>> Live from San Francisco, it's theCUBE covering the IBM Think 2019, brought to you by IBM. >> Welcome to the redone Moscone Center here in San Francisco. I'm Stu Miniman with my co-host, Dave Vellante. You're watching theCUBE's wall to wall coverage of IBM Think 2019. >> Happy to welcome back to the program, a CUBE alumni Stephanie Trunzo, who's the Global Head of IBM Cloud Garage. >> Stephanie, thanks for joining us again. >> Yeah, yeah, great to be here. >> Good to see you. >> So, you're one of the IBM boomerangs. >> So you've worked for IBM before >> Indeed, that's right. and you're back now. So tell us a little about, we've had some interviews about the IBM Cloud Garage but tell us about your role, what you are doing. >> So I was with IBM for 13 years. >> I left and started a company called Point Source. >> We were a business partner and we did a lot of work in mobile and digital transformation and I sold that company and I kind of thought, "Well what's next?" and this opportunity presented itself. >> And it's perfect because the Cloud Garage is taking a new approach to how we interact with our clients from an IBM perspective and a lot of it is very similar to what we did at Point Source which is take this digital transformation, digital agency approach to looking at business outcomes first. >> Yeah, so one of our favorite topics, you know, cause it's a buzzword for a few years but when we talked to companies, I mean it's real. >> A few years back it was like, right, >> I'm doing a mobile app. I'm doing things like that. >> Bring us inside. It's a spectrum and every company is different but tell us what digital transformation means to the costumers that you're working with and how IBM and the Cloud Garage is helping them along that journey >> Yeah >> You know it's funny that you say that. Digital transformation can feel like a buzzword, right? >> And I think it's because there's so many things that are broader than just digital about transformation. So we talk in the Cloud Garage about guided transformation as a way of helping our clients not only think about how do they take Legacy applications, how do they take a new modern approach to their technology? How do they apply digital to processes that they already have in place? >> But also think about culture, new ways of working. >> Those aren't necessarily digital topics but we think about it as a guided transformation approach, meaning, can we teach along the way? >> So we're not just helping our clients see rapid outcomes and develop MVPs but are we helping them also learn along the way? >> So clients are really looking for people to help them, coach them on making decisions, bring expertise to the table so that they also have sustainable frameworks and you know, they're skilling people up in these new modern technologies as well. >> So digital transformation, of course, it is the buzzword of the day but every CEO you talk to is trying to get digital transformation right. >> So, what do you think some of the common ways in which people are pursuing the right path of digital transformation and maybe the question is what's perhaps some of the mistakes that people are making? >> Yeah, yeah, so I think if we think about it from the side of some challenges or mistakes or you know maybe missteps that people have along the way, is probably not spending enough time focusing on users, you know, taking the time to take a real outside-in approach. What is necessary to interact with your clients differently? >> What are the new capabilities that you could be offering? But instead of just daydreaming about all of the cool stuff that technology could do, really grounding it in an understanding of what your users want, what your users need, the data that will help inform those decisions. So I think that that's one misstep, is that people get excited about new technologies and so often it's like a solution looking for a problem and so we try to help make sure that we're really identifying business outcomes and what are the things that they want to test, to learn more so it's real iterative learning. >> And I think something you said is also really important, getting it right. >> What does that mean? >> Getting it right, it's a journey, it's this evolution so I'm not sure you ever hit a stage were you say, "Ah-ha, I've done it." (laughing) But more you can identify milestones where you can learn and apply that learning to keep evolving. >> Yeah. Often when we talk to users, the long pole to tent that transformation is that application portfolio. There's some stuff that can move pretty quick and we've seen that happen in the industry but boy, there's some stuff that I shoved it into VM and I kept it running five or 10 years longer than I should. >> How are companies doing along that line? >> How do we help get, because that's one of the challenges for users is, "Ugh, I have to use this horrible application." >> Yes. >> "That just can't move this at the speed that we need it to." >> Yeah, so when I talk with clients about this, one of the things that we often discuss is that you