but I'm not rethinking the business. What I really want to do is own, I want to have more control and I want to have more flexibility and growth in that back office function. So it would behoove when you think about our RPA, and bring in our RPA technology so I have it one onshore, two, leverage the data more securely potentially, and then leverage that data as part of my lake to say how do I use that data to correlate to get to what I really need which is customer relevance at the front office, right. So, look, I think that this whole notion of you're in a different country, and therefore the labor pools are different, and therefore their arbitrage will get benefits from that, those days are over. I mean, it's just a question of when does it die. >> Dave: The data value offsets that arbitrage advantage. >> Well, forget that. The arbitrage is dead itself because the machines, >> Yeah, yeah, right. >> You're talking about orders that have made it to a cheaper per unit cost for an RPA, for a bot to do something than it is for a person that has to eat, sleep, take vacation, and get sick, and all that stuff. And so no matter where they are in the world. So what I would say is that notion is dead. It's just not buried. And overtime we're going to migrate again to machines doing all that robotic stuff. But, again, those people, they're going to do different things. It's not like we're going to see hordes, hundreds of thousands and millions of people not be able to work, I think they're going to be doing different things using their heads in different ways. >> Lisa: I like that answer. >> That's a plan. >> Dave: It's good. >> There's a price somewhere? >> I'm absolutely wrong, I just don't know how wrong, right. >> Well, it's fun to think about, and you provided some context. It was very useful. So, thank you. >> And I imagine folks that are attending your session at IBM Think on Wednesday are going to hear a little bit more into that. So thanks for sharing. >> We going to see some specifics, yeah. >> Thanks for sharing your insights, Steve, and for joining us on theCUBE. You guys, the innovation equation is changing, and I thank you for letting me sit between a very innovative and informative conversation. >> Thank you both. It was fun. >> Thanks Steve. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE live on Day One of IBM Think 2018. Head over to thecube.net to watch all of our videos with our guests, and siliconanglemedia.com for all the written articles about that. Also check out Wikibon, find out what our analysts are saying about all things digital transformation, Blockchain, AI, ML, et cetera. Dave and I are going to be right back after a short break with our next guest. We'll see you then. (upbeat 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)

>> 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)

(upbeat music) >> Covering InterConnect 2017, brought to you by IBM. >> Welcome back. We're here live in Las Vegas from Mandalay Bay for the IBM InterConnect 2017, this is Cube's exclusive coverage with SiliconANGLE media. I'm John Furrier, my co-host Dave Vellante here all week. We missed our kickoff this morning on day two and, because the keynotes went long with Ginni Rometty. Great star line up, you had Marc Benioff, the CEO of AT&T, and CEO of H&R Block, which I love their ad with Mad Men's guy in there. Dave let's wrap up day two. Big day, I mean traffic on the digital site, ibmgo.com was off the charts and the site just performed extremely well, excited about that. Also the keynote from the CEO of IBM, Ginni, really kind of brings us themes we've been talking about on theCUBE. I want to get your reaction to that, which is social good is now a purpose that's now becoming a generational theme, and it's not just social good in terms of equality of pay for women, which is great and of course more STEM, it's everything, it's society's global impact but also the tagline is very tight. Enterprise strong, has a Boston strong feeling to it. Enterprise strong, data first, cognitive to the core, pretty much hits their sweet spot. What did you think of her keynote presentation? >> I thought Ginni Rometty nailed it. I've always been a huge fan of hers, I first met her when she was running strategy, and you know the question you used to always get because IBM 19 quarters of straight declining revenue, how long is Ginni going to get? How long is Ginni going to get? You know when is her tenure going to be up? My answer's always been the same. (laughs) Long enough to prove that she was right. And I think, I just love her presentation today, I thought she was on, she was engaging, she's a real pro and she stressed the innovation that IBM is going through. And this was the strategy that she laid out, you know, five, six years ago and it's really coming to fruition and it was always interesting to me that she never spoke at these conferences and she didn't speak at these conferences 'cause the story was not great you know, it was coming together the big data piece or the analyst piece was not formed yet. >> So you think she didn't come to these events because the story wasn't done? >> Yeah, I think she was not-- >> That is not a fact, you believe that. >> No, this is my belief. She was not ready to showcase you know, the greatness of IBM and I said about a year ago, I said you watch this whole strategy is coming together. You are going to see a lot more of Ginni Rometty than you've seen in the past. You started to see her on CNBC much more, we saw her at the Women in Tech Conference, at the Grace Hopper Conference, we saw her at World of Watson and now we see her here at InterConnect and she's very good on stage. She's extremely engaging, I thought she was good at World of Watson, I thought she was even better today. And a couple of notable things, took a swipe at both AWS and maybe a little bit at HPE, I'm not so sure that they worry about HPE. Sam Palmisano, before he left on a Wall Street Journal interview, said "I don't worry about HPE, they don't invest in RND. "I worry about Oracle." But nonetheless, she said, it's not just a new way, cloud is not just a new way to deliver IT. Right that's the Amazon you know. >> HP. >> And certainly new way of you style by IT. >> You style by IT. >> Is Meg's line. She also took a swipe at Google basically saying, look we're not taking your data to inform some knowledge draft that we're going to take your IP and give it to the rest of the world. We're going to protect your data, we're going to protect your models. They're really making a