>> Narrator: Live from Las Vegas! It's the Cube covering Interconnect 2017, brought to you by IBM. >> Okay, welcome back everyone. We are live in Las Vegas at IBM Interconnect 2017, IBM's cloud and now data show. I'm John Furrier with my co-host Dave Vellante. This is the Cube. Our next guest is Derek Schoettle, the general manager of Watson Data Platform, and Adam Kocoloski who's the CTO of the Watson Data Platform. Guys, welcome to the Cube. Good to see you again Derek. Great to see you, welcome Adam! >> Thanks, John. >> So, obviously the data was a big part of the theme. You saw Chris Moody from Twitter up there, obviously, they have a ton of data. I like to joke about they have a really active user right now in the President of the United States. >> Daily State of the Union, I think, was the one take away. >> Daily State of the Union. But this is the conversation that's happening in all over IT, and enterprise, and cloud, both public and enterprise, is the data conversation in context to cloud. Super relevant right now, and there's architecturals at play, it's app, it impacts app developers, it impacts architectures. And that's the Holy Grail, the so-called app data layer or cloud data layer. What's your vision, guys, on this? Derek, I'll start with you, your vision on this data opportunity. How does IBM approach it? And what's different from, or could be different from the competitors? >> Yeah, I know, one, it's an exciting time. We were just chatting about before we went live is, there's so much change taking place in and around data, right? It used to be it's the natural currency, it's everything everyone is talking about. The reality is, it's changing business models, right? It introduces a whole new set of discussions when you introduce cloud, self-service and open source. So, when we step back and think about how we can differentiate, how we can make IBM's offer to clients and the broader market interesting, is shift to a platform strategy where it says, we have instead of discreet compossible services that act independent of one another that are not, I'll say, self-aware, shift into a platform where you have common governance, you have common management, and you have really a collaborative by design approach where data is at the epicenter. Data is what starts every conversation whether you're on the app dev side, whether you are a data scientist, someone who's, you know, at the edge of discovery. And cloud's what's enabling that, self-service is what's enabling that and operationalize is what we do. I mean, we spend our days thinking about and then operationalizing feature, function, and then performance for a lot of different workloads. 'Cause it used to be, I think the, I was at Vertica, right? So that was the introduction of volume, variety, and velocity, right? Now, with the introduction of AI and cognitive, it's really about taking any and all and rationalizing it. And any and all meaning sitting within your corporate structure, as well as what's more broadly in the internet, out available within social media, right? That to me is the shift that's taking place. It's all companies are realizing they made a lot of investments, they have a lot of data, and they're not taking advantage of it. And we see that the big shift is... People are saying data scientist, what we think about is the merging of data and science. You think of science as cognitive and AI, right? That's a small population that really understands and can take advantage of. You have a whole big market that's out there in traditional data and analytics. Our platform is about merging those two. It's really about merging those experiences so everyone takes advantage of the benefits of data and science. >> What's the conversations that you are having, Derek, with customers? Because I think that's, there's a lot of bells going off into the CXO or even practitioners when you hear about machine learning, you hear AI, cognitive, autonomous vehicles, sensor networks. Obviously that's, the alarms are going off, like, I'd better get my act together. So, how do they pull that off? How do your customers pull off making that happen? Because now you got to bring in to be cloud ready, you have all these decoupled component parts. >> Yeah. >> John: You got to operate them in the cloud and you got to kind of have an on-prem component that's hybrid. What are the conversations that you are having with customers in how they're pulling this off? >> Yeah so, I'll cover the first piece, and I know Adam is spending certainly this week and a lot of time as well with clients on this topic. You know, the first part of the discussion is do you believe that the cloud can help you? Most folks are saying, "Yes, we believe it can help". Second piece is, how do I take advantage of emerging technologies that are moving at a rate and pace that perhaps my skills, my existing IT architecture, and my business model can't fully kind of, grasp, if not take advantage of? So, what we've introduced is a methodology, a data first method, which literally is a, it sounds simple, but at the end of the day, it is a common, uniform, agile way for us as IBM to engage with partners and clients that literally starts with the discovery workshop that says how does data inform your business? It's not static reporting anymore, it's what is the data that's sitting within your organization? You heard it from James at PlayFab. Data is changing the way people build in games today, thinking about how to enrich games, so on and so forth. Data First Method is what we've introduced, so you'll see going forward, IBM will sell Data First, we will engage Data First. So, any conversation with someone who says, "How do I take advantage of AI, "or machine learning, "or data science experience?". Well, let's step back for a second and talk about data. 