 Live from Las Vegas, it's theCUBE. Covering IBM Think 2018, brought to you by IBM. Welcome back to IBM Think 2018. This is theCUBE, the leader in live tech coverage, and my name is Dave Vellante. And we've been covering IBM Think. This is our second day. IBM's inaugural conference will be here three days, wall-to-wall coverage. Al Martinez here is the IBM VP of Hybrid Data Management, client success. I'm going to get that in there, because it's such an important part of the title. Al, welcome to theCUBE. Thanks for coming on. Thank you, pleasure. We're going to start with Hybrid Data Management. What do you mean by Hybrid Data Management? What is that? Well, I think it starts with data, and they call it information technology, not data technology for a reason, meaning I have the pleasure or the burden, one of the two, in terms of being able to set up what we call the AI ladder. Meaning you start with data, you push it up the stack, push value up the stack, that being analytics, ML, AI, and data today is a challenge. I mean, it's a huge problem. It doesn't matter what size client you are, it's a challenge for you. And so it's unstructured, it's structured, it can be in the cloud, it can be on-prem. So when we say hybrid, it's across the challenge that I have, it's across all those different form factors. We've got to make data simple and accessible across all those form factors. That's hybrid. It's tall job, tall order. Pretty much all jobs. Okay, how do you do it? How do I do it? Well, very carefully. We develop technologies that do just that. What we do it via is a common analytics engine, first and foremost. We use an engine like no matter what form factors say I'm in an appliance, I can query the appliance, and then if I want to take that workload outside that appliance and put it in against my own hardware, I can take that database out and still query, do the same analytics. I can put that in the cloud, do the same query and analytics, no different. So the way we do it is we don't care whether it's structured or unstructured. We don't care whether it's no SQL or SQL. We'll do both. We'll do analytic processing. We'll do operational processing and we try to do it within the same footprint. That's essentially how we do it. Okay, so what I like about this is your TAM is every customer, I mean of every company, right? That's the challenge. So what's the conversation like when you walk into a client or a prospect? How do you, what are the words they're using to describe their problems? Help us understand that. Well, that is the great question because it is very difficult to get those words out very often. A lot of clients are struggling where they are and what I call the maturity curve. To that point, what I typically do is start with a conceptual maturity curve. And if you can imagine a graph going from left to right, it's a hockey stick of value relative to maturity. And so we figure out where a client is on that maturity curve. By example, if you look at like, there's like imagine four quadrants on the left most quadrant is operations. That's your ERP systems, your billing systems. If they're there, the opportunity is cost optimization or the deal is is operational systems don't typically do well with analytics. So if they're looking at analytics, then they'll move to the next quadrant and do data warehousing. Then the opportunities tend to be data lakes. You might want to get into Hadoop. And then once you graduate from there, you go into self-service analytics. That'd be like the third quadrant. And then you're thinking about Spark as a common analytics engine. You're thinking about IoT. And then you start getting into machine learning. And by the time you hit the fourth quadrant, that is where new models begin. And you're really driving machine learning and driving the progress to AI. And what I try to think, when I look at that model, those four quadrants I just walked you through is I'm pushing as much as I can to both the developer and the business and give them the empowerment. And when you do that, then governance comes into play, data science comes into play, new personas come into play. So it's quite a challenge, but I find where the client is on that graph and figure out where they want to be, current state, desired state, and then we draw up a plan to get them there. So let's talk about those. That is, I guess, the maturity model, right? We started with the core systems, ERP, transaction systems. You started to build data warehouses, data marts. They were largely bespoke systems. It was sort of an asynchronous data move. You had to build big complicated cubes. Still do. Still do. They're driving decision support, but it got really expensive. And a lot of times it was like a snake swallowing a basketball to make a change. Okay, so then along comes a dupe, throw it into a data lake, like you say. It's kind of a reduction of investment, but then you got to get value out of it. Now you're talking about self-service analytics, Spark comes into play, simplifies things a little bit, and now you get ML, more automation. My question is, as you proceed, as customers proceed down that journey, is there a hybrid data management architecture that has to be put in place so that these aren't like separate bespoke pieces that I leave behind, but they all come together in an enterprise data model? Yes, well, so here's the way I would explain that. And making the complex as simple as possible. We figure out where they are, and then there's essentially five different key elements that we key on. One is hybrid data management, that's what I'm responsible for. And by example, the database we use supports HTAP, which means it'll do both analytical and, or warehousing and transactional processing. Same time, by example. When you're looking at unified governance, that would be number two. The unified governance is, the best way to describe that is, is unified governance is done for data, what libraries do for books. Same concept. And then the third one is then, when you're pushing that closer to the developer, then that's where you get into data science and the models start building upon themselves and that's where the magic happens. Those