 Live from Times Square in New York City, it's theCUBE, covering IBM's Change the Game, winning with AI, brought to you by IBM. Hello everybody, welcome to theCUBE's special presentation. We're covering IBM's announcements today around AI. IBM, as theCUBE does, runs of sessions and programs in conjunction with Strata, which is now at the Javits, and we're here with Rob Thomas, who's the general manager of IBM Analytics. Long time, CUBE alum, Rob, great to see you. Dave, great to see you. So you guys got a lot going on today. We're here at the Weston Hotel. You've got an analyst event, you've got a partner meeting, you've got an event tonight. Change the game, winning with AI at Terminal 5. Check that out, ibm.com slash win with AI, go register there, but Rob, let's start with what you guys have going on. Give us the rundown. Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So we've structured a week where today we're gonna spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening we've got a broadcast that's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's gonna be a pretty unique event, which should be a lot of fun. So this place, it looks like a cool venue, Terminal 5 is just up the street on the west side highway, probably a mile from the Javits Center. So definitely check that out. All right, let's talk about, Rob, we've known each other for a long time. We've seen the early Hadoop days. You guys were very careful about diving in. You kind of let things settle and watched very carefully and then came in at the right time. But we saw the evolution of so-called big data go from a phase of really reducing investments, cheaper data warehousing. And what that did is allowed people to collect a lot more data and kind of get ready for this era that we're in now. But maybe you can kind of give us your perspective on the phases, the waves that we've seen of data and where we are today and where we're going. I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. But I think probably nobody disagrees that they need something there. The question is how do you get there? So you think about the steps. It's about a lot of people started with, we're going to reduce the cost of our operations. We're going to use data to take out cost. That was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data. We need higher performance data, BI data warehousing. So everybody, I would say, is dabbled in those two areas. The next leap forward is self-service analytics. So how do you actually empower everybody in your organization to use and access data? And then the next step beyond that is, can I use AI to drive new business models, new levers of growth for my business? So I kind of ask clients, pin yourself on this journey. Most are kind of depends on the division or the part of the company. They're at different areas. But as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around. So I try to get them to pin down, where are you versus where do you want to go? Okay, so four phases, basically. The sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration. You guys are getting a lot of investments there. And then kind of new business models with AI automation running on top. Where are we today? Would you say we're kind of in between BI, DW modernization and self-service on our way to self-service analytics? Or what's your sense? I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard because it requires you sometimes to take a couple of steps back and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through kind of the processes around data governance. So sometimes you have to take one step back to go two steps forward. That's why I see a lot of people, I'd say stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall. And I think that's pretty illustrative of what's possible. Yeah, so, okay. So you're saying they've got to maybe take a step back and get the infrastructure right with let's say a catalog and to give them some basic things that they have to do, some X's and O's, the Vince Lombardi play down here. And also skill sets, I would imagine, is a key part of that. So that's what they've got to do to get prepared. And then what's next? They start creating new business models, imagining is where the chief data officer comes in and it's an executive level. What are you seeing clients as part of digital transformation? What's the conversation like with customers? The biggest change, the great thing about the times we live in is technology's become so accessible. You can do things very quickly. We created a team last year called Data Science Elite. And we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with Data Science. So we put a team in normally one month, two months, normally a team of two or three people, our investment. And we say, let's go build a model, let's get to an outcome. And you can do this incredibly quickly now. I think people, I tell clients, I see so many that say, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today who is a distinguished engineer at IBM and he's very much involved in that team. And I think we have a customer who's actually gone through that to talk about what their experience was with the Data Science Elite team. All right, you got some hard news coming up. You've actually made some news earlier with Hortonworks and Red Hat. I want to talk about that, but you've also got some hard news today. Take us through that. Yeah, let's talk about all three. First, Monday we announced kind of expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I've talked about. Every enterprise is modernizing their data and application of states. I don't know if there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was we're working closer with Red Hat to take all of our data services as part of cloud private for data, which are basically microservice for data and we're running those on OpenShift and we're starting to see great customer attraction with that. And where does Hortonworks come in? Hadoop has been kind of the outlier on moving to microservices, containers. We're working with Hortonworks to help them make that move as well. So it's really about the three of us getting together and helping clients with this modernization journey. So I just to remind people, so you remember ODPI folks and it was all this kerfuffle, but why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore open source. IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to when it's public call that the relationship with IBM drove many, many seven figure deals, which obviously means that customers are getting value out of this. So it's great to see that come to fruition and it wasn't just a Barney announcement a couple of years ago. So congratulations on that. Now, there's other news that you guys announced this morning. Talk about that. So two other things. One is we are announcing a relationship with Stack Overflow. 