look backwards at your legacy architectures or your systems, like, core systems that take forever to migrate and often they were architected with time, not intention, right? So one microdecision after another took place over 10, 15, 20 years and your architecture, it reflects that. So I think that Cloud offers this really unique opportunity to look at your architecture going forward with an intentional mindset. So, kind of resetting the clock on all those architectural decisions that have accrued over a time. And I think that one of the aspects of getting people moving, even on the sticky projects, is breaking it down to consumable pieces. So one of the things we do in the Cloud Garage is help our clients figure out how to identify an actionable MVP. A minimum viable product that we can show quick success against. They've got a hypothesis they need to test. Let's just take one application, let's take one work load, and let's move that and see what happens. So we're going to do that learning, we're going to test that hypothesis and that starts you down a path that's a little quicker. >> How do I engage with the IBM Cloud Garage? >> If I'm interested, how do I get started? Is it a set of services? How does it all work? >> Yes, so we have 15 locations globally so they're built for purpose, built for activity spaces around the world. You can come into one of those spaces and we can do a tour, we can do a framing workshop which helps identify business opportunities, that first piece, the first step in the journey and get you moving really quickly. >> We also will do a couple different kinds of models if one of those locations doesn't work for a client or isn't a good geographical location. We'll also do pop-up Garages where we'll go to the client and work directly onsite with them. >> We've heard a lot about how Cloud fits into a lot of the digital transformation? >> What I haven't heard as much, but I would expect IBM is doing is how AI fits into that activity. >> Absolutely. Yeah, so in fact, I kind of lump that all together, to be honest, because part of the journey is identifying, again, if you're starting from business outcomes, you're working back to the technology solution so maybe the objective is to, you're in insurance industry and you need to develop policy quotes quicker. In order to develop that solution, that might necessarily involve us figuring out how to not only get their core systems to clouds so that they can extract data faster but also get more intelligent about underwriting processes so they can get quotes out quickly. So all of those technologies come into our process almost as a subplot to the business outcome that we're trying to drive for our clients. >> How much do you get involved in helping with the data strategy specifically? I mean, we think of the innovation sandwich that is data plus machine intelligence plus Cloud for scale, how involved are you in the data strategy? >> Is that part of the initiative? >> Absolutely. In fact, I think there's a really great symbiotic relationship and we see this pattern really often where clients will come to us because they want to do some application modernization as a starting point. >> As soon as we get into that conversation, you realize you actually need to modernize your data strategy as well. So there's a cyclical relationship and either entry point ends up involving the other, so if you're modernizing your data, what are you doing with it? You're probably surfacing it in an application, now we're back into an application discussion again. So we do definitely get involved in that and in fact, we have several offerings that are specifically geared towards data and analytics. >> Stephanie, about how long is a typical engagement? >> Is there an ending point or are there follow-ups that you have to make sure you're tweaking ... >> It never ends. It never ends. Yeah. (laughing) >> So, a typical engagement is we would start with the framing workshop I mentioned to identify the business opportunity. Design thinking workshop to take that business opportunity. Take all these great big ideas that people come up with and funnel it into something that's actionable. >> So take all the big ideas then and turn it into the one that we're going to pursue. >> And then an MVP workshop where we co-create with the client so we're teaching those skills, pair programming and working directly with them and a product owner to develop an MVP, test that hypothesis. And at the end, sometimes the MVP is something that is ready to roll in to production. >> Sometimes the MVP is something that leads to a learning that produces a second MVP. >> A typical engagement, end to end, for us, is probably around three months to get that first MVP and that's a pretty rapid pace to go the whole way from, and sometimes it's just as short as three weeks. So it just depends on the scope. But to go the whole way from identifying an opportunity and to testing it and having a real results, it's pretty fast. >> Are there specific KPI's that the customer can usually have coming out of that? >> Three