>> Narrator: Live from New York, it's the Cube. Covering Big Data New York City 2016. Brought to you by headline sponsors: Cisco, IBM, Nvidia, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and Jeff Frick. >> Welcome back to New York City, everybody. This is the Cube, the worldwide leader in live tech coverage. Rob Thomas is here, he's the GM of products for IBM Analytics. Rob, always good to see you, man. >> Yeah, Dave, great to see you. Jeff, great to see you as well. >> You too, Rob. World traveller. >> Been all over the place, but good to be here, back in New York, close to home for one day. (laughs) >> Yeah, at least a day. So the whole community is abuzz with this article that hit. You wrote it last week. It hit NewCo Shift, I guess just today or yesterday: The End of Tech Companies. >> Rob: Yes. >> Alright, and you've got some really interesting charts in there, you've got some ugly charts. You've got HDP, you've got, let's see... >> Rob: You've got Imperva. >> TerraData, Imperva. >> Rob: Yes. >> Not looking pretty. We talked about this last year, just about a year ago. We said, the nose of the plane is up. >> Yep. >> Dave: But the planes are losing altitude. >> Yep. >> Dave: And when the funding dries up, look out. Interesting, some companies still are getting funding, so this makes rip currents. But in general, it's not pretty for pure play, dupe companies. >> Right. >> Dave: Something that you guys predicted, a long time ago, I guess. >> So I think there's a macro trend here, and this is really, I did a couple months of research, and this is what went into that end of tech companies post. And it's interesting, so you look at it in the stock market today: the five highest valued companies are all tech companies, what we would call. And that's not a coincidence. The reality is, I think we're getting past the phase of there being tech companies, and tech is becoming the default, and either you're going to be a tech company, or you're going to be extinct. I think that's the MO that every company has to operate with, whether you're a retailer, or in healthcare, or insurance, in banking, it doesn't matter. If you don't become a tech company, you're not going to be a company. That's what I was getting at. And so some of the pressures I was highlighting was, I think what's played out in enterprise software is what will start to play out in other traditional industries over the next five years. >> Well, you know, it's interesting, we talk about these things years and years and years in advance and people just kind of ignore it. Like Benioff even said, more SaaS companies are going to come out of non-tech companies than tech companies, OK. We've been talking for years about how the practitioners of big data are actually going to make more money than the big data vendors. Peter Goldmacher was actually the first, that was one of his predictions that hit true. Many of them didn't. (laughs) You know, Peter's a good friend-- >> Rob: Peter's a good friend of mine as well, so I always like pointing out what he says that's wrong. >> But, but-- >> Thinking of you, Peter. >> But we sort of ignored that, and now it's all coming to fruition, right? >> Right. >> Your article talks about, and it's a long read, but it's not too long to read, so please read it. But it talks about how basically every industry is, of course, getting disrupted, we know that, but every company is a tech company. >> Right. >> Or else. >> Right. And, you know, what I was, so John Battelle called me last week, he said hey, I want to run this, he said, because I think it's going to hit a nerve with people, and we were talking about why is that? Is it because of the election season, or whatever. People are concerned about the macro view of what's happening in the economy. And I think this kind of strikes at the nerve that says, one is you have to make this transition, and then I go into the article with some specific things that I think every company has to be doing to make this transition. It starts with, you've got to rethink your capital structure because the investments you made, the distribution model that you had that got you here, is not going to be sufficient for the future. You have to rethink the tools that you're utilitizing and the workforce, because you're going to have to adopt a new way to work. And that starts at the top, by the way. And so I go through a couple different suggestions of what I think companies should look at to make this transition, and I guess what scares me is, I visit companies all over the world, I see very few companies making these kind of moves. 