 Live from San Francisco, it's theCUBE. Covering IBM Think 2019, brought to you by IBM. Okay, welcome back everyone here live in San Francisco here on Moscone Street for theCUBE's exclusive coverage of IBM Think 2019. I'm John Furrier, Dave Vellante. Four days of coverage, bringing on all the action, talking to the top executives, entrepreneurs, ecosystem partners, and everyone who can bring the signal from the noise here on theCUBE. Next guest is Rob Thomas, general manager of IBM Data and AI within IBM. Cube alumni, great to see you again. Great to see you guys. So have you written a book yet this year? I mean, you've written 10 books on AI data, you're general manager. No book yet, too much work. Not enough time for a book. That's a good sign, it means you're working hard. Okay, give us the data here, because AI anywhere in the center of the announcements, we have a story up on SiliconANG that's already been reported on CNBC. John Ford was here earlier, talking to Ginny. This is a core centerpiece of it, AI on any cloud. This highlights the data conversation that you've been part of, now I think what, seven years seems like more, but this is now happening, give us your thoughts. Go back to basics, I've shared this with you before, there's no AI without IA, meaning you need an information architecture to support what you want to do in AI. We start looking into that, our thesis became, so clients are buying into that idea. The problem is, their data is everywhere, on-premise, private cloud, multiple public clouds, so our thesis became very simple. If we can bring AI to the data, it will make Watson the leading AI platform. So what we announced with Watson Anywhere is you can now have it wherever your data is. Public, private, any public cloud, build the models, run them where you want, I think it's going to be amazing. So data everywhere and AI anywhere. So containers are a big role in this, it's a little bit of a DevOps, the world you've been living in, convergence of data, cloud, how does that set for clients up, what do they need to know about this announcement? What's the impact of them, if any? The way that we enable multi-cloud in Watson Anywhere is through IBM Cloud Private for data. That's our data microservices architecture running on Kubernetes. That gives you the portability so that it can run anywhere. Because in addition to, I'd say AI ambitions, the other big client ambition is around, how do we modernize to cloud native architectures, more composable services, so the combination gets delivered as part of this. So this notion of you can't have AI without IA, it's obviously a great tagline, you use it a lot. But it's super important because there's a gap between those who sort of have AI chops and those who don't. And if I understand what you're doing is you're closing that gap by allowing you to bring, you called it AI to the data. Is it sort of a silo buster in regard to? Yeah, the model we use, I called the AI ladder. So think of it as all the levels of sophistication an organization needs to think about from how you collect data, to how you organize data, analyze data, and then infuse data with AI. That's kind of the model that we use to talk to clients about that. What we're able to do here is say, you don't have to move your data. The biggest problem with AI projects is the first task is, okay, move a bunch of data, that takes a lot of time, that takes a lot of money. We say, you don't need to do that, leave your data wherever it is. With cloud private for data, we can virtualize data from any source. That's kind of the aha moment people have when they see that. So we're making that piece really easy. What's the impact this year at IBM Think to the product portfolio? You had data products in the past, now you got AI products, any changes? How should people think of the ladders? Just gives them a kind of rubric or a view of where they fit into it. But what's up with the products? Any changes people should know about? Well, we've brought together the analytics and AI units in IBM into this new organization we call Data and AI. That's a reflection of us seeing that as two sides of the same coin, really couldn't really keep them separate. We've really simplified how we're going to market with the Watson products. It's about how you build, run, manage your AI, Watson Studio, Watson Machine Learning, Watson OpenScale. That's for clients that want to build their own AI. For clients that want something out of the box, they want an application. We've got Watson Assistant for customer service, Watson Discovery, Watson Health Assets. We've made it really easy to consume Watson whether you want to build your own or you want an application designed for the line of business. And then up and down the data stack, bunch of different announcements. We're bringing out Big SQL on Cloudera as part of our evolving partnership with the new Cloudera Hortonworks entity. Virtual Data Pipeline is a partnership that we built with Actifio. So we're doing things at all layers of the ladder. So you're simplifying the consumption from a client, your customer perspective. It's all data, it's all Watson's umbrella for brand for everything underneath that from A to Z. Is that right? Absolutely, yeah. Watson is the AI brand. It is AI technology that's having an impact. We have amazing clients on stage with us this week talking about AI is no longer, I like to say AI is not magic. It's no longer this mystical thing. We have clients that are getting real outcomes with AI today. We've got Royal Baker of Scotland talking about how they've automated and augmented 40% of their customer service with Watson Assistant. So we've got great clients talking about how they're using AI today. Are you seeing any patterns, Rob, in terms of those customers you mentioned? Some customers want to do their own AI. Some customers want it out of the box. What are the patterns that you're seeing in terms of who wants to do their own AI? Why do they want to do their own AI? Do they get some kind of competitive advantage? Do they have additional skill sets that they need? It's a maker's market is how I would describe it. There's a lot of people that want to make their own and try their own. You, I think most organizations are going to end up with hundreds of different tools for building, for running. This is why we introduced Watson OpenScale at the end of last year. That's how would you manage all of your AI environments, whether they come from IBM or not? Because you got the, and an organization has to have this manageable, understandable, regardless of which tool they're using. I would say though, the biggest impact that we see is when we pick a customer problem that is widespread. And the number one right now is customer service. Every organization regardless of industry wants to do a better job of serving clients. That's why Watson Assistant is taking off. This is where data, the value of real-time data, historical data, kind of horizontally scalable data, not silo data. We've talked about this in the past. How important is the data quality piece of this? Because you have real-time and you