months. That's a great window. >> You used to think about these engagements that used to roll out. >> Three years! >> It used to be more. >> Yeah, exactly. >> Yeah, so we do look at... >> It really depends on what it is that they're trying to achieve. But we do define success criteria upfront. Those success criteria then are the things that we're testing as part of the MVP process. And so at the end, you will have actionable results. You'll have information that you've learned from as a result of developing that MVP. >> Sometimes it's something like understanding whether certain security protocols internally can be met with moving a workload in a certain way. Sometimes it's actually about user conversion. So it could be a marketing goal. >> It really depends on what they're trying to achieve. >> Where do you want to see this go? I mean, obviously, you're riding the waves. Digital transformation, AI, data. Where do you see this going over the next two to five years? >> Yeah. So I think some of the fascinating things that we've been doing and the Garage is a great place because so much innovation is happening there. Our clients are kind of testing boundaries. So we get to see a lot of the pretty far, out-there things. >> We've had projects with blockchain tracking fish in streams like a farm to table scenario but marry that with Watson image recognition so we can tell what the fish is and digitally imprint an ID on it. The sky's the limit on the kinds of things that we can come up with and build an MVP for. But I think some of the stuff that I would see in the next few years is really more around what I'll say ambient computing. We're adding additional senses, it's no longer just sight. Now we have so much voice. >> There's all of these other ways that we are interacting in context. >> And so I think we're going to keep exploring this kind of ambient notion of the things that are going on around us, whether that's data, artificial intelligence, and forming things, and then incorporating that into how technology interacts with consumers, users, et cetera. >> You're really taking the notion of digital transformation to the next level. >> That's right. >> Say, sensing. >> Exactly. >> Acting on behalf of the brand. >> That's right. >> Injecting intelligence layer- >> You got it. >> Into that all. >> Exactly. >> Nice. >> Yeah. >> Alright. Stephanie, there's tons of users here at the show. Are there customer stories that people get to hear throughout the week? >> What highlights? >> Yeah, definitely. So, we really are big on storytelling because it's the easiest way to understand these things. Some of these technologies are difficult, you know. They're intense concepts. >> So we have a lot of our clients come and share their stories onstage. There's a keynote on Thursday where we're talking about how to take an idea to MVP and we've got several clients joining us to talk about the Cloud Garage and how we actually impacted their business so, yeah. >> Alright. Well, Stephanie, we really appreciate all the updates on IBM Cloud Garage. >> Yeah, absolutely. >> Congratulations. >> Thanks for having me back! Five years. Great. >> Alright. Well, we always love to tell the stories of what's happening at all the big shows. Help extract the signal from the noise. From Dave Vallente, I'm Stu Miniman. >> We'll be right back. Thanks for watching theCube.

>> Live from Seattle, Washington it's theCUBE covering KubeCon and CloudNativeCon North America 2018 brought to you by Red Hat the Cloud Native Computing Foundation and it's Ecosystem Partners. >> Well, everyone welcome back to theCube's live coverage here in Seattle for KubeCon and CloudNativeCon 2018. I'm John Furrier with Stu Miniman. Three days of coverage around the Cloud Native growth, around the Ecosystem around open source, and the role of micro servers in the cloud. Our next guest is Roland Barcia who's the IBM Distinguished Engineer for IBM's Hybrid Cloud. Welcome to theCube. >> Thank you, glad to be here. >> Thanks for joining us. Being a Distinguished Engineer of IBM is a pretty big honor so congratulations. >> Thank you. >> it means you got technical chops so we can get down and dirty if we want to. >> Sure. >> I want to get your take on this because a lot of companies in IT are transforming and then that's been called digital transformation, it's happening and cloud has developed scale. And the wish list if you had the magic wand that could make things do better is actually happening. Supernetting's actually creating some goodness that if you had the magic wand, if I asked that question three years ago, if you had a magic wand what would an environment look like? Seamless operations around the cloud, so it's kind of happening. How are you guys positioned for this? Talk about the IBM cloud, what you're doing here, and how you see this cloud native market exploding. It's almost 8,000 people here up from 4,000 last year. >> Yeah, that's a great question I think. I work a lot with our enterprise clients. I'm part of what's called the IBM Cloud Garage, so I'm very customer