'Cause it's a major shake-up to culture, it's a major shake-up to how they run their business, and, you know, I use the Warren Buffett quote, "When the tide goes out, you can see who's been swimming naked." The tide may go out pretty soon here, you know, it'll be in the next five years, and I think you're going to see a lot of companies that thought they could never be threatened by tech, if you will, go the wrong way because they're not making those moves now. >> Well, let's stay cognitive, now that we're on this subject, because you know, you're having a pretty frank conversation here. A lot of times when you talk to people inside of IBM about cognitive and the impact it's going to have, they don't want to talk about that. But it's real. Machines have always replaced humans, and now we're seeing that replacement of cognitive functions, so that doesn't mean value can't get created. In fact, way more value is going to be created than we can even imagine, but you have to change the way in which you do things in order to take advantage of that. >> Right, right. One thing I say in the article is I think we're on the cusp of the great reskilling, which is, you take all the traditional IT jobs, I think over the next decade half those jobs probably go away, but they're replaced by a new set of capabilities around data science and machine learning, and advanced analytics, things that are leveraging cognitive capabilities, but doing it with human focus as well. And so, you're going to see a big shift in skills. This is why we're partnering with companies like Galvanize, I saw Jim Deters when I was walking in. Galvanize is at the forefront of helping companies do that reskilling. We want to help them do that reskilling as well, and we're going to provide them a platform that automates the process of doing a lot of these analytics. That's what the new project Dataworks, the new Watson project is all about, is how we begin to automate what have traditionally been very cumbersome and difficult problems to solve in an organization, but we're helping clients that haven't done that reskilling yet, we're helping them go ahead and get an advantage through technology. >> Rob, I want to follow up too on that concept on the capital markets and how this stuff is measured, because as you pointed out in your article, valuations of the top companies are huge. That's not a multiple of data right now. We haven't really figured that out, and it's something that we're looking at, the Wikibon team is how do you value the data from what used to be liability 'cause you had to put it on machines and pay for it. Now it's really the driver, there's some multiple of data value that's driving those top-line valuations that you point out in that article. >> You know it's interesting, and nobody has really figured that out, 'cause you don't see it showing up, at least I don't think, in any stock prices, maybe CoStar would be one example where it probably has, they've got a lot of data around commercial real estate, that one sticks out to me, but I think about in the current era that we're in there's three ways to drive competitive advantage: one is economies of scale, low-cost manufacturing; another is through network effects, you know, a number of social media companies have done that well; but third is, machine learning on a large corpus of data is a competitive advantage. If you have the right data assets and you can get better answers, your models will get smarter over time, how's anybody going to catch up with you? They're not going to. So I think we're probably not too far from what you say, Jeff, which is companies starting to be looked at as a value of their data assets, and maybe data should be on the balance sheet. >> Well that's what I'm saying, eventually does it move to the balance sheet as something that you need to account for? Because clearly there's something in the Apple number, in the Alphabet number, in the Microsoft number, that's more than regular. >> Exactly, it's not just about, it's not just about the distribution model, you know, large companies for a long time, certainly in tech, we had a huge advantage because of distribution, our ability to get to other countries face to face, but as the world has moved to the Internet and digital sales and try/buy, it's changed that. Distribution can still be an advantage, but is no longer the advantage, and so companies are trying to figure out what are the next set of assets? It used to be my distribution model, now maybe it's my data, or perhaps it's the insight that I develop from the data. That's really changed. >> Then, in the early days of the sort of big data meme taking off, people would ask, OK, how can I monetize the data? As opposed to what I think they're really asking is, how could I use data to support making money? >> Rob: Right. Right. >> And that's something a lot of people I don't think really understood, and it's starting to come into focus now. And then, once you figure that out, you can figure out what data sources, and how to get quality in that data and enrich that data and trust that data, right? Is that sort of a logical sequence that companies are now going through? >> It's an interesting observation, because you think about it, the companies that were early on in purely monetizing data, companies like Dun & Bradstreet come to mind, Nielsen come to mind, they're not the super-fast-growing companies today. So it's kind of like, there was an era where data monetization was a viable strategy, and there's still some of that now, but now it's more about, how do you turn your data assets into a new business model? There was actually a great, new Clay Christensen article, it was published I think last week, talking about companies need to develop new business models. We're at the time, everybody's kind of developed