 Live from New York, it's theCUBE, covering machine learning everywhere. Build your ladder to AI, brought to you by IBM. Welcome back to New York City. theCUBE continue our coverage here at IBM's event machine learning everywhere. Build your ladder to AI. And with us now is Rob Thomas, who's the vice president of, or general manager rather of IBM analytics. Sorry about that Rob. Good to have you with us this morning. Good to see you sir. And Dave, great to see you as well. Yeah, so let's just talk about the event first. Great lineup of guests. We're looking forward to visiting with several of them here on theCUBE today. But let's talk about first off, general theme with what you're trying to communicate and where you sit in terms of that ladder to success in the AI world. So maybe you start by stepping back to, we saw you guys a few times last year. Once in Munich I recall another one in New York and the theme of both of those events was data science renaissance. We started to see data science picking up steam in organizations. We also talked about machine learning. The great news is that in that timeframe, machine learning has really become a real thing in terms of actually being implemented into organizations and changing our company's run. And that's what today is about is basically showcasing a bunch of examples, not only from our clients, but also from within IBM, how we're using machine learning to run our own business. And the thing I always remind clients when I talk to them is machine learning is not going to replace managers, but I think machine learning managers that use machine learning will replace managers that do not. And what you see today is a bunch of examples of how that's true because it gives you superpowers. If you've automated a lot of the insight, data collection, decision making, it makes you a more powerful manager. And that's going to change a lot of enterprises. It seems like a no-brainer, right? Or a must-have. I think there's always a, sometimes there's a fear factor, there's a culture piece that holds people back. We're trying to make it really simple in terms of how we talk about the day and the examples that we show to get people comfortable, to kind of take a step onto that ladder back to the beginning. Okay, it's conceptually a no-brainer, but it's a challenge, you wrote a blog and it was really interesting. One of the clients said to you, I'm so glad I'm not in the technology industry. And you went, hello, I got news for you. You are in the technology industry. So a lot of customers that I talk to, feel like, well, you know, in our industry it's really not getting disrupted. That's kind of taxis and retail. We're in banking, but digital is disrupting every industry and every industry is going to have to adopt ML, AI, whatever you want to call it. Can traditional companies close that gap? What's your take? I think they can, but I'll go back to the word I used before, it starts with culture. Do, am I accepting that I'm a technology company, even if traditionally I've made tractors, as an example, or if traditionally I've just been selling shirts and shoes? Have I embraced the role, my role as a technology company? Because if you set that culture from the top, everything else flows from there. It can't be, you know, IT is something that we do on the side. It has to be a culture of its fundamental to what we do as a company. There was an MIT study that said, data-driven cultures drive productivity gains of six to 10% better than their competition. You can't, that stuff compounds too. So if your competitors are doing that and you're not, not only do you fall behind in the short term, but you fall woefully behind in the medium term. And so I think companies are starting to get there, but it takes a constant push to get them focused on those. So if you're a tractor company, you've got human expertise around making tractors and messaging and marketing tractors. And then data is kind of there, sort of a bolt-on because everybody's got to be data-driven. But if you look at the top companies by market cap, you know, we were talking about it earlier, data's foundational, it's at their core. So that seems to me to be the hard part, Rob. I'd like you to comment in terms of that cultural shift. How do you go from sort of data in silos and not having cloud economics that are fundamental to having that dynamic? And how does IBM help? You know, I think to give companies credit, I think most organizations have developed some type of data practice or discipline over the last, call it five years. But most of that's historical, meaning, yeah, we'll take snapshots of history, we'll use that to guide decision-making. You fast forward to what we're talking about today, just so we're on the same page. Machine learning is about you build a model, you train a model with data, and then as new data flows in, your model's constantly updating. So your ability to make decisions improves over time. That's very different from we're doing historical reporting on data. And so I think it's encouraging that companies have kind of embraced that data discipline in the last five years. But what we're talking about today is a big next step and what we're trying to break it down to what I call the building blocks. So back to the point on an AI ladder. What I mean by an AI ladder is you can't do AI without machine learning. You can't do machine learning without analytics. You can't do analytics without the right data architecture. So those become the building blocks of how you get towards a future of AI. And so what I encourage companies is, if you're not ready for that AI leading edge use case, that's okay, but you can be preparing for that future now. That's what the building blocks are about. Yeah, I think we're, I know we're gonna have, Jeremiah Alyang on a little bit later, but I was reading something that he had written about. Got an instinct, you know, from the C suite and how that's how companies were run, right? You had your CEO, your president, they made decisions based on their guts or their instincts. And now you got this whole new objective tool out there that's