strong statement in that regard which I think is really important for CIOs and CDOs and CEOs today. Thoughts? >> I agree. I first of all am a big fan of Ginni, I always kind of question whether she came in, I never put it together like you intuitively around her not seeing the story but you go to all the analyists thing, so I think that's legit I would say that I would buy that argument. Here's what I like. Her soundbite is enterprise strong, data first, cognitive to the core. It's kind of gimmicky, but it hits all their points. Enterprise strong is core in the conversations with customers right now. We see it in theCUBE all the time. Certainly Google Nexus was one event we saw this clearly. Having enterprise readiness is not easy and so that's a really tough code to crack. Oracle and Microsoft have cracked that code. So has IBM of the history. Amazon is getting faster to the Enterprise, some of the things they are doing. Google has no clue on the Enterprise, they're trying to do it their way. So you have kind of different dimensions. So that's the Enterprise, very hard to do, table stakes are different than having pure cloud native all the time 100%, lift and shift, rip and replace, whatever you want to call it. Data First is compelling because they have a core data strategy analytics but I thought it was interesting that they had this notion of you own your own data, which implies you're renting everything else, so if you're renting everything else, infrastructure (laughs) and facilities and reducing the cost of doing business, the only thing you really got is data, highlighted by Blockchain. So Blockchain becomes a critical announcement there. Again, that was the key announcement here at the show is Blockchain. IOT kind of a sub-text to the whole show but it's supported through the Data First. And finally Cognitive to the Core is where the AI is going to kind of be the shiny, silly marketing piece with I am Watson, I'm going to solve all your health problems. Kind of showing the futuristic aspect of that but under the hood there is machine learning, under that is a real analytics algorithms that they're going to integrate across their business whether it's a line of business in verticals, and they're going to cross pollinate data. So I think those three pillars, she is a genius (laughs) in strategy 'cause she can hit all three. What I just said is a chockfull of strategy and a chockfull execution. If they can do that then they will have a great run. >> So I go back to Palmisano's statement before Ginni took over and it was a very candid interview that he gave. And as they say, you look at when he left IBM, it was this next wave was coming like a freight train that was going to completely disrupt IBM's business, so it was, it's been a long turn around and they've done it with sort of tax rates, (laughs) stock buybacks, and all kinds of financial engineering that have held the company's stock price up, (laughs) and cash flow has been very strong and so now I really believe they're in a good position. You know to get critical for just a second, yes there's no growth but look who else isn't growing. HPE's not growing, Oracle's not growing, Tennsco's not growing, Cisco's not growing, Microsoft's not growing. The only two companies really in the cartel that are growing showing any growth really are Intel a little bit and SAP. The rest of the cartel is flat (laughs) to down. >> Well they got to get on new markets and I mean the thing is new market penetration is interesting so Blockchain could be an enabler. I think it's going to be some resistance to Blockchain, my gut tells me that but the innovative entrepreneur side of me says I love Blockchain. I would be all over Blockchain if I was an entrepreneur because that really would change the game on identity and value and all that great stuff. That's a good opportunity to take the data in. >> Well the thing I like is IBM's making bets, big bets, Blockchain, quantum computing, we'll see where that goes, cloud, clearly we could talk about, you know you said it (laughs) InterConnect two or three years ago you know SoftLayer's kind of hosting. True, but Blu makes the investments hoping-- >> SoftLayer's is not all Blu makes. >> That's right, well yeah so but any rate, the two billion dollar bet that they made on SoftLayer has allowed them to go to clients and say we have cloud. Watson, NAI, Analytics, IOT these are big bets which I think are going to pay off. You know, we'll see if quantum pays off in the year term, we'll see about Blockchain, I think a lot of the bets they've been making are going to pay off, Stark, et cetera. >> So let's talk about theCUBE interviews Dave, what got your attention? I'll start while you dig up something good from your notes. I loved Willie Tejada talked about this, they're putting in these clouds journey pieces which is not a best practice it's not a reference architecture but it's actually showing the use cases of people who are taking a cross functional journey of architecture and cloud solutions. I love the quantum computing conversation we had with believe it or not the tape person. And so from the tape whatever it was, GS. >> GS8000. >> GS8000. >> It's a storage engineering team. >> But in terms of key points, modernizing IOT relevance was a theme that popped out at me. It didn't come out directly. You start to see IOT be a proof point of operationalizing data. Let me explain, IOT right now is out there. People are focused on it because it's got real business impact, because it's either facilities, it's industrial or customer connected in some sort. That puts the pressure to operationalize that data, and I think that flushes out all the cloud washing and all the data washing, people who don't have any solutions there. So I think the operationalizing of the data with IOT is going to force people to come out with real solutions. And if you don't, you're gone, so that's, you're dead. The cultural issue is interesting. Trust as now table stakes in the equation of whether it's product trusts, operational trusts, and process trusts. That's something I saw very clearly. And of course I always get excited about DevOps and cloud native, as you know. And some of the stuff we did with data as an asset from the chief data architect. >> A couple I would add from yesterday, Indiegogo who I thought had a great case study, and then Mohammed Farooq, talking about cloud brokering. 