facing. And often times, we're seeing that there is different paces of a journey. And so for example, I worked with a client that started building a cloud native application. They built about 60 micro services. And at the end of that, they were deploying it as one job which means they defeated the whole purpose of micro service architecture. And so what we really need to think about is an end to end journey. I think the developers are probably the more modern role in an enterprise, but we're starting to see modernization of an operations team for example, and adopting culture, and cutting down the walls of IT organizational groups into mixed squads, adopting something like a Spotify model. And I think a lot of the challenges in adopting kubernetes is really in cultural aspects and in enterprise. Does that make sense? >> Yeah. And because network guys are different than the app guys, and now they have policy knobs on kubernetes they can play with. Network guys love policy. >> Yeah, and they're fighting over ownership, right? >> Roland indeed. We look at that modernization, the application modernization really is that long home intent. And what we hear here is you need to be able to meet customers where they are. Sure, there's some stuff they're building shiny and new and have the developers, but enterprises have a lot of application and therefore there's a grand spectrum. What do you hear from customers? What's the easy part and where's the parts they're getting stuck? >> Yeah, so I think the easy part is writing the application. I think where they're getting stuck is really scaling it to the enterprise, doing the operations, doing the DevOps. I always tell people that a modernization journey might be better started by taking a certain class of applications like middleware where we have a WebSphere heritage from IBM, and saying why don't we take a look at containerizing that. We've built tools like Transformation Advisor that'll scan your WebSphere applications and tell you what do you need to change in that middleware application to make it behave well in a containerized platform. Then from there, you build your DevOps engine, your DevOps pipeline and you really start to get your operations teams going in delivering containers, delivering applications as containers. And then getting your policies and your standards in place. Then you can start opening up around innovation and start really driving towards building cloud native new applications in addition to that. >> One of those areas we've been talking about in the industry for decades is automation. The conversation's a little bit different these days. Maybe you can bring us up to speed about what's different than say it was earlier days. >> Yeah, I think IT organizations have always done a bit of automation. I think they write scripts, they automate builds. I think the mantra that I use is automate everything, right? Organizations need to really start to automate in a new way. How I deliver containers, but delivering the app is not enough. I need to automate all levels of testing in a modern way. Test driven development is big. At the IBM Cloud Garage, we have something we call the IBM Cloud Garage Method which really takes a set of practices like test driven development, pair programming, things out of lean startup, extreme programming, and really start to help enterprises adopt those practices. So I say why can't we automate end to end performance testing in the pipeline, and functional testing, and writing them early and in the beginning of projects? That way, as I'm deploying containers which are very dynamic, along with configuration, and along with policy you're testing it continuously. And I think that level of automation is what we need to get to. >> Talk about security as well 'cause security's one of those things where it's got to be baked in upfront. You got to think about it holistically. It's also now being pulled out of IT, it's more of a board function because the risk management is one hack you could get crushed. And so you got to have security. And the container there's a security boundary issue, so it's important. >> Last week we met with an insurance company. We did a workshop. And they walked us through all the compliant steps that they need to go through today. How they do it with traditional middleware and virtual machines and hardware and it was a very, what I'm going to say governance driven process. And so a lot of checks and balances, stop don't move forward, which is really the industry for developing and innovating is going the opposite way: self service and enabling. And there's a lot of risk with that. And so what we're really trying to do with technology is like Multicloud Manager, technology we have around multicluster, management is how do I do things like I want to check which clusters are Hipaa compliant and which ones are out. How do i force that policy? >> That's smart. >> Now that everything is software driven, software developed, there's an opportunity to really automate those checks. >> So your point automate everything. >> Yeah, I