gold. And it's kind of taking some of the gut and instinct out of it in a way. And maybe there are people who still can't quite grasp that that maybe their guts and their instincts, you know, what their gut tells them is one thing, but there's pretty objective data that might indicate something else. Moneyball for business. A little bit of a clash. I mean, is there a little bit of a clash in that respect? I think you'd be surprised by how much decision making is still pure opinion. I mean, I see that everywhere. But we're heading more towards what you described for sure. One of the clients talking here today, AMC Networks, I think it's a great example of a company that you wouldn't think of as a technology company, primarily a content producer, they make great shows. But they've kind of gone that extra step to say we can integrate data sources from third parties, our own data about viewer habits, we can do that to change our relationship with advertisers. Like that's a company that's really embraced this idea of being a technology company and you can see it in their results. And so results are not coincidence in this world anymore. It's about a practice applied to data, leveraging machine learning on a path towards AI. If companies are doing that, they're going to be successful. And we're going to have Vitaly from AMC on. But so there's a situation where they have embraced it. They've dealt with that culture. And data has become foundational. Now I'm interested as to what their journey look like. What are you seeing with clients? How they break down the silos of data that have been built up over decades? I think, so they get almost like a maturity curve. You've got, and really I talk about is 40, 40, 20, where 40% of organizations are really using data just to optimize costs right now. That's okay. But that's on the lower end of the maturity curve. 40% are saying, all right, I'm starting to get into data science. I'm starting to think about how I extend to new products, new services, using data. And then 20% are on the leading edge. And that's where I'd put AMC networks by the way. Because they've done unique things with integrating data sets and building models so they've automated a lot of what used to be painstakingly long processes, internal processes to do it. So you got this 40, 40, 20 of organizations in terms of their maturity on this. If you're not on that curve right now, you have a problem. But I'd say most are somewhere on that curve. If you're in the first 40% and right now data for you is just about optimizing cost, you're going to be behind. If you're not right now, you're going to be behind in the next year. That's a problem. So I kind of encourage people to think about what it takes to be in the next 40%. Ultimately, you want to be in the 20% that's actually leading this transformation. It's changing 40, 20, 40. That's where you want it to go, right? Yes, exactly. You want to flip that paradigm. I want to ask you a question. You've done a lot of M&A in the past. You spent a lot of time in Silicon Valley. And Silicon Valley obviously very disruptive cultures and organizations. And it's always been a sort of technology disruption. It seems like there's another disruption going on, not just horizontal technologies, cloud or mobile or social, whatever it is, but within industries. Some industries have been, as we've been talking, radically disrupted. Retail, taxis, certainly advertising, et cetera, et cetera. Some have not yet, the client that you talk to. Do you see technology companies, generally Silicon Valley companies specifically, as being able to pull off a sort of disruption of not only technologies, but also industries? And where does IBM play there? You've made a, sort of, Ginny in particular has made a deal about, hey, we're not going to compete with our customers. So, talking about this sort of dual disruption agenda, one on the technology side, one within industries, that Apple's getting into financial services, and Amazon getting into grocery. What's your take on that, and where does IBM fit in that world? So, I mean, IBM's been in Silicon Valley for a long time, I would say probably longer than 99.9% of the companies in Silicon Valley. So, we've got a big lab there. We do a lot of innovation out of there. So, love it. I mean, the culture of the Valley is great for the world because it's all about being the challenger, it's about innovation, and that's tremendous. No fear. Yeah, absolutely. So, look, we work with a lot of different partners, some who are purely based in the Valley. I think they challenge us. We can learn from them, and that's great. I think the one misnomer that I see right now is there is a undertone that innovation's happening in Silicon Valley and only in Silicon Valley. And I think that's a myth. Give an example, we just, in December, we released something called EventStore, which is basically our stab at reinventing the database business that's been pretty much the same for the last 30 to 40 years. And we're now ingesting millions of rows of data a second. We're doing it in a parquet format using a spark engine. Like, this is an amazing innovation that will change how any type of IoT use case can manage data. Now, people don't think of IBM when they think about innovations like that, because it's not the only thing we talk about. We don't have, the IBM website isn't dedicated to that single product, because IBM is a much bigger company than that. But we're innovating like crazy. A lot of that is out of what we're doing in Silicon Valley and our labs around the world. And so, I'm very optimistic on what we're doing in terms of innovation. Yeah, in fact, I think we rephrased my question. I was, you know, you're right. I mean, people think of IBM as getting disrupted. I wasn't posing it. I think of you as a disruptor. I know that may sound weird to some people, but in the sense that you guys made some huge bets with things like Watson unsolving some of the biggest world's problems. And so, I see you as disrupting sort of, maybe yourselves, okay, frame that. But I don't see IBM as saying, okay, we are going to now