60% of IBM's business is still services. Services is very very important. And I think that when I look at IBM's big challenge, to me, John, it's when you take that deep industry expertise that they have that competes with Accenture and ENY and Deloitte and PWC. Can you take that deep industry expertise and codify it in software and transform into a more software-oriented company? That's what IBM's doing, trying to do anyway, and challenging. To me it's all about differentiation. IBM has a substantially differentiated cloud strategy that allows them not to have to go head to head with Amazon, even though Amazon is a huge factor. And the last thing I want to say is, it's what IBM calls the clients. It's the customers. They have a logo slide, they bring up the CEOs of these companies, and it's very very impressive, almost in the same way that Amazon does at its conferences. They bring up great customers. IBM brings in the C-Suite. They're hugging Ginni. You know, it was a hug fest today. Betty up on stage. It was a pretty impressive lineup of partners and customers. >> I didn't know AT&T and IBM were that close. That was a surprise for me. And seeing the CEO of AT&T up there really tees it out. And I think AT&T's interesting, and Mobile World Congress, one of the things that we covered at that event was the over the top Telco guys got to get their act together, and that's clear that 5G and wireless over the top is going to power the sensors everywhere. So the IOT on cars, for instance, and life, is going to be a great opportunity for, but Telco has to finally get a business model. So it's interesting to see his view of digital services from a Telco standpoint. The question I have for AT&T is, are they going to be dumped pipes or are they actually going to move up the stand and add value? Interesting to see who's the master in that relationship. IBM with cognitive, or AT&T with the pipes. >> And, you know, you're in Silicon Valley so you hear all the talk from the Silicon Valley elites. "Oh well, Apple and Amazon "and Google and Facebook, "much better AI than Watson." I don't know, maybe. But IBM's messaging-- >> Yes. >> Okay, so yes, fine. But IBM's messaging and positioning in the enterprise to apply their deep industry knowledge and bring services to bear and solve real problems, and protect the data and protect the models. That is so differentiable, and that is a winning strategy. >> Yeah but Dave, everyone who's doing-- >> Despite the technical. >> Anyone who's doing serious AI attempts, first of all, this whole bastardized definition. It's really machine learning that's driving it and data. Anyone who's doing any serious direction to AI is using machine learning and writing their own code. They're doing it on their own before they go to Watson. So Watson is not super baked when it comes to AI. So what I would say is, Watson has libraries and things that could augment traditional custom-built AI as a kernel. Our 13-year-old guest Tanmay was on. He's doing his own customizing, then bring it to Watson. So I don't see Watson being a mutually exclusive, Watson or nothing else. Watson right now has a lot of things that adds to the value but it's not the Holy Grail for all things AI, in my opinion. The innovation's going to come from the outside and meet up with Watson. That to me is the formula. >> Going back to Mohammed Farooq yesterday, he made the statement, roughly, don't quote me on these numbers, I'll quote myself, for every dollar spent on technology, 10 dollars are going to be spent on services. That's a huge opportunity for IBM, and that's where they're going to make Watson work. >> If I'm IBM and Watson team, and I'm an executive there and engineering lead, I'm like, look it, what I would do is target the fusion aspect of connecting with their customers data. And I think that's what they're kind of teasing out. I don't know if they're completely saying that, but I want to bring my own machine learning to the table, or my own custom stuff, 'cause it's my solution. If Watson can connect with that and handshake with the data, then you got the governance problem solved. So I think Seth, the CDO, is kind of connecting the dots there, and I think that's still unknown, but that's the direction that I see. >> And services, it remains critical because of the complexity of IBM's portfolio, but complexity has always been the friend of services. But at the same time, IBM's going to transform its services business and become more software-like, and that is the winning formula. At the end of the day, from a financial perspective, to me it's cash flow, cash flow, cash flow. And this company is still a cash flow cow. >> So the other thing that surprised me, and this is something we can kind of end the segment on is, IBM just reorganized. So that's been reported. The games, people shift it a little bit, but it's still the same game. They kind of consolidated the messaging a little bit, but I think the proof point is that the traffic for on the digital side, for this show, is 2X World of Watson. The lines to get into keynotes yesterday and today were massive. So there's more interest in InterConnect than World of Watson. >> Well we just did. >> Amazing, isn't it? >> Well then that was a huge show, so what that means is, this is hitting an interest point. Cloud and data coming together. And again, I said it on the intro yesterday. IOT is the forcing function. That to me is bringing the big data world. We just had Strata Hadoop and R event at BigDataSV. That's not Hadoop anymore, it's data and cloud coming together. And that's going to be hitting IOT and this cognitive piece. So I think certainly it's going to accelerate at IBM. >> And IBM's bringing some outside talent. Look at Harry Green who came from Thomas Cook, Michelle Peluso. Marketing chops. They sort of shuffled the deck with some of their larger businesses. Put Arvind Krishna in charge. Brought in David Kenny from the Weather Company. Moved Bob Picciano to the cognitive systems business. So as you say, shuffle things around. Still a lot of the same players, but sometimes the organization-- >> By the way, we forgot to talk about Don Tapscott who came on, my favorite of the day. >> Another highlight. >> Blockchain Revolution, but we interviewed him. Check out his book, Blockchain can be great. Tomorrow we got a big lineup as well. We're going to have some great interviews all day, going right up to 5:30 tomorrow for day three coverage. This is theCUBE, here at the Mandalay Bay for IBM InterConnect 2017. I'm John Furrier and Dave Vellante. Stay with us, join us tomorrow, Wednesday, for our third day of exclusive coverage of IBM InterConnect 2017, thanks for watching.