50 million developers go to Stack Overflow month. It's an amazing environment for developers that are looking to do new things and we're sponsoring a community around AI. Back to your point before you said, is there a skills gap in enterprises? There absolutely is. I don't think that's a surprise. Data science, AI developers, not every company has the skills they need. So we're sponsoring a community to help drive the growth of skills in and around data science and AI. So it's things like Python, R, Scala. These are the languages of data science and it's a great relationship with us in Stack Overflow to build a community to get things going on skills. Great. Okay, and then there was one more. Last one's a product announcement. This is one of the most interesting product announcements we've had in quite a while. Imagine this, you write a SQL query and traditional approaches, I've got a server, I point at that server, I get the data, it's pretty limited. We're announcing technology where I write a query and it can find data anywhere in the world. I think of it as wide area SQL. So it can find data on a automotive device, a telematics device, an IoT device. It could be a mobile device. We think of it as SQL the whole world. You write a query, you can find the data anywhere it is and we take advantage of the processing power on the edge. The biggest problem with IoT is it's been the old mantra of go find the data, bring it all back to a centralized warehouse. That makes it impossible to do real time. We're enabling real time because we can write a query once, find data anywhere. This is technology we've had in preview for the last year. We've been working with a lot of clients to prove out use cases to do it. We're integrating it as the capability inside of IBM Cloud Private for data. So if you buy IBM Cloud Private for data, it's there. Interesting, so I mean you've been around as I have long enough to see some of the pendulum swings and there's clearly a pendulum swing back toward decentralization in the edge. But the key is from what you just described is you're sort of redefining the boundary. So I presume it's the edge, any cloud or on-premises where you can find that data, is that correct? Yeah, so it's multi-cloud. Every organization is going to be multi-cloud. Like 100%, that's going to happen. And that could be private, it could be multiple public cloud providers. But the key point is data on the edge is not just limited to what's in those clouds. It could be anywhere that you're collecting data. And we're enabling an architecture which performs incredibly well because you take advantage of processing power on the edge where you can get data anywhere that it sits. Okay, so then I'm setting up a cloud, I'll call it a cloud architecture that encompasses the edge where essentially there are no boundaries and you're bringing security. We talked about containers before. We've been talking about Kubernetes all week here. I bet. Had a big data show. Popular topic. And then of course cloud, it was interesting. I think many of the Hadoop distro vendors kind of missed cloud early on. And then now are sort of saying, oh wow, it's a hybrid world and we've got a part of you guys obviously made some moves, a couple billion dollar moves to do some acquisitions and get hardcore into cloud. So that becomes a critical component. You're not just limiting your scope to the IBM cloud. You're recognizing that it's a multi-cloud world. That's what customers want to do, your comments. It's multi-cloud and it's not just the IBM cloud. I think the most predominant cloud that's emerging is every client's private cloud. Every client I talk to is building out a containerized architecture. They need their own cloud and they need seamless connectivity to any public cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting. People don't want to talk about it. But one of the biggest inhibitors kind of to this AI point comes back to data curation, data ingestion because if you're dealing with multiple clouds, suddenly your data is in a bunch of different spots. Well, so you're basically, when we talked about this a lot on theCUBE, you're bringing the cloud model to the data wherever the data lives. Is that the right way to think about it? I think organizations have spoken, set aside what they say, look at their actions. Their actions say we don't want to move all of our data to any particular cloud. We'll move some of our data. We need to give them seamless connectivity so that they can leave their data what they want. We can bring cloud native architectures to their data. We can also help move their data to a cloud data of architecture if that's what they prefer. Well, it makes sense, right? Because you've got physics, latency, right? You've got economics. You're moving all the data into a public cloud is expensive and just doesn't make economic sense. And then you've got things like GDPR, which say, well, you have to keep the data, certain laws of the land, if you will, that say you've got to keep data in whatever it is in Germany or whatever country. So those sort of edicts kind of dictate how you approach managing workloads and what you put where, right? Right, okay. What's going on with Watson? Give us the update there. Yeah, I think there's, I get a lot of questions. People trying to peel back the onion of what exactly is it? So I want to make that super clear here. Watson is a few things. Start at the bottom. You need a runtime for models that you've built. And so we have a product called Watson Machine Learning runs anywhere you want. That is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models. You need a development environment. That is called Watson Studio. So we had a product called Data Science Experience. We've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment. Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features. Vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson applications. We've got Watson for call centers, doing customer service and chatbots. And then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs. They've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So it is really a stack of capabilities. And where we're driving the greatest productivity, this is a lot of the examples you'll see tonight for clients is clients that have bought into this idea of, I need a development environment. I need a runtime where I can deploy models anywhere. We're getting a lot of momentum on that. And then that raises the question of, well, do I have explainability? Do I have trust and transparency? And that's another thing that we're working on. Okay, so this API oriented architecture, exposing all these services will make it very easy for people to consume. Right. Okay, so we've been talking all week at Cube NYC, is big data is an AI, is this old wine new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation and early adoption anyway of some of these innovations, but a lot of potential going forward. Last thoughts. But people have to realize is AI is not magic. It's still computer science. So it actually requires some hard work. You need to roll up your sleeves. You need to understand how I get from point A to point B. You need to develop an environment. You need to run time. I just, I want people to really think about this. It's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time and it's not a lot of time, you'll see the examples tonight. Most of them have been done in one or two months. There's great business value and starting to leverage AI in your business. Awesome, all right, so if you're in the city or you're at strata, go to ibm.com slash win with AI, register for the event tonight. Rob, we'll see you there. Thanks so much for coming back. It's going to be fun. Thanks, Dave. Great to see you. All right, keep it right there. Everybody, we'll be back with our next guest right after this short break. You're watching theCUBE.