in, we sell hardware, we sell software, we sell services, or whatever we sell, and his point was now is the time to develop a new business model, and those will, now my view, those will largely be formed on the basis of data, so not necessarily just monetizing the data, to your point, Dave, but on the basis of that data. >> I love the music industry, because they're always kind of out at the front of this evolving business model for digital assets in this new world, and it keeps jumping, right? It jumped, it was free, then people went ahead and bought stuff on iTunes, now Spotify has flipped it over to a subscription model, and the innovation of change in the business model, not necessarily the products that much, it's very different. The other thing that's interesting is just that digital assets don't have scarcity, right? >> Rob: Right. >> There's scarcity around the data, but not around the assets, per se. So it's a very different way of thinking about distribution and kind of holding back, how do you integrate with other people's data? It's not, not the same. >> So think about, that's an interesting example, because think about the music, there's a great documentary on Netflix about Tower Records, and how Tower Records went through the big spike and now is kind of, obviously no longer really around. Same thing goes for the Blockbusters of the world. So they got disrupted by digital, because their advantage was a distribution channel that was in the physical world, and that's kind of my assertion in that post about the end of tech companies is that every company is facing that. They may not know it yet, but if you're in agriculture, and your traditional dealer network is how you got to market, whether you know it or not, that is about to be disrupted. I don't know exactly what form that will take, but it's going to be different. And so I think every company to your point on, you know, you look at the music industry, kind of use it as a map, that's an interesting way to look at a lot of industries in terms of what could play out in the next five years. >> It's interesting that you say though in all your travels that people aren't, I would think they would be clamoring, oh my gosh, I know it's coming, what do I do, 'cause I know it's coming from an angle that I'm not aware of as opposed to, like you say a lot of people don't see it coming. You know, it's not my industry. Not going to happen to me. >> You know it's funny, I think, I hear two, one perception I hear is, well, we're not a tech company so we don't have to worry about that, which is totally flawed. Two is, I hear companies that, I'd say they use the right platitudes: "We need to be digital." OK, that's great to say, but are you actually changing your business model to get there? Maybe not. So I think people are starting to wake up to this, but it's still very much in its infancy, and some people are going to be left behind. >> So the tooling and the new way to work are sort of intuitive. What about capital structure? What's the implication to capital structures, how do you see that changing? So it's a few things. One is, you have to relook at where you're investing capital today. The majority of companies are still investing in what got them to where they are versus where they need to be. So you need to make a very conscious shift, and I use the old McKinsey model of horizon one, two and three, but I insert the idea that there should be a horizon zero, where you really think about what are you really going to start to just outsource, or just altogether stop doing, because you have to aggressively shift your investments to horizon two, horizon three, you've really got to start making bets on the future, so that's one is basically a capital shift. Two is, to attract this new workforce. When I talked about the great reskilling, people want to come to work for different reasons now. They want to come to work, you know, to work in the right kind of office in the right location, that's going to require investment. They want a new comp structure, they're no longer just excited by a high base salary like, you know, they want participation in upside, even if you're a mature company that's been around for 50 years, are you providing your employees meaningful upside in terms of bonus or stock? Most companies say, you know, we've always reserved that stuff for executives. That's not, there's too many other companies that are providing that as an alternative today, so you have to rethink your capital structure in that way. So it's how you spend your money, but also, you know, as you look at the balance sheet, how you actually are, you know, I'd say spreading money around the company, and I think that changes as well. >> So how does this all translate into how IBM behaves, from a product standpoint? >> We have changed a lot of things in IBM. Obviously we've made a huge move towards what we think is the future, around artificial intelligence and machine learning with everything that we've done around the Watson platform. We've made huge capital investments in our cloud capability all over the world, because that is an arms race right now. We've made a huge change in how we're hiring, we're rebuilding offices, so