>> Commentator: Live from Las Vegas. It's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Hello everyone. Welcome to theCUBE special broadcast here at the Mandalay Bay in Las Vegas for IBM InterConnect 2017. This is IBM's big Cloud show. I'm John Furrier. My co-host, David Vellante for the next three days will be wall-to-wall coverage of IBM's Cloud Watson. All the goodness from IBM. The keynote server finishing up now but this morning was the kickoff of what seems to be IBM's Cloud strategy here with Dave Vellante. Dave, you're listed in the keynote, we are hearing the presentation. We had the General Manager/Vice President of Data from Twitter on there, Chris Moody, talkin' about everything from the Trump presidential election being the avid tweeter that he is and got a lot of laughs on that. To the SVP of Cloud talking about DevOps and this is really IBM is investing 10 million dollars plus into more developer stuff in the field. This is IBM just continuing to pound the ball down the field on cloud. Your take? >> Well IBM's fundamental business premise is that cognitive, which includes analytics, John plus cloud plus specific industry solutions are the best way to solve business problems and IBM's trying to differentiate from the other cloud guys who David Kenny was on stage today saying, you know, they started with a retail business or the other guys started with search, we started with business problems, we started with data. And that's fundamental to what IBM is doing. The other point, I think is-- the other premise that IBM is putting forth is that the AI debate is over. The Artificial Intelligence, you know, wave of excitement in the 70s and 80s and then, you know, nothing is now back in full swing. An AI on the Cloud is a key differentiator from IBM. In typical IBM fashion for the last several Big Shows, IBM brought out not an IBMer but a customer or and or a partner. And today it brought out Chris Moody from Twitter talking about their relationship with IBM but more specifically the fact that Twitter's 11 years old. Some of the things you're doing with Twitter obviously connected into March Madness and then Arvind Krishna who has taken over for Robert LeBlanc as the head of the Cloud group, talked about IBM, AI, IBM's Cloud, blocked chain, trusted transactions, IoT, DevOPs, all the buzz words merged into IBM's Cloud Strategy. And of course, we reported several years ago at this event about Bluemix as the underpinning of IBM's developer strategy. And as well it showcased several partners. Indiegogo was a crowdfunding site and others. Some of those guys are going to be in theCUBE. So. You know as they say, this AI debate is over. It's real and IBM's intent is to the platform for business. >> Dave, the thing I want to get your thoughts on is IBM's on a 19 consecutive quarters of revenue problems with the business on general but they've been on a steady course and they kind of haven't wavered. So it's as if they know they got to shrink to grow approach but we just came off the heels of Google Next which is their Cloud Show. How the Amazon is on re-invent as the large public cloud but the number one question on the table that's going to power IoT, that's going to power AI, is the collision between cloud computing and IoT, cloud computing in big data I should say is colliding with IoT at the center which is going to fuel AI and so, it brings up the question of enterprise readiness. Okay? So this is the number one conversation in the hallways here at Las Vegas and every single Cloud Show in the enterprise is, can I move to the cloud? Obviously it's a hybrid world, multi-cloud world. IBM's cloud play. They had a Cloud. They're in the top four as we put them in there. Has to be enterprise ready but yet it as to spawn the development side. So again, your take on enterprise readiness and then really fueling the IoT because IoT is a real conversation at an architectural level that is shifting the-- tipping the scales if you will for where the action will be. >> Well John, you and I have talked in theCUBE for years now. Going on probably five years that IBM had to shrink to grow. They've got the shrink part down. They've divested some of its business like the x86 business and the microelectronics business. They have not solved the grow problem. Let's just say 19 straight quarters of declining revenue. But here's the question. Is IBM stronger today than it was a year ago? And I would argue yes and why is that? One is its focus. Its got a much clearer focus on its strategy around cognitive, around data and marrying that to Cloud. I think the other is as an 80 billion dollar company even though it's shrinking, its free cash flow is still 11.6 billion. So it's throwing off a lot of cash. Now of course, IBM made those numbers, made its earnings numbers by with through expense control, its got lower tax right. Some of the new ones of the financial engineering. Its got some good IP revenue. But nonetheless, I would still argue that IBM is stronger this year than it was a year ago. Having said that, IBM's service as business is still 60% of the company. The software business is still only about 30% into it but 10% is hardware. So IBM-- people say IBM has exited the hardware business. It hasn't exited completely the hardware business but it's only focusing on those high value areas like mainframe and they're trying to sort of retool power. Its got a new leader with Bob Picciano but it's still 60% of the company's business is still services and it's shifting to a (mumbles) model. An (mumbles) model. And that is sometimes painful financially. But again John, I would argue that it is stronger. It is better positioned. And now its got some growth potential in place with AI and with, as you say, IoT. We're going to have Harriet Green on. We're going to have Deon Newman on. Focusing on the IoT opportunity. The weather company acquisition as a foundation for IoT. So the key for IBM is that it's strategic imperatives are now over 40% of its business. IBM promised that it would be a 40 billion dollar business by 2018 and it's on track to do that. I think the question John is, is that business as profitable as its old business? And can it begin growing to offset the decline in things like storage, which has been seeing double digit declines and its traditional hardware business. >> So Dave, this is to my take on IBM. IBM has been retooling for multiple years. At least a five year journey that they have to do because let's just go down the enterprise cloud readiness matrix that I'm putting