want to automate everything. >> Governance is a service. (laughing) >> Yeah, that's right. And actually, that can help get away from error prone human checks where they had all these tons of documents of all different policies they have to go through can now be automated in a seamless way. >> So compliance and governance could be a stumbling block or it can be just part of the software. That's what you're getting at here. >> That's right, that's what I'm getting at. I think the transition is look at it as an opportunity now that everything is software driven, use software disciplines that developers are used to in those security roles and those CSO roles, etc. >> So I want to ask you a question. So one of the things we're seeing obviously with the cloud is it's great for certain things, and then on premises it has latency issues. We saw Amazon essentially endorse this by saying RDS on VMware on premises. They announced Outpost had reinvent oh, latency. Things aren't moving into the cloud as fast. So you're going to see this hybrid environment. So hybrids, we get that, it's been around, check. No real discussion other than it's happening. The real trend is multicloud, right? >> That' right. >> And so multicloud is just a modern version of the word multi vendor about the client server days. So systems were a multi vendor man choice. This is a fundamental thing. It's not so much about multicloud as it is about choice. How do you guys see that? You are in an environment where you have a lot of customers who don't have one cloud, so this is a big upcoming trend in 2019. >> Most of our clients have at least five different clouds that they deal with, whether it be an IaaS, a PaaS, a SaaS base solution. What we're seeing as a trend is we talked about on premise and private and enterprise is I think is 80% of workloads are still in the data center. And so they want to build that private cloud environment as a transitionary point to public, but what we're seeing across the multicloud space is I'm going to say a new integration space. So if you really think 15 years ago, SOA and enterprise service bosses in a very centralized fashion, I think there's a new opportunity for integration across clouds and on-prem in a more decentralized way. So I think integration is kind of the next trend that we're seeing in this multicloud space because the new applications that we're seeing with cognitive data AI are mixing data sources from multiple clouds and on-prem and needing to control that in a hybrid control plane is key. >> It's funny, the industry always talks about these buzzwords, multicloud. If we're talkin' about multicloud, then it's a problem. The idea of infrastructure as code it's not even use the word multicloud. I mean, if you think about it, if you're programming the infrastructure and enabling the stuff under the covers, why even talk about cloud? It should be automated, so that's the future state, but in reality, that's kind of what enterprisers are tryin' to think about. >> They are, and I think it's a tension between innovation and moving fast and control, right? The enterprisers want to move fast, but they want to make sure that they don't break security protocol, that they don't break resiliency that they're maybe have used to with their existing customers and applications. I do think the challenge is how operations teams and management teams start to act like developers to get to that point. And I think that's part of the journey. >> Open source obviously a big part of this show, and that's open source, people contribute upstream It's great stuff. IBM is a big contributor, and it'll be even more when Red Hat gets into the mix. So upstream's great, but as you got 8,000 people here, you're startin' to see people talkin' about business issues, and other things. One of the downstream impacts of this conference being so open source centric is the IT equation and then just the classic developer. So you have multiple personas now kind of interacting. You got the developer, you got the IT architect, cloud architect pro whatever, and then you got the open source community members. Melting pot: good, challenges, thoughts? >> So I think it's so developers love that, right? I think from an enterprise perspective, there are issues. We're seeing a lot of our clients with our private cloud platform ask us to build out what's called air gapped environment which is how do I build up an open source style ecosystem within my enterprise. So things like getting an artifactory registry or a Docker registry or whatever type of registry where I get certified, open source packages in my enterprise that I've gone and done security vulnerability scans with, or that I've made sure that I look at every layer from the Linux kernel all the way up to whatever software is included. So what we're seeing is how do I open the aperture a bit, but do it in a more responsible fashion I think is the key. >> Yeah, and that's for stability, right? So Stu, one of things I've been talkin' about and want to get your thoughts on this role is that you got the cloud