we put an office in Cambridge, downtown Boston. Put an office here in New York downtown. We're opening the office in San Francisco very soon. >> Jeff: The Sparks Center downtown. >> Yeah. So we've kind of come to urban areas to attract this new type of skill 'cause it's really important to us. So we've done it in a lot of different ways. >> Excellent. And then tonight we're going to hear more about that, right? >> Rob: Yes. >> You guys have a big announcement tonight? >> Rob: Big announcement tonight. >> Ritica was on, she showed us a little bit about what's coming, but what can you tell us about what we can expect tonight? >> Our focus is on building the first enterprise platform for data, which is steeped in artificial intelligence. First time you've seen anything like it. You think about it, the platform business model has taken off in some sectors. You can see it in social media, Facebook is very much a platform. You can see it in entertainment, Netflix is very much a platform. There hasn't really been a platform for enterprise data and IP. That's what we're going to be delivering as part of this new Watson project, which is Dataworks, and we think it'll be very interesting. Got a great ecosystem of partners that will be with us at the event tonight, that're bringing their IP and their data to be part of the platform. It will be a unique experience. >> What do you, I know you can't talk specifics on M&A, but just in general, in concept, in terms of all the funding, we talked last year at this event how the whole space was sort of overfunded, overcrowded, you know, and something's got to give. Do you feel like there's been, given the money that went in, is there enough innovation coming out of the Hadoop big data ecosystem? Or is a lot of that money just going to go poof? >> Well, you know, we're in an interesting time in capital markets, right? When you loan money and get back less than you loan, because interest rates are negative, it's almost, there's no bad place to put money. (laughing) Like you can't do worse than that. But I think, you know the Hadoop ecosystem, I think it's played out about like we envisioned, which is it's becoming cheap storage. And I do see a lot of innovation happening around that, that's why we put so much into Spark. We're now the number one contributor around machine learning in the Spark project, which we're really proud of. >> Number one. >> Yes, in terms of contributions over the last year. Which has been tremendous. And in terms of companies in the ecos-- look, there's been a lot of money raised, which means people have runway. I think what you'll see is a lot of people that try stuff, it doesn't work out, they'll try something else. Look, there's still a lot of great innovation happening, and as much as it's the easiest time to start a company in terms of the cost of starting a company, I think it's probably one of the hardest times in terms of getting time and attention and scale, and so you've got to be patient and give these bets some time to play out. >> So you're still sanguine on the future of big data? Good. When Rob turns negative, then I'm concerned. >> It's definitely, we know the endpoint is going to be massive data environments in the cloud, instrumented, with automated analytics and machine learning. That's the future, Watson's got a great headstart, so we're proud of that. >> Well, you've made bets there. You've also, I mean, IBM, obviously great services company, for years services led. You're beginning to automate a lot of those services, package a lot of those services into industry-specific software and other SaaS products. Is that the future for IBM? >> It is. I mean, I think you need it two ways. One is, you need domain solutions, verticalized, that are solving a specific problem. But underneath that you need a general-purpose platform, which is what we're really focused on around Dataworks, is providing that. But when it comes to engaging a user, if you're not engaging what I would call a horizontal user, a data scientist or a data engineer or developer, then you're engaging a line-of-business person who's going to want something in their lingua franca, whether that's wealth management and banking, or payer underwriting or claims processing in healthcare, they're going to want it in that language. That's why we've had the solutions focus that we have. >> And they're going to want that data science expertise to be operationalized into the products. >> Rob: Yes. >> It was interesting, we had Jim on and Galvanize and what they're doing. Sharp partnership, Rob, you guys have, I think made the right bets here, and instead of chasing a lot of the shiny new toys, you've sort of thought ahead, so congratulations on that. >> Well, thanks, it's still early days, we're still playing out all the bets, but yeah, we've had a good run here, and look forward to the next phase here with Dataworks. >> Alright, Rob Thomas, thanks very much for coming on the Cube. >> Thanks guys, nice to see you. >> Jeff: Appreciate your time today, Rob. >> Alright, keep it right there, everybody. We'll be back with our next guest right after this. This is the Cube, we're live from New York City, right back. (electronic music)