together and let's just go through the components and then think about what was old IBM and what's new. Global infrastructure. Compute networking, storage and content delivery, databases, developer tools, security and identity, management tools, analytics, artificial intelligence, Internet of Things, mobile services, enterprise applications, support, hybrid integration, migration, governance and security. Not necessarily in that order. That is IBM, right? So this is a company that has essentially (mumbles) together core competencies across the company and to me, this is the story that no one's talking about at IBM is that it's really hard to take those components and decouple them in a fashion that's cloud enabled. This is where, I think, you're going to start to see the bloom on the rose come out of IBM and this is what I'm looking at because IBM had a little bit here, they had a little bit here, then a little stove pipe over here. Now bringing that together and make it scalable, it's elastic infrastructure. It's going to be really the key to success. >> Well, I think, if again if you breakdown those businesses into growth businesses, the analytics business is almost 20 billion. The cloud business is about 14 billion. Now what IBM does is that they talk about as a service runway of you know, 78 billion so they give you a little dimensions on you know, their financials but that cloud business is growing at 35% a year. The as a service component, let's call it true cloud, is growing over 60% a year. Mobile growing, 35%. Security, 14%. Social, surprisingly is down actually year on year. You would thought that would be a growth theory for them but nonetheless, this strategic initiatives, this goal of being 40 billion by 2018 is fundamental to IBM's future. >> Yeah and the thing too about the enterprise rate is in the numbers, it speaks to them where the action is. So right now the hottest conversations in IT are SLA's. I need SLA's. I have a database strategy that has to be multi-database. So (mumbles) too. Database is a service. This is going to be very very important. They're going to have to come in and support multiple databases and identity and role-based stuff has to happen because now apps, if you go DevOps and you go Watson Data Analytics, you're going to have native data within the stack. So to me, I think, one of the things that IBM can bring to the table is around the enterprise knowledge. The SLA's are actually more important than price and we heard that at Google Next where Google tried it out on their technologies and so, look at all the technology, buy us 'cause we're Google. Not really. It's not so much the price. It's the SLA and where Google is lacking as an example is their SLA's. Amazon has really been suring up the SLA's on the enterprise side but IBM's been here. This is their business. So to me, I think that's going to be something I'm going to look for. As well as the customer testimonials, looking at who's got the hybrid and where the developer actually is. 'Cause I think IoT is the tell sign in the cloud game and I think a lot of people are talking about infrastructures of service but the actual B-platform as a service and the developer action. And to me, that's where I'm looking. >> Well comparing and contrasting, you know, those two companies. Google and Amazon with IBM, I think completely different animals. As you say, you know, Google kind of geeky doesn't really have the enterprise readiness yet although they're trying to talk that game. Diane Green hiring a lot of new people. AWS arguebly has, you know, a bigger lead on the enterprise readiness. Not necessarily relative to IBM but relative to where they were five years ago. But AWS doesn't have the software business that IBM has yet. We'll see. Okay so that's IBM's ace in the hole is the software business. Now having said that, David Kenny got on stage today. So he came out and he's doing his best Jeremy Burton impression. Came out in sort of a James Bond, you know, motif and guys with sunglasses and he announced the IBM Cloud Object Storage Flex. And he said, yes we have a marketing department and they came up with that name. You know, this to me is their clever safe objects tour to compete with S3, you know several years late. After Amazon has announced S3. So they're still showing up some of that core infrastructure but IBM's-- the (mumbles) of IBM strategy is the ability to layer cognitive and their SAS Portfolio on top of Cloud and superglue those things together. Along, of course, with its analytics packages. That's where IBM gets the margin. Not in volume infrastructure as a service. >> I want to get your take on squinting through the marketing messages of IBM and get down to the meat and the bone which is where is the hybrid cloud? Because if you look at what's going on in the cloud, we hear the new terms, lift and shift. Which to me is rip and replace. That's one strategy that Google has to take is if you run (mumbles) and Google, you're kind of cloud native. But IBM is dealing a lot at pre-existing enterprise legacy stuff. Data center and whatnot so the lift and shift is an interesting strategy so the question is, for you is, what does it take for them to be successful? With the data platform, with Watson, with IoT, as enterprise extend from the data center with hybrid. >> Well I think that, you know, again IBM's (mumbles) is the data and the cognitive platform. And what IBM is messaging to your question is that you own your data. We are not going to basically take your data and form our models and then resell your IP. That's what IBM's telling people. Now why don't we dig into that a little bit? 