>> [Announcer] Live from Los Angeles, it's theCUBE! Covering E3, 2018. Brought to you by SiliconANGLE Media. >> Hey, welcome back everybody. Jeff right here at theCUBE. We're at the L.A. Convention Center in E3. It's our first time coming to this convention. It's 68,000 people and every single hall and outside, inside hotels. It's pretty crazy--pretty crazy scene. We're happy to be here. Well, we've got our next guest. She's been coming for a while. It's Katie Stone Perez. She's the director of Mixer Interactive. From Mixer, Katie, great to see you. >> Thanks so much for having me! >> Absolutely. So before we jump into it, I'd love to get your perspective. You've been in this industry for-- >> 17 years. >> 17 years. I wasn't going to say that. I was going to say close to two decades. >> (Laughing) >> So as you've been in and watched this thing develop, what are your impressions today in 2018 and how it's transformed?-- >> Of the show? You know, the whole game industry has so fundamentally transformed over the last 17 years, right? I mean, at that point in time, we didn't even have services like Xbox Live where people were connecting and playing online together. Everything was really sold as a disc-based media. So you walked into a store to purchase your disk. Now we have so many digital purchases happening online. We had no player data. We had no way to actually know how far in the game our players were getting and all of this kind of stuff-- >> [Jeff] That's right. You just shipped the disc out, right? You didn't know. >> And now we have all of this telemetry, right? We have all of these experiences. You have the, you know, free-to-play has made a huge rise. We have mobile, right? Mobile gaming within the space. So the show has so transformed both from the people who are playing within the space, the technologies that people are using, and the growth. I mean, we can also just see-- years ago, it was really much more about a trade show so that the big people who are going to buy the disc can actually come to E3-- >> [Jeff] Right, right. >> Check out our games and place their disc orders. And now it's really much more of a consumer phenomenon as well. >> [Jeff] So I'm curious, we covered a ton of tech shows. Just I've been here before and data and the use of data is a huge part of the digital transformation story. >> Yeah. >> So I'm curious from your point of view from a game developer point of view, how did that change? Because you guys are a little bit ahead of the curve in getting the usage data, getting the tracking data. How did that impact the industry in the way you developed and shipped games? >> It's phenomenal. You know, all of a sudden, you can start to understand who your players are and so if you're gonna do an upsell offer, you know, you can understand, like, "Oh, this person has actually already purchased this type of material." So I'm gonna give him this type of upsell vs this type of upsell. Or, You know, "I see all of my players are really struggling on level three and no one is making it through. What's wrong with level three?" Let's look at changing that up a bit. >> [Jeff] Right. >> So data has actually really informed us in so many ways to re-look at our basic gameplay loops. Our retention mechanics and all of that kind of stuff and, you know, most game companies now have teams of data analysts who are just specifically focusing on those KPIs and just analyzing the data and learning. >> [Jeff] Right. >> But with that too, we've also then had to get more agile in our development and publishing processes because, you know, when you ship a disc and you just let it go, you can get data but then what are you gonna do about it, right? >> [Jeff] Right, right. >> Your next sequel is a couple of years out and so now, too, with the ability to push updates over the air and all of this kind of stuff, It changes it so we can actually take that information, have an immediate impact, and sometimes you can get that data within one or two days. Actually have an impact, you know? >> [Jeff] Right. >> So I actually work on mixer which is a game broadcasting platform so we have a live service. So we can just constantly update and make these changes. >> [Jeff] I'm gonna ask you a philosophical question that I'm always thinking about. In terms of difficulty and the right amount of difficulty, and just kind of generically but engage specifically-- >> Right. >> You want to be difficult enough so people feel challenged and want to continue the journey. >> Yeah. >> But obviously you can't make it so difficult that they just couldn't get through. So I just wondered if you had some-- >> Yeah! >> If there's some best practice or philosophy about what's the right level to the degree of difficulty? >> Yeah, you know funny enough, I gave a talk at GDC in, like, 2005 and it was called Let Me Win and so my background is actually in psychology and it was really as someone who has a psychology background who loves to play games. My issues of playing through so many games in our media because we're a very defeatist mentality. If you think about it, we started as an industry as this coin-op industry where we had to kill you off because we needed you to put another quarter in the machine. But now we carry that trope with us even though we have people put 60 quarters-- $60 worth of quarters in the machine in advance >> [Jeff] Right, right. >> But we're still killing you off in the same way. And so it's kind of crazy to me. And so we really as an industry, I do think, need to think about that more. Now there's certain games like Cuphead is one of my favorite games but it's really brutally hard but that was very much the intention, you know? >> [Jeff] Right. >> These dark souls and the cupheads in those games. Their genre is that they are super hard-- >> [Jeff] Right. >> So people kind of know that going into them. But I do think across our broader audience, we need to think about how we're being more inclusive in our design And that's everything from, you know, still giving people that harder experience but also an educational principal called scaffolding. So, you know, just like when you're teaching a kid to do something, you're not gonna say "Okay, do this and this and this and this and this." Because that's not fun. >> [Jeff] Right, right. >> So instead, if you can be, like, "Here's what the goal is. Here's your tools." And then within the game, we want to help do that. Now with data, actually, we can help scaffold better. Cause we can actually see "Oh, these players didn't do this" Or "This age group of players didn't do this." Or "This type of thing didn't do this." So we can actually use that to inform our decisions and actually do better scaffolding within the game. >> [Jeff] Okay, so before we get to mixer and streaming which is like the latest thing, I want to get to this middle step which was the