'Cause 30 years ago, 20, that's how every conversation started. You get on a whiteboard, you design a schema, you talk about the relationships. That's how it started, and we're kind of cycling back to that, right? We got to put data first. >> So, Adam, the geeks are always arguing speeds, "I got a Hadoop cluster here, "I got this over here.". I mean, there's a lot of variety and diversity in terms of how people can manage either databases, and middleware or what not, right? So, how do you see the data first? How does it play out architecturally? And how does that play out for the solution? >> I think one of the big advantages we have in the world of the cloud platform is this opportunity to, on the one hand, use more a broader variety of compossible services, but also be able to take different parts of the business that were historically a little bit more separated from one another and bring them together. So you look at a Hadoop-flavored data leg on premises. It's a good area to do discovery, a good area to do exploration. But what clients really care about time and time again, a common refrain is the operationalization of the analytics, of the machine learning models. How do I take this insight that my data science team has discovered, and have it really influence a business process or incorporate it into an application? And in the on-premises architecture, that's often times quite a challenge. In the world of the cloud platform and the Watson data platform, we have an opportunity to be a little bit closer to things like the world of kubernetes which are really ideally suited for deploying and scaling microservices and APIs in a cloud-native, fault-tolerant, reliable fashion, right? So, you're seeing us take that menu of composable services in the cloud platform, and treat the data platform as one such composition. An opinionated way to put together this menu of services specifically to help data professionals collaborate, and drive the business forward. >> So, when you guys announced the Watson Data Platform, I think you called it Data Works, then changed the name, about five, maybe six months ago you messaged that 80% of, you know, data professionals' time is spent wrangling data, not enough time doing the fun stuff. And the premise was you coming up with a platform for collaboration that sort of integrates those different roles as well as, as you pointed out just now, allows you to operationalize analytics. Okay, so we're five months in, six months in, what kind of proof points do you have? Have you seen it? I mean, some people were skeptical saying, "Okay, well, it's IBM, "they've put a nice wrapper on this thing, "pulling in some different legacy components, "and you know, nice name." Okay, so, what do you say to that? And what evidence do you have that what you said is going to come true is actually coming true? >> You're going to do tech and I can do customer? >> Yeah, go for customer first. >> Yeah, so what we've seen is if you think about why we ended up at a platform. So, if you roll the tape back to when Cloudant got acquired in 2014, the journey that we were on was everyone was building rich applications, they wanted to be smarter, they wanted to understand what that exhaust was coming off. >> Right. >> Derek: And they wanted to add different ingredients to it. So, instead of a do-it-yourself kit that is a bunch of proprietary interoperability issues that's a ton of expense and inefficiency, and can't take advantage of the cloud, we decided, in very much of then our path towards, let's build a platform that allows you to easily ingest, govern, curate, and then, I'll say present and deploy. So, starting in actually June, and thhis started first with Spark. We made a huge bet on Spark 'cause we believed that to be kind of the operational operating system, if you will, for an analytic fabric. So, it started in Spark. Then, when we announced the Watson Data Platform in October it was, here's how we're going to take our heritage run governance, our heritage run traditional structured, non-structured data repositories, and here's how we're going to take visualization and distribution of data. So, that then next went into how we bring it to market? That's Data First. So, we've been working with large insurance companies, large financial services companies, retailers, gaming companies, and the net that we see is three things. First is, yes everyone agrees the platform is the right place to go. It's where do we get started? How do I take my existing investment and take advantage of this platform? And that, invariably, is I'm going to build a net new application whether it be Watson Conversations, so that runs into Watson Data Platform. We want to ingest data, but we want that data to be resident on-prem, we want it to be native to the cloud, and so we're going to work through the architectural change to adopt that. Another great example is we want to start with just an analytic application because we are already hosting with you a mobile app. Well, we're going to run it into your analytic fabric using dashDB, and dashDB works with Watson Analytics and we're going to build an application that's resident. The really creative and compelling piece here, back to your comment on IBM is, it's really hard to buy things from this company historically. Buying things from IBM is not easy, so we built a platform, we built the methodology to help you understand how to take advantage of it, and now we have a subscription, the Bluemix subscription is which you can come in and draw down those services, be it an object store, be it a sequel data store, be the visualization layer. >> John: Opposability basically. >> Yeah, but in a common governed framework. The big takeaway is, and I'll pass to Adam, governance and security and operationalizing the platform is what we can bring to bear. 