'Cause I don't understand sort of how you separate the data from the models but David Kenny on stage today was explicit. That the other guys, he didn't mention Google and Amazon, but that's who he was talkin' about, are essentially going to be taking your data into their cloud and then informing their models and then essentially training those models and seeping your IP out to your competitors. Now he didn't say that as explicitly as I just did but that's something as a customer that you have to be really careful of. Yes, it's your data. But if data trains the models, who owns the model? You own the data but who owns the model? And how do you protect your IP and keep it out of the hands of the competitors? And IBM is messaging that they are going to help you with the compliance and the governance and the (mumbles) of your organization to protect your IP. That's a big differentiator if in fact there's meat in the bone there. >> Well you mentioned data, that's a key thing. I think whether doing it really quickly is getting the hybrid equation nailed so I think that's going to like just pedal as fast as you can. Get that going. But data first enterprise is really speaks to the IoT opportunity and also the new application developers. So to me, I think, for IBM to be successful, they have to continue to nail this data as value concept. If they can do that, they're going to drive (mumbles) and I think that's their differentiation. You look at, you know, Oracle, Azure, Microsoft Azure and IBM, they're all playing their cards to highlight their differentiation. So. Table stakes infrastructures of service, get some platform as a service, cloud native, open source, all the goodness involved in all the microservices, the containers, Cooper Netties, You're seeing that marker just develop as it's developing. But for IBM to get out front, they have to have a data layer, they have to have a data first strategy and if they do that well, that's going to be consistent with what I think (mumbles). And so, you know, to me I'm going to be poking at that. I'm going to be asking all the guests. What do you think of the data strategy? That's going to be powering the AI, you're seeing artificial intelligence, and things like autonomous vehicles. You're seeing sensors, wearables. Edge of the network is being redefined so I'm going to ask the quests really kind of how that plays out in hybrid? What's your analysis going to be for the guests this week? >> Well, I think the other thing too is the degree to-- to me, a key for IBM success and their ability to grow and dominate in this new world is the degree to which they can take their deep industry expertise in health care, in financial services and certain government sectors and utilities, et cetera. Which comes from their business process, you know, the BPO organization and they're consulting and the PWC acquisition years ago. The extent to which they can take that codifier, put it in the software, marry it with their data analytics and cognitive platforms and then grow that at scale. That would be a huge differentiator for IBM and give them a really massive advantage from a business model standpoint but as I said, 60% of the IBM's business remains services so we got a ways to go. >> Alright. We're going to be drilling into it again. There's a collision between cloud and big data markets coming together that's forming the IoT. You can see machine learning. You can see artificial intelligence. And I'm really a forcing function in cloud acceleration with data analytics being the key thing. This is theCUBE. We'll be getting the data for you for the next three days. I'm John Furrier. With Dave Vellante. We'll be back with more coverage. Kicking off day one of IBM InterConnect 2017 after the short break.

>> Announcer: Live from Boston Massachusetts this is theCube. Covering Spark Summit East 2017, brought to you by Databricks. Now here are your hosts Dave Vellante and George Gilbert. >> Welcome back to theCube, everybody, we're here in Boston. The Cube is the worldwide leader in live tech coverage. This is Spark Summit, hashtag #SparkSummit. And Robbie Strickland is here. He's the Vice President of Engines & Pipelines, I love that title, for the Watson Data Platform at IBM Analytics, formerly with The Weather Company that was acquired by IBM. Welcome to you theCube, good to see you. >> Thank you, good to be here. >> So, it's my standing tongue-in-cheek line is the industry's changing, Dell buys EMC, IBM buys The Weather Company. [Robbie] That's right. >> Wow! That sort of says it all, right? But it was kind of a really interesting blockbuster acquisition. Great for the folks at The Weather Company, great for IBM, so give us the update. Where are we at today? >> So, it's been an interesting first year. Actually, we just hit our first anniversary of the acquisition and a lot has changed. Part of my role, new role at IBM, having come from The Weather Company, is a byproduct of the two companies bringing our best analytics work and kind of pulling those together. I don't know if we have some water but that would be great. So, (coughs) excuse me. >> Dave: So, let me chat for a bit. >> Thanks. >> Feel free to clear your throat. So, you were at IBM, the conference at the time was called IBM Insight. It was the day before the acquisition was announced and we had David Kenny on. David Kenny was the CEO of The Weather Company. And I remember we were talking, and I was like, wow, you have such an interesting business model. Off camera, I was like, what do you want to do with this company, you guys are like prime. Are you going public, you going to sell this thing, I know you have an MBA background. And he goes, "Oh, yeah, we're having fun." Next day was the announcement that IBM bought The Weather Company. I saw him later and I was like, "Aha!" >> And now he's the leader of the Watson Group. >> That's right. >> Which is part of our, The Weather Company joined The Watson Group. >> And The Cloud and analytics groups have come together in recognition that analytics and The Cloud are peanut butter and jelly. >> Robbie: That's absolutely right. >> And David's running that organization, right? >> That is absolutely right. So, it's been an exciting year, it's been an interesting year, a lot of challenges. But I think where we are now with the Watson Data Platform is a real recognition that the use dase where we want to try to make data and analytics and machine learning and operationalizing all of those, that that's not easy for people. And we need to make that easy. And our experience doing that at The Weather Company and all the challenges we ran into have informed the organization, have informed the road map and the technologies that we're using to kind of move forward on that path. >> And The Watson Data Platform was announced in, I believe, October. >> Robbie: That's right. >> You guys had a big announcement in New York City. And you took many sort of components that were viewed as individual discreet functions-- >> Robbie: That's right. >> And brought them together in a single data pipeline. Is that right? >> Robbie: That's right. >> So, maybe describe that a little bit for our audience. >> So, the vision is, you know, one of the things that's missing in the market today is the ability to easily grab data from some source, whether it's a database or a Kafka stream, or some sort of streaming data feed, which is actually something that's often