Cloud. And really opening up the ability to do multi-player games, opening up the ability to go from just that consul out into the universe and play lots of other people. Again, how did that really transform the way you guys thought about designing and delivering games? >> I mean, fundamentally, you know, Xbox Live was a apart of our program. Very early on, Live came into the Xbox business and I think it was actually great because we had that as a Microsoft asset and strength that we can bring over that type of infrastructure. And we've seen it really just connect and bring people together in form community, right? And it's so much fun. There's some element that you get when you're sitting next to someone and playing but not everyone in the world has someone sitting next to them. >> [Jeff] Right. >> So we're doing that over Live by bringing people together and through different platforms and services like Mixer as well where we can bring these communities together. >> [Jeff] Right. >> So it's really, I really think about creating that essence of community. It just makes everything more fun. >> [Jeff] Right. So now we're in 2018 and actually, it's been going on for a little while which is a whole different level of community and that's streaming where someone's playing a game for those that aren't familiar and other people are invited to participate with them. >> Yeah. >> Again, another huge shift in the way that people interacting with the game. And more importantly, kind of the social aspects around their playing with the game. >> Yeah and that's what's so cool. So in traditional game streaming platforms too, there's quite a bit of latency so what the gamer-- the streamer's actually doing at the time, you know, by the time the viewers end up seeing it on a platform, and then, you know, they can comment on it and then the streamer kind of sees it. There's a lot of latency there. So Mixer was actually created by two young kids who actually were huge in the Minecraft community. They had already created a million dollar business actually hosting Minecraft servers and they had all these streamer friends that were Minecraft streamers and they were talking about how frustrating it was because they were streaming and people were like "Put the block over here, put the block over there." But by the time they saw that feedback from their fans, they had already moved on. They had already done something different. So Mixer created low latency streaming. So what we called our faster-than-light technology where we have sub-second latency. So exactly what's happening in the game, that's what people on Mixer are seeing. And then they can comment and the streamer immediately sees those comments and that then paved the way for this richer conversation. And from there, we had interactivity come about. So we have all of our new Mixplay experiences where people can actually come on to Mixer and not just watch. Now they are playing themselves. So you can actually be playing one of our games like Next Up Hero and I can actually choose to help heal you or I can choose to help throw in enemies. Then you'll see my gamer text "Sweets" go right across the screen, right? You can actually see as a gamer who's then broadcasting, you can see what I'm doing on Mixer and how that's having an impact within your game. >> Didn't the streamer kind of like the latency so that they had time to kind of split their attention between playing the game and interacting with the community? >> No because it's all->> streamers for them, It's all about community. Now there are certain competitive sports events and things like that that we do within the e-sports space, and so there might be certain instances in which you don't want to have low latency engaged. But for the most part, streamers want to be having that conversation and are faster- >> than-light technology on Mixer really enables that for them. >> [Jeff] Right. And it just seems like it's almost gonna come full circle so if I'm engaging with the streamer and I'm participating in the game to some degree, at some point, do I just step in and we're playing the game together? >> Yeah. I mean, really now, you can play on Mixer. That's really what we're talking about with our new Mixplay experiences. So we even have games that are playable only on Mixers so these games aren't even-- we were talking about distribution, right? These games aren't even shipping. There's no disc. They're not even shipping on any of these other platforms. They're playable only on Mixer and so you can actually go to mixer.com today and check out several of these game experiences and you can actually look for Mixplay experiences. We have filters and so you can actually find all of that content. >> [Jeff] Alright. So to get your perspective before we let you->> you've been at this for a while. So as storage and compute and networking, it gets infinite in scale and asymptotically approaches zero in cost. As you look forward, where do you see leveraging some of this new horsepower? >> Well, I think again, you know, Microsoft actually just had this amazing acquisition of PlayFab technology and I love seeing what they're doing within this space and bringing that into our portfolio of content as well. Because again, it's about having this data and being able to really respond and change your game instantly to really make sure that you're doing the best things for your business. And so it really just makes developers be informed and be able to be much more agile in their approach. And it's also democratizing that opportunity. Previously years ago, to get some of these insights, you would have had to be one of the largest game companies on the planet. And now with the democratization of these different game engines, and then then the democratization of this type of tooling and online services that are available, with things like Azure and things like PlayFab, it really creates an amazing opportunity for all developers everywhere. >> [Jeff] And to me, the democratization, the thing where you're over and over-- >> Yeah. >> More of data, more of the tools, and more of the ability to do something about it is distributed to a broader audience. Alright Katie, well thank you for-- >> We get more voices with that, right? >> Right, right. >> You get a much broader set of content that ends up like the content that you see here today is much more diverse and much broader. You know, we still have a long way to go as an industry but it's very different than my first E3 17 years ago. >> [Jeff] 17 years ago. Alright Katie, well thanks for taking-- >> Thank you! >> a few minutes out of your day and congrats on all the success. >> Thanks! >> Alright, this is Katie and I'm Jeff. You're watch theCUBE from E3, L.A. Convention Center. Thanks for watching. (upbeat, techno music)

>> 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.