as a scalable system then one of the things that's being discussed in Silicon Valley now for the first time, we've been sitting on theCube for years, is the cloud's a system. It's just some architecture, it's network distributing, computing, art paradigm, all that computer science has been around for awhile, right? >> Yes, yes. >> So if you've been a systems person whether hardware or whatever, operating systems, you get cloud. But also you got the horizontal specialism of applications that are using machine learning and data and applications which is unique on top. So you have the collision of those two worlds. This is kind of a modern version of two worlds that we used to call systems and apps, but they're happening in a real dynamic way. What's your thoughts on this? Because you got the benefits of horizontally scalable cloud and you now have the ability to power that so we're seeing things like AI, which has been around for a long, long time, have a renaissance because now you got a lot of compute. >> That's right, and I think data is the real big challenge we're seeing with a lot of our clients. They have a lot of it in their enterprise, they don't want to unlock it all right away. We recently did what's called IBM Cloud Private for Data, in which we brought in a set of technologies around our AI, our Watson core to really start leveraging some of those tools in a private manner. And then what we're seeing is a lot of applications that are moving to the cloud have a data drag. It might start as something as simple as caching data and no SQL databases, but very quickly they want to learn a lot more about that data. So we're seeing that mix happening all the time. >> We've had it, we've had someone say in theCube ML's the new SQL. >> Yeah. >> Because you're starting to see SQL abstraction layers are a beautiful thing if they're connected. So I want to get your thoughts on this because everyone's kind of in discovery mode right now. Learning, there's a lot of education. I mean, we're talkin' about real, big time players. Architects are becoming cloud architects. Sysadmins are becoming operators for large infrastructure scale. You see network guys goin' wait a minute, if I don't get on the new network programmable model I'm going to be irrelevant. So a lot of persona changes in the enterprise. How are you guys handling that with customers? I know you guys have the expert program. Comment on that dynamic. >> I think what we're doing is we use the IBM Cloud Garage to bring in practices like the Spotify method where we start pushing things like >> What's the Spotify method? >> Spotify method is a way of doing kind of development where rather than having your disciplines of architects, development, operations, we're now splitting teams, let's say functionally, where I have mixed disciplines in a squad and maybe saying hey, the person building the account team has an SRE, an ops guy, a dev guy all within their same squad. And then maybe have guilds across disciplines, right? And so what we do at the Garage is we bring 'em in to one of the Garages. We have four team locations worldwide. Maybe do your first project. Then we build enablement and education around that, bring it back to the enterprise and start making that viral. And that's what we're doing in the IBM Cloud Garage. >> So not a monolithic thing, breakin' it down, integrating multiple disciplines, kind of like a playlist. >> Yeah, that's right. And I think the best way to do it is to practice it, right, in action. Let's pick a project rather than talking about it. >> If I had to ask you in 2019, what is the IT investment going to look like with kubernetes impact? How does kubernetes change the IT priorities and investments for an enterprise? >> Yeah, so I think you'll see kubernetes become a vehicle for enterprises to deliver content. So one, the whole area around helm and other package managers as a way to bundle software. I think as people build more clusters, multicluster management is going to be the big trend of how do I deal now with clusters that I have in public cloud and private cloud, all different clouds? And I think that integration layer that I talked about where what does modern integration look like across kubernetes based applications. >> Someone asked me last week at Reinvent hey, can't we just automate kubernetes? And then I was like, well it's kind of automated now. What's your thoughts on that? >> So I think when someone asks a question what does it mean to automate that I think the kubernetes stack really sits on top of IaaS infrastructure. And so for example, our IBM Cloud Private you can run it on zLinux or Power. And we have a lot of IBM folks that run multi architecture clusters. And therefore, they still need a level of automating how I create clusters over IaaS and there's technologies like Terraform and others that help with that, but then there's also automating standing up the DevOps stack, automating deployment of the applications over that stack. And I think they mean automating how I use kubernetes in an environment. >> So 2019, the year of programmability and automation creating goodness around kubernetes. >> Yeah, absolutely, >> Roland, thanks for comin' >> Thank you, it was great. >> on theCube, thanks for that smart insight. TheCube coverage here, day two winding down. We got day three tomorrow. This is theCube covering KubeCon and CloudNativeCon 2018. We'll be right back with more day two coverage after this short break. (happy electronic music)