overlooked. Usually you have platforms that are oriented around streaming data, data feeds, or oriented around data at rest, batch data. One of the things that we really wanted to do was sort of combine those two together because we think that's really important. So, to be able to easily acquire data at scale, bring it into a platform, orchestrate complex workflows around that, with the objective, of course, of data enrichment. Ultimately, what you want to be able to do is take those raw signals, whatever they are, and turn that into some sort of enriched data for your organization. And so, for example, we may take signals in from a mobile app, things like beacons, usage beacons on a mobile app, and turn that into a recommendation engine so we can feed real time content decisions back into a mobile platform. Well, that's really hard right now. It requires lots of custom development. It requires you to essentially stitch together your pipeline end to end. It might involve a machine learning pipeline that runs a training pipeline. It might involve, it's all batch oriented, so you land your data somewhere, you run this machine learning pipeline maybe in Spark or ADO or whatever you've got. And then the results of that get fed back into some data store that gets merged with your online application. And then you need to have a restful API or something for your application to consume that and make decisions. So, our objective was to take all of the manual work of standing up those individual pieces and build a platform where that is just, that's what it's designed to do. It's designed to orchestrate those multiple combinations of real time and batch flows. And then with a click of a button and a few configuration options, stand up a restful service on top of whatever the results are. You know, either at an interim stage or at the end of the line. >> And you guys gave an example. You actually showed a demo at the announcement. And I think it was a retail example, and you showed a lot of what would traditionally be batch processes, and then real time, a recommendation came up and completed the purchase. The inference was this is an out of the box software solution. >> Robbie: That's right. >> And that's really what you're saying you've developed. A lot of people would say, oh, it's IBM, they've cobbled together a bunch of their old products, stuck them together, put an abstraction layer on, and wrapped a bunch of services around it. I'm hearing from you-- >> That's exactly, that's just WebSphere. It's WebSphere repackaged. >> (laughing) Yeah, yeah, yeah. >> No, it's not that. So, one of the things that we're trying to do is, if you look at our cloud strategy, I mean, this is really part and parcel, I mean, the nexus of the cloud strategy is the Watson Data Platform. What we could have done is we could have said let's build a fantastic cloud and compete with Amazon or Google or Microsoft. But what we realized is that there is a certain niche there of people who want to take individual services and compose them together and build an application. Mostly on top of just raw VMs with some additional, you know, let's stitch together something with Lambda or stitch together something with SQS, or whatever it may be. Our objective was to sort of elevate that a bit, not try to compete on that level. And say, how do we bring Enterprise grade capabilities to that space. Enterprise grade data management capabilities end-to-end application development, machine learning as a first class citizen, in a cohesive experience. So that, you know, the collaboration is key. We want to be able to collaborate with business users, data scientists, data engineers, developers, API developers, the consumers of the end results of that, whether they be mobile developers or whatever. One of the things that is sort of key, I think, to the vision is that these roles that we've traditionally looked at. If you look at the way that tool sets are built, they're very targeted to specific roles. The data engineer has a tool, the data scientist has a tool. And what's been the difficult part is the boundaries between those have been very firm and the collaboration has been difficult. And so, we draw the personas as a Venn diagram. Because it's very difficult, especially if you look at a smaller company, and even sometimes larger companies, the data engineer is the data scientist. The developer who builds the mobile application is the data scientist. And then in some larger organizations, you have very large teams of data scientists that have these artificial barriers between the data scientist and the data engineer. So, how do we solve both cases? And I think the answer was for us a platform that allows for seamless collaboration where there is not these clean lines between the personas, that the tool sets easily move from one to the other. And if you're one of those hybrid people that works across lines, that the tool feels like it's one tool for you. But if you're two different teams working together, that you can easily hand off. So, that was one of the key objectives we're trying to answer. >> Definitely an innovative component of the announcement, for sure. Go ahead, George. >> So, help us sort of bracket how mature this end-to-end tool suite is in terms of how much of the pipeline it addresses. You know, from the data origin all the way to a trained model and deploying that model. Sort of what's there now, what's left to do. >> So, there are a few things we've brought to market. Probably the most significant is the data science experience. The data science experience is oriented around data science and has, as its sort of central interface, Jupyter Notebooks. Now, as well as, we brought in our studio, and those sorts of things. The idea there being that we'll start with the collaboration around data scientists. So, data scientists can use their language of choice, collaborate around data sets, save out the results of their work and have it consumed either publicly by some other group of data scientists. But the collaboration among data scientists, that was sort of step one. There's a lot of work going on that's sort of ongoing, not ready to bring to market, around how do we simplify machine learning pipelines specifically, how do we bring governance and lineage, and catalog services and those sorts of things. And then the ingest, one of the things we're working on that we have brought to market is our product called Lift which connects, as well. And that's bringing large amounts of data easily into the platform. There