are the three, but there's two more. Under data science, I usually call out machine learning, because machine learning is very important. I mean, that enables that path to AI that everybody talks about, the bridge to AI. And then finally, I think a key to any client strategy is open source. What open, you know, most people don't know that IBM is one of the largest contributors to open source, like Apache Spark, by example. We believe in open source because it hastens or increases the pace to market. So if you have those five different strategies, that's how you be successful. Within my organization, you can have an appliance, you can have, for hybrid data management, you can have an HTAP database, we have one-click data movement, all those things go into that to make up that complete solution. HTAP, by the way, is hybrid transaction and analytic processing. That's exactly right. You see those worlds come together. I remember the Z13 announcement a couple of years ago, you guys made a big deal out of that, and so that's actually happening, is that right? That is absolutely happening, yes. And so that involves what actually doing the analytics in the transaction system, is that right? In the database of the transaction system? It depends on workloads. There's a lot of depending factors, but yeah, that's the... As opposed to what? Putting some kind of infiniband pipe into my data warehouse. Well, you talked about it earlier, where previously you have to create complete separate data marks. You have to transition and use ETL to go from an operational store or in a transactional store to an analytical store, completely separate, trying to do both those in the same database as our objective. That's HLAP. Excellent. Now, you're also running the Global Elite program. What is that all about? Well, let me back up for a second and tell you how we got here. I am running the Global Elite program, but it started out just as a sheer campaign of driving personalization for our clients. Pretty simple, right? But we have got the technology now to really personalize our experience with our clients, using ML and some of these same technologies that I talked about. By example, we use ML and Watson to both internally and externally with clients. In other words, internally, we make recommendations to our analysts. Externally, you can use a bot and ask Watson the questions. We're pushing all our content out, essentially free of charge, opening it up. We have very aggressive push to push that content out and we're driving direct to expert. So that's just like standard now for us. That's the basic. But then we've taken that further because we want to treat each client relative to their needs and profile. So what we've done is we just, for the platform offerings that we have, we just offered a... We just came up with a new offering called Enhanced Support. So what that does is it's front of the line service, kind of consider it your airline priority service. So it's front of the line, it's faster response times targets, and it also provides some consulting. And then on top of that, we've got what's called a premium tier. And that premium tier does everything of what I've already described, but then it adds a named context and experts to work directly with you with one foot within IBM and one foot within whatever client and that expertise required. So I give you all that. Global lead is at the top of that. These are our partners that are innovating with us, that are rewarding us with their business, but they're innovating with us. They're serving as references and together we're partnering and transforming together, whether it's retail, insurance, or otherwise. So those are a small set of our global elite clients, and I encourage any clients that are listening out there, if they feel like, hey, I want to partner directly with IBM, I want to push the envelope, references are in my future, I'm in. So what are some examples that you can share with us? Some examples of... So look, what we've done, we tend to have a motto with the global elites that we never say no. And I'm still waiting, I haven't said no yet, but we'll see if that ever comes. Well, we never say no, and what we've done, by example, as a result, as an evolution of the global elite program is, think conferences like this. A lot of times, you can only send so many people. So what we've done is we've taken a mini conference and we call it Analytics University, and we've taken that directly to clients. And we'll do a day or two and do this conference in a miniature scale, focused on the areas and the content that they prefer. The other thing we've done is then, a lot of times when we do that, we'll find interests and visions that they have that they have not been able to really get into a roadmap or your progress. So then we'll bring them into the lab and we'll do design thinking sessions. And then we'll work together, and in terms of doing the design thinking sessions, what we essentially ultimately accomplish is one independent roadmap between two different companies. Because they help set our roadmap, we help influence theirs, and all of a sudden, they've got a strategy to the future and it's just, it's organically aligned with ours. Excellent. All right, Al, so let's put the bumper sticker on IBM Think 2018. It's only day two here, but what's your take away from the conference? Trucks are pulling away. What's the bumper sticker say? Hey, the bumper sticker is make data simple. That's where my head's at. Make data simple. I got a podcast out there that's called Make Data Simple. I'd encourage everybody to listen to it. We get into all these different technologies. But I think we make data simple with the water, the breath we get data, we can drive value up the stack. So make data simple podcast, right? Yeah, do it. It's under, it's actually under analytics insights. Okay, your iTunes. Analytics insights. Are you probably there at all? That's all me. All right, beautiful. Yeah, make data simple podcast, Google that, and you'll find it. Now, thanks very much for coming with you. Thank you. Pleasure to have you. All right, keep it right there, everybody. We'll be back right after this short break.