'Cause we're bringing Open Source, we're bringing proprietary technologies, but if it's done independent, it doesn't really deliver on the promise of a platform. >> I will say that architecturally, that's incredibly liberating to know that there is this one common mind model. >> It's also highly requested by customers. That's what they want. >> Derek: That's what they want. It's the path to get there that I think is, we're at that intersection right now, it's crossing the chasm. >> John: So, what's liberating? Give us good-- >> Oh, just the fact that you know that if there's a common access control layer under the hood, if there's a common governance layer under the hood, that you don't have to compromise and come up with an alternative proposition for taking some capability, maybe deploying a model to a scoring engine. You can have the one purpose filled scoring engine and know that I can call that in on demand from discovery phase to go to production and I don't have to sort of engage in another separate mind conversation or separate entitlement conversation or a separate enabling conversation. This catalog is allowing it to work together. >> That to me from a team sport perspective is that the steps you have to take. So, think of ETL. ETL really in a modern real time, like getting away from batch and go into real time, that's just flow. So, the skill set and the ownership of the infrastructure associated with that is evolved, especially in cloud where that's just a dynamic where it's going to be a team deciding here's the data I want, here's how I want to enrich it, here's how I want to govern and curate it. >> It's a team sport. I love that. We were just at the Strata Hadoop. We had our big data SV event and the collision between batch and real time, they are not mutually exclusive and some people just made bets on batch and forgot real time. And they have real time people who don't do batch. So, you kind of see that coming together. >> Adam: Conversion. >> So, the question, Adam, for you is that, with the world kind of moving in that direction, how do you rationalize so the customer who's saying, "Hey, I'm cloud native but I also have a hybrid here "and I want to be cloud native purely "on this net new applications". So, there's a conversation happening. I call it the dev ops of data which is like data ops. Hey, I'm a programmer. I just want data as code. I just don't want to get in the weeds of setting up a data warehouse, and prepping an ETL, all that batch stuff that someone else does. I'm writing some software. I want data native to my app, but I don't want to go in and do the wrangling. I don't want to go out. I just want stuff to magically work. How do you tackle that premise? >> I mean, I think the dev ops of data piece is certainly a topic we're going to be hearing a lot more about over the next coming six months, in a year. I think the reason for that is precisely because this earlier topic of operationalization. You've got lots of people building up, budding data science teams and so on. And the first thing they're going to do is be working in the discovery area. They won't be in the world of pushing things to production. When they do, it's going to become more important that the folks who truly understand the details of the algorithm are close enough to the deployed assets, so that they can understand how this model is behaving over time. So that they can understand new data quality issues that might have cropped up and get close to that without obviously sort of breaking the separation duties that are important for a production system. So, I think, that is one part of the data ops conversation that hasn't yet been worked out. It's going to be a real opportunity for folks who-- >> That's an emerging area. You agree, right? >> It's a cultural shift too. I mean that is a re-thinking of, because most companies keep data in steel pipes. They're highly regulated. Their rules, the personalities that own them so to speak. The proposition that we've been on and every client asks for is how do I create a common fabric that gives access to people, that is governed and curated so you can always give a shopping experience. People that work with data do not want to talk about and say this : "How long does it take to stand up a server? "When can I get the data stood up in the staging area "so I can actually access it?" That's over. >> It's interesting, we're doing some Wikibon research on this, and this is the point where people look at value extraction of the data so they tend to, it's kind of like if you're a hammer, everything looks like a nail. So if you're in IT, it's infrastructure. If you are on the business line, it's the apps. So, you're seeing the shift where apps is value creating the value, but the infrastructure is more elastic, more compossible so it's enablement