are a few components that have sort of been brought to market. dashDB, of course, is a key source of data clouded. So, one of the things that we're working on is some of these existing technologies that actually really play well into the eco system, trying to tie them well together. And then add the additional glue pieces. >> And some of your information management and governance components, as well. Now, maybe that is a little bit more legacy but they're proven. And I don't know if the exits and entries into those systems are as open, I don't know, but there's some capabilities there. >> Speaking of openness, that's actually a great point. If you look at the IIG suite, it's a great On-Premise suite. And one of the challenges that we've had in sort of past IBM cloud offerings is a lot of what has been the M.O. in the past is take a great On-Prem solution and just try to stand it up as a service in the cloud. Which in some cases has been successful, in other cases, less so. One of the things we're trying to look at with this platform is how do we leverage (a) open source. So that whatever you may already be running open source on, Prem or in some other provider, that it's very easy to move your workloads. So, we want to be able to say if you've got 10,000 lines of fraud detection code to map produce. You don't need to rewrite that in anything. You can just move it. And the other thing is where our existing legacy tech doesn't necessarily translate well to the cloud, our first strategy is see if there's any traction around an existing open source project that satisfies that need, and try to see if we can build on that. Where there's not, we go cloud first and we build something that's tailor made to come out. >> So, who's the first one or two customers for this platform? Is it like IBM Global Business Services where they're building the semi-custom industry apps? Or is it the very, very big and sophisticated, like banks and Telcos who are doing the same? Or have you gotten to the point where you can push it out to a much wider audience? >> That's a great question, and it's actually one that is a source of lots of conversation internally for us. If you look at where the data science experience is right now, it's a lot of individual data scientists, you know, small companies, those sorts of things coming together. And a lot of that is because some of the sophistication that we expect for Enterprise customers is not quite there yet. So, we wouldn't expect Enterprise customers to necessarily be onboarded as quickly at the moment. But if we look at sort of the, so I guess there's maybe a medium term answer and a long term answer. I think the long term answer is definitely the Enterprise customers, you know, leveraging IBM's huge entry point into all of those customers today, there's definitely a play to be made there. And one of the things that we're differentiating, we think, over an AWS or Google, is that we're trying to answer that use case in a way that they really aren't even trying to answer it right now. And so, that's one thing. The other is, you know, going beta with a launch customer that's a healthcare provider or a bank where they have all sorts of regulatory requirements, that's more complicated. And so, we are looking at, in some cases, we're looking at those banks or healthcare providers and trying to carve off a small niche use case that doesn't actually fall into the category of all those regulatory requirements. So that we can get our feet wet, get the tires kicked, those sorts of things. And in some cases we're looking for less traditional Enterprise customers to try to launch with. So, that's an active area of discussion. And one of the other key ones is The Weather Company. Trying to take The Weather Company workloads and move The Weather Company workloads. >> I want to come back to The Weather Company. When you did that deal, I was talking to one of your executives and he said, "Why do you think we did the deal?" I said, "Well, you've got 1500 data scientists, "you've got all this data, you know, it's the future." He goes, "Yeah, it's also going to be a platform "for IOT for IBM." >> Robbie: That's right. >> And I was like, "Hmmm." I get the IOT piece, how does it become a platform for IBM's IOT strategy? Is that really the case? Is that transpiring and how so? >> It's interesting because that was definitely one of the key tenets behind the acquisition. And what we've been working on so hard over the last year, as I'm sure you know, sometimes boxes and arrows on an architecture diagram and reality are more challenging. >> Dave: (laughing) Don't do that. >> And so, what we've had to do is reconcile a lot of what we built at The Weather Company, existing IBM tech, and the new things that were in flight, and try to figure out how can we fit all those pieces together. And so, it's been complicated but also good. In some cases, it's just people and expertise. And bringing those people and expertise and leaving some of the software behind. And other cases, it's actually bringing software. So, the story is, obviously, where the rubber meets the road, more complicated than what it sounds like in the press release. But the reality is we've combined those teams and they are all moving in the same direction together with various bits and pieces from the different teams. >> Okay, so, there's vision and then the road map to execute on that, and it's going to unfold over several years. >> Robbie: That's right. >> Okay, good. Stuff at the event here, I mean, what are you seeing, what's hot, what's going on with Spark? >> I think one of the interesting things with what's going on with Spark right now is a lot of the optimizations, especially things around GPUs and that. And we're pretty excited about that, being a hardware manufacturer, that's something that is interesting to us. We run our own cloud. Where some people may not be able to immediately leverage those capabilities, we're pretty excited about that. And also, we're looking at some of those, you know, taking Spark and running it on Power and those sorts of things to try to leverage the hardware improvements. So, that's one of the things we're doing. >> Alright, we have to leave it there, Robbie. Thanks very much for coming on theCube, really appreciate it. >> Thank you. >> You're welcome. Alright, keep it right there, everybody. We'll be right back with our next guest. This is theCube. We're live from Spark Summit East, hashtag #SparkSummit. Be right back. >> Narrator: Since the dawn of The Cloud, theCube.