disrupt healthcare, disrupt financial services. Rather, we are going to help our, like some of your, I don't know if you call them competitors, Amazon, as they say, getting into content and buying grocery food stores. You guys seem to have a different philosophy. That's what I'm trying to get to, is we're going to disrupt ourselves, okay, fine. But we're not going to go hard into healthcare, hard into financial services other than selling technology and services to those organizations. Does that make sense? Yeah, I mean, look, our mission is to make our clients better at what they do. That's our mission. We want to be essential in terms of their journey to be successful in their industry. So frankly, I love it every time I see an announcement about Amazon entering another vertical space because all of those companies just became my clients because they're not going to work with Amazon when they're competing with them head to head, day in, day out. So I love that. So us working with these companies to make them better through things like Watson Health where we're doing healthcare, it's about making companies who have built their business in healthcare more effective at how they perform, how they drive results, revenue, ROI for their investors. That's what we do. That's what IBM has always done. Yeah, so it's an interesting discussion. I tend to agree. I think Silicon Valley maybe should focus on those technology disruptions. I think they'll have a hard time pulling it off that dual disruption. It may be broadly defined Silicon Valley as Seattle and so forth. But it seems like that formula has worked for decades and will continue to work. Other thoughts on sort of the progression of ML, how it gets into organizations, where you see this going? Again, I was saying earlier, the parlance is changing. Big data is kind of, hmm, okay, Hadoop, well, it's fine. We seem to be entering this new world that's pervasive, it's embedded, it's intelligent, it's autonomous, it's self healing, it's all these things that we aspire to. We're now back in the early innings. We're late innings of big data, that's kind of, but early innings of this new era. What are your thoughts on that? You know, I'd say the biggest restriction right now, I see. We talked before about somehow, sometimes companies don't have the desire. So we have to help create the desire, create the culture to go do this. Even for the companies that have a burning desire, the issue quickly becomes a skill gap. And so we're doing a lot to try to help bridge that skill gap. Let's take data science as an example. There's two worlds of data science that I would describe. There's clickers and there's coders. Clickers want to do drag and drop. They will use traditional tools like SPSS, which we're modernizing. That's great, we want to support them if that's how they want to work and build models and deploy models. There's also this world of coders. This is people that want to do all their data science and ML and Python and Scala and R, like that's what they want to do. And so we're supporting them through things like data science experience, which is built on Apache Jupiter. It's all open source tooling, it's designed for coders. The reason I think that's important, it goes back to the point on skill sets. There is a skill gap in most companies. So if you walk in and you say, this is the only way to do this thing, you kind of excluded half the companies because they say I can't play in that world. So we are intentionally going after a strategy that says, there's a segmentation in skill types, in place of there's a gap, we can help you fill that gap. That's how we're thinking about that. And who does that bode well for? If you say that you're trying to close a gap, is it, does that bode well for, we talked about the millennial crowd coming in and so they, whether they have a different approach or different mental outlook on this or is it to the mid-range employee, who is open-minded, but who's in that sweet spot? You think that say, oh, this is a great opportunity right now. So just take data science as an example, the clicker-coder comment I made. I would put the clicker audience is mostly people that are 20 years into their career. They've been around a while. The coder audience is all the millennials, it's all the new audience. So I think the greatest beneficiary is the people that find themselves kind of stuck in the middle. Which is, they're kind of interested in this. They're just out of both sides of the line. But they've got the skill set and the desire to do some of the new tooling and new approaches. So I think this kind of creates an opportunity for that group in the middle to say, what am I going to adopt as a platform for how I go forward and how I provide leadership in my company? So your advice, if you're talking to your clients, I mean, you're also talking to their workforce. So your advice to them is, join the jump on the wave, right? Absolutely. You can't straddle, you've got to go. And you got to experiment, you got to try things. Ultimately, organizations are going to gravitate to things that they like using in terms of an approach or a methodology or a tool. But that comes with experimentation. So people need to get out there and try something. I wonder if we could talk about developers a little bit. We were talking to Dinesh earlier. And you guys, of course, have focused on data scientists, data engineers, obviously developers. And Dinesh was saying, look, many, if not most of the 10 million Java developers out there, they're not like focused around the data. That's really the data scientist job. But then my colleague, John Furrier says, hey, data is the new development kit. Somebody said recently, Andreessen's comment, software's eating the world. Well, data is eating software. So if Furrier's right, and that comment is right, it seems like developers increasingly have to become more data aware. Fundamentally, blockchain developers clearly are more data focused. What's your take on the developer community, where they fit into this whole AI machine learning space? So I was just in Las Vegas yesterday, and