have historical data and everything in between that you have to bring to bear at low latency. Applications now are going to have data embedded in them as a feature. How does this change the workloads, the makeup if you manage a customer service is one piece of the low-hanging fruit? I get that, but this is a key thing. It's a data architecture more than anything, isn't it? It is. Now remember, there's two rungs at the bottom of the ladder. On data collection, we have to be able to collect data in any form and any type. That's why you've seen us do relationships with MongoDB, relationship obviously with Cloudera. We've got our own data warehouse. So we integrate all of that through our SQL engine. That then gets to your point around how are you going to organize the data? How are you going to curate it? We've got data catalog. Every client will have a data catalog for managing all their data across clouds. We're now doing automated metadata creation using AI and machine learning. So the organization piece, once you've collected it then the organization piece becomes most important. Certainly if you want to get to self-service analytics you want to make data available to data scientists around the organization you have to have those governance pieces. Talk about the ecosystem. One of the things that's been impressive IBM of the years is your partnerships. You've done good partners, partnership and you have relationships. Now in an ecosystem there's a lot of building blocks. This more complexity requires software to abstract them away. We get that. What's opportunities for you to create new relationships? Where are the opportunities for someone, a developer or a company to engage with you guys? Where's the white spaces? Where does someone take advantage of your momentum and your vision? I am dying for ISV partners that are doing domain-specific, industry-specific applications to come have them run on IBM Cloud Private for Data which unleashes all the data they need to be a valuable application. We've already got a few of those. Data Mirror is one. Sensing is another one that are running now as industry applications on top of IBM Cloud Private for Data. I'd like to have a thousand of these. So all comers there. We announced a partnership with Red Hat back in May. Eventually that became more than just a partnership but that was about enabling Cloud Private for Data on Red Hat OpenShift. So we are partnering in all layers of the stack but the greatest customer need is give me an industry solution, leveraging the best of my data. That's why I'm really looking for ISV partners to run an IBM Cloud Private. What's your pitch to those guys? Why? Why should we go to IBM? There is no other data platform that will connect to all your data sources whether they're on AWS, Azure, Google Cloud on-premise. So if you believe data is important to your application there's simply no better place to run than IBM Cloud Private for Data. That's the goal. It turns out functionality, breadth or everything. Well, integrating with all your data. Normally they have to have the application in five different places. We integrate with all the data. We build the data catalog so the data is organized so the ingestion of the data becomes very easy for the ISV. And by the way, thirdly, IBM's got pretty good reach globally. 170 countries, business partners, resellers all over the world, sales people all over the world. We will help you get your product to market. That's a pretty good value for us. You know, Dave, we talk about this on theCUBE all the time. When the cloud came, one of the best things about the cloud was that it allowed people to put applications together really quickly, stand them up, startups did that. But now in this domain world of data with the cloud scale, I think you're right. I think domain expertise is the top of the stack where you need specialism, expertise, and you don't have to build the bottom half out. What you're getting at is if you're, if you know how to create innovation in a business model, you can come in and innovate quickly. And vertical apps don't scale enough for me. So that's why I focus on horizontal things like customer service. But if you go talk to a bank, sometimes customer service is not enough. It's I want to do something in loan origination or you're an insurance company. I want to do something about underwriting. Those are the solutions that will get a lot of value out of running on an integrated data stack. Thousands of flowers bloom as kind of an ecosystem opportunity, looking forward to checking in on that. Thoughts on gaps for you guys want to do M&A on or areas that you think you want to double down on that might need some help, either organic innovation or M&A. What areas are you looking at? Can you share a little bit of a direction on that? We have a unique benefit in IBM because we have IBM research. One of our big announcers this week is what we call auto AI, which is basically automating the process of feature engineering, algorithm selection, bringing that into Watson Studio and Watson Machine Learning. I am spending most of my time figuring out how do I continue to bring great technology out of IBM research and put in the hand of clients through our products. You guys saw the debater stuff yesterday. We're just getting started with that. We've got some pretty exciting organic innovation happening in IBM. That's awesome. Great news for startups. Final question for you, for the folks watching who aren't here in San Francisco. What's the big story here at IBM Think here in San Francisco? Big event closing down the streets here in Howard Street. It's huge. What's the big story? What's the most important things happening? The most important thing to me is the customer stories here are unbelievable. I think we've gotten past this point of AI as some idea for the future. We have hundreds of clients here talking about how they did an AI project and here's the outcome they got. It's really encouraging to see. What I encourage all clients though is don't build your strategy off of one big AI project. Companies should be doing hundreds of AI projects. So in 2019, do a hundred projects. Half of them will probably fail. That's okay. The ones that work will more than make up for the ones that don't work. So we're really encouraging mass experimentation and I think the clients that are here are creating an aspirational thing for things. And just anecdotally, you mentioned earlier customer service is a low hanging fruit. Other use cases that are great low hanging fruit opportunities for AI? Data discovery, data curation. These are really hard manual tasks today. You can start to automate some of that. That has a really big impact. Rob Thomas, general manager of the data and AI group here within IBM now, part of a bigger portfolio of Watson. Rob, great to see you. Congratulations on all your success. Been following you from the beginning. Great momentum and on the right wave. Congratulations Rob. More CUBE coverage here live in San Francisco from Moscone North. I'm John Furrier, Dave Vellante. Stay with us for more coverage after this short break.