(upbeat music) >> Announcer: Live from Las Vegas it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is The Cube, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host Peter Burris, Moe Abdulla is here. He's the vice president of Cloud Garage and Solution Architecture Hybrid Cloud for IBM and Tim Davis is here, Data Analytics and Cloud Architecture Group and Services Center of Excellence IBM. Gentlemen, welcome to The Cube. >> Glad to be here. >> Thanks for having us. >> Moe, Garage, Cloud Garage, I'm picturing drills and wrenches, what's the story with Garage? Bring that home for us. >> (laughs) I wish it was that type of a garage. My bill would go down for sure. No, the garage is playing on the theme of the start-up, the idea of how do you bring new ideas and innovate on them, but for the enterprises. So what two people can do with pizza and innovate, how do you bring that to a larger concept. That's what The Garage is really about. >> Alright and Tim, talk about your role. >> Yeah, I lead the data and analytics field team and so we're really focused on helping companies do digital transformation and really drive digital and analytics, data, into their businesses to get better business value, accelerate time to value. >> Awesome, so we're going to get into it. You guys both have written books. We're going to get into the Field Guide and we're going to get into the Cloud Adoption Playbook, but Peter I want you to jump in here because I know you got to run, so get your questions in and then I'll take over. >> Sure I think so obvious question number one is, one of the biggest challenges we've had in analytics over the past couple of years is we had to get really good at the infrastructure and really good at the software and really good at this and really good at that and there were a lot of pilot failures because if you succeeded at one you might not have succeeded at the other. The Garage sounds like it's time to value based. Is that the right way to think about this? And what are you guys together doing to drive time to value, facilitate adoption, and get to the changes, the outcomes that the business really wants? >> So Tim you want to start? >> Yeah I can start because Moe leads the overall Garage and within the Garage we have something called the Data First Methodology where we're really driving a direct engagement with the clients where we help them develop a data strategy because most clients when they do digital transformation or really go after data, they're taking kind of a legacy approach. They're building these big monolithic data warehouses, they're doing big master data management programs and what we're really trying to do is change the paradigm and so we connect with the Data First Methodology through the Garage to get to a data strategy that's connected to the business outcome because it's what data and analytics do you need to successfully achieve what you're trying to do as a business. A lot of this is digital transformation which means you're not only changing what you're doing from a data warehouse to a data lake, but you're also accelerating the data because now we have to get into the time domain of a customer, or your customer where they may be consuming things digitally and so they're at a website, they're moving into a bank branch, they go into a social media site, maybe they're being contacted by a fintech. You've got to retain an maintain a digital relationship and that's the key. >> And The Garage itself is really playing on the same core value of it's not the big beating the small anymore, it's the fast beating the slow and so when you think of the fast beating the slow, how do you achieve fast? You really do that by three ways. So The Garage says the first way to achieve fast is to break down the problem into smaller chunks, also known as MVPs or minimum viable product. So you take a very complex problem that people are talking and over-talking and over engineering, and you really bring it down to something that has a client value, user-centered. So bring the discipline from the business side, the operation side, the developers, and we mush them together to center that. That's one way to do fast. The second way-- >> By the way, I did, worked with a client. They started calling it minimum viable outcomes. >> Yes, minimum viable outcomes means what product and there's a lot of types of these minimum viable to achieve, we're talking about four weeks, six weeks, and so on and so forth. The story of American Airlines was taking all of their kiosk systems for example and really changing them both in terms of the types of services they can deliver, so now you can