by itself so that's interesting. So, your thoughts on that, guys? Where is that value of the data coming from most, right now? Is it the apps? Is the infrastructure still evolving? The hybrid not-- >> We think there's a value model here. There is certainly elements of the data pipeline that are purely operational, reporting base and things like that, which drive value on their own. But we also recognize that it's new uses of data and new business processes that are primarily driven by applications, driven by conversational interfaces, driven by these sort of emerging paradigms. And one of our goals in the data platform is to ensure that clients can move along that curve more aggressively. >> How are people getting started with the Watson Data Platform? Do they go jumping all in? Is there a community edition, you can try it before you buy it kind of thing? >> Yeah, so you're signing up in Bluemix. You have access to a set of services around the platform. You have a 30-day window where you can try everything included within it, and then at some point you got to commit to a credit card or you got to commit a 12-month term agreement. I think in parallel, we see a lot of other companies that end up blasting in size challenge for IBM. We have a lot of clients. We have got a lot of clients that we are working with today in traditional architects and infrastructure, helping them through a methodology, helping them with the right skills. That is a more traditional, hey, come in and try an analytic workload on the platform. We'll give the skills. We'll help do the enablement and then we're off and running. I think the big difference is whether or not clients are paying for and they are willing to pay for it. 'Cause we are helping them get to this new model. We're helping them get to the platform, and I think the big thing we're working through is how do we get to velocity? I think when you look at these workloads that are happening. The reason they're happening is now data is not just in some dark corner. With AI, the machine learning is always on. So, there's a lot of different ways in which you can unleash that, that then, how do you take advantage of it? And that is a cultural shift. It's re-thinking business models, it's re-thinking how you got skills deployed which is incredibly exciting for us, and I think the market in general. I think back to how AI is cast in many cases as the robots are going to rule the world. There's a lot of good that can come from exposing vast amounts of data to AI and to frameworks where you can get a lot of value out of it. From how to better position products to how to, better design of medicines to fulfillment chains in countries that need help. >> So, guys, in the last minute that we have I want you to take a minute to either together or one of you guys talk about how IBM is helping solve what seems to be the number one question we get on the Cube where I get asked, hey, how do you help me build a hybrid architecture. I have more data-rich workloads coming on board now. Either I have some heavy data rich workloads that are run on-prem, I got more cloud action coming, I got IOT and I'm investing in data science. So, how do you guys specifically help me build a hybrid cloud architecture that's going to fuel and support data-rich workloads and propel my data science operation. >> Yeah, so, I'll take the basics for me. It is the Data First method. It is dashDB, which is an extensible on-prem hybrid in the cloud so that the common analytic fabric. There's Data Connect, which is our ability to move data batch continuous into different end states in the cloud, and then there's data science experience. So data science experience is our offering that brings together community, it brings together content, it brings together various tooling for the data scientist or data engineers. And I think the other piece of this is, we have something called solutions assurance. So we're literally designing patterns that we stand up in our own environments that reflect what we see on Premise and what we see workloads going into the cloud with, and stamping that as hybrid architectures that are repeatable, and we remove risk, the operational risk. But the reality is (mumbles) is, clients have to make sacrifices in getting to the cloud. You have to deprecate, you have to rethink. And that's where some of the smoothing of those rough edges come into the discipline of us saying, here's a supported architecture, here's the destination that you're going to, and we're going to have to work together to get there. Which is the fun part, I mean, that's what we're all in this for, is getting the outcomes. >> I think the key is not to pretend that these environments are completely identical to one another. There are things that the public cloud is uniquely well suited for. So let's make sure that those kinds of use cases are really nailed there, right? And then there are other cases where you're dealing with mainframe systems running critical business processes, and you want to be able to infuse that process with some analytics. So you have to look at the use case. Maybe it's training a machine learning model in the cloud, being able to export that model and run it-- >> So use proven solutions and be prepared to be handling new ones coming onboard. Alright, Derek Schoettle, general manager, and Adam Kocoloski, the CTO, the leaders at IBM Watson Data Group, IMB Watson Platform. This is The Cube, back with more live coverage after this short break.