I did a session with a bunch of our business partners, ISVs, so software companies, mostly a developer audience. And the discussion I had with them was around, you're building great products, you're building great applications, but your product is only as good as the data and the intelligence that you embed in your product. Because you're still putting too much of a burden on the user, as opposed to having everything happen magically, if you will. So that discussion was around how do you embed data, embed AI, into your products, and do that at the forefront, versus you deliver a product and the client asks to say, all right, now I need to get my data out of this application and move it somewhere else so I can do the data science that I want to do. So that's what I see happen with developers, it's kind of getting them to think about data, as opposed to just thinking about the application development framework, because that's where most of them tend to focus. Right. Well, we've talked about, well, earlier on about governance, I'm just curious, with Madhu and Coach Arvid, we'll have that interview in just a little bit here. I'm kind of curious about your take on that, is that it's a little kinder, gentler, friendlier than maybe some might look at it nowadays because of some organization that it causes within your group and some value that's being derived from that, that more efficiency, more contextual information that's more relevant, whatever. When you talk to your clients about meeting rules, regs, GDPR, all these things, how do you get them to see that it's not a black veil of doom and gloom, but it really is and really more of an opportunity for them to cash in? You know, my favorite question to ask when I go visit clients is I say, I said, just show of hands, how many people have all the data they need to do their job? To date, nobody has ever raised that. That's too many hands up. And the reason I phrase it that way is that's fundamentally a governance challenge. And so when you think about governance, I think everybody immediately thinks about compliance, GDPR, that type of thing you mentioned, and that's great, but there's two use cases for governance, one is compliance, the other one is self-service analytics. Because if you've done data governance, then you can make your data available to everybody in the organization because you know you've got the right rules, the right permission set up, that will change how people do their jobs. And I think sometimes governance gets painted into a compliance corner when organizations need to think about it as, this is about making data accessible to my entire workforce. That's a big change. I don't think anybody has that today, except for the clients that we're working with where I think we've made good strides. What's your sort of number one, two, and three, or pick one advice for those companies as you blogged about, don't realize yet that they're in the software business and the technology business. For them to close the machine intelligence, machine learning, AI gap, where should they start? So I do think it can be basic steps. And the reason I say that is, if you go to a company that hasn't really viewed themselves as a technology company and you start talking about machine intelligence, AI, like everybody runs away scared, like it's just not interesting. So I bring it back to building blocks. For a client to be great in data and to become a technology company, you really need three platforms for how you think about data. You need a platform for how you manage your data. So they get those data management. You need a platform for unified governance and integration. And you need a platform for data science and business analytics. And to some extent, I don't care where you start, but you got to start with one of those. And if you do that, you'll start to create a flywheel of momentum where you'll get some small successes, then you can go in the other area. And so I just encourage everybody to start down that path, pick one of the three, or you may already have something going in one of them, so then pick one where you don't have something going. Just start down the path because those building blocks, once you have those in place, you'll be able to scale AI and ML in the future in your organization. But without that, you're gonna always be limited to kind of a use case at a time. And I would add is you've talked about a couple of times today is that cultural aspect, that realization that in order to be data-driven, buzzword, you have to embrace that and drive that through the culture. Right, that starts at the top, right? Which is not normal to have a culture of we're gonna experiment, we're gonna try things. Half of them may not work. And so it starts at the top in terms of how you set the tone and set that culture. IBM Think, we're less than a month away. Cube's gonna be there, very excited about that. First time that you guys have done Think, you've consolidated all your big events. What can we expect from you guys? I think it's gonna be an amazing show. To your point, we thought about this for a while, consolidating to a single IBM event. There's no question just based on the response and the enrollment we have so far, that was the right answer. Love people from all over the world, a bunch of clients. We've got some great announcements that will come out that week. And for clients that are thinking about coming, honestly the best thing about it is all the education and training. We've basically built a curriculum, and think of it as a curriculum around how do we make our clients more effective at competing with the Amazons of the world back to the other point. And so I think we've built a great curriculum and it'll be a great week. Excellent, if I've heard anything today, it's about don't be afraid to dive into the deep end, just dive. Right, get after it. And looking forward to the rest of the day. Rob, thank you for joining us here. And we'll see you in about a month. Sounds great. Right around the corner. All right, Rob Thomas joining us here from IBM Analytics and GM at IBM Analytics. Back with more on theCUBE.