recheck your flights, et cetera, within six week periods and you really, that's fast, and doing it in one terminal and then moving to others. The second way you do fast is by understanding that the change is not just technology. The change is culture, process, and so on. So when you come to The Garage, it's not like the mechanic style garage where you are sitting in the waiting room and the mechanic is fixing your car. Not at all. You really have some sort of mechanical skills and you're in there with me. That's called pair programming. That's called test-driven, these types of techniques and methodologies are proven in the industry. So Tim will sit right next to me and we'll code together. By the time Tim goes back to his company, he's now an expert on how to do it. So fast is achieving the cultural transformation as well as this minimum viable aspect. >> Hands on, and you guys are actually learning from each in that experience, aren't you? >> Absolutely. >> Oh yeah. >> And then sharing, yeah. >> I would also say I would think that there's one more thing for both of you guys and that is increasingly as business acknowledges that data is an asset unlike traditional systems approaches where we built a siloed application, this server, that database manager, this data model, that application and then we do some integration at some point in time, when you start with this garage approach, data-centric approach, figure out how that works, now you have an asset that can be reused in a lot of new and interesting ways. Does that also factor into this from a speed aspect? >> Yeah it does. And this is a key part. We have something called data science experience now and we're really driving pilots through The Garage, through the data first method to get that rapid engagement and the goal is to do sprints, to do 12 to 20 week kind of sprints where we actually produce a business outcome that you show to the business and then you put it into production and we're actually developing algorithms and other things as we go that are part of the analytic result and that's kind of the key and behind that, you know the analytic result is really the, kind of the icing on the cake and the business value where you connect, but there's a whole foundation underneath that of data and that's why we do a data topology and the data topology has kind of replaced the data lake, replaces all that modeling because now we can have a data topology that spans on premise, private cloud, and public cloud and we can drive an integrated strategy with the governance program over that to actually support the data analytics that you're trying to drive and that's how we get at that. >> But that topology's got to tie back to the attributes of the data, right? Not the infrastructure that's associated with it. >> It does and the idea of the topology is you may have an existing warehouse. That becomes a zone in the topology, so we aren't really ripping and replacing, we're augmenting, you know, so we may augment an on premise warehouse that may sit in a relational database technology with a Hadoop environment that we can spin up in the cloud very rapidly and then the data science applications and so we can have a discovery zone as well as the traditional structured reporting and the level of data quality can be mixed. You may do analytic discovery against raw data versus where you have highly processed data where we have extreme data quality for regulatory reporting. >> Compared to a god box where everything goes through some pipe into that box. >> And you put in on later. >> Yes. >> Well and this is the, when Hadoop came out, right, people thought they were going to dump all their data into Hadoop and something beautiful was going to happen right? And what happened is everybody created a lot of data swamps out there. >> Something really ugly happened. >> Right, right, it's just a pile of data. >> Well they ended up with a cheaper data warehouse. >> But it's not because that data warehouse was structured, it has-- >> Dave: Yeah and data quality. >> All the data modeling, but all that stuff took massive amounts of time. When you just dump it into a Hadoop environment you have no structure, you have to discover the structures so we're really doing all the things we used to do with data warehousing only we're doing it in incremental, agile, faster method where you can also get access to the data all the way through it. >> Yeah that makes sense. >> You know it's not like we will serve new wine before its time, you know you can. >> Yeah, yeah, yeah, yeah. >> You know, now you can eat the grapes, you can drink the wine as it's fermenting, and you can-- >> No wrong or right, just throw it in and figure it out. >> There's an image that Tim chose that the idea of a data lake is this organized library with books, but the reality is a library with all the books dumped in the middle and go find the book that you want. >> Peter: And no Dewey Decimal. >> And, exactly. And if you want to pick on the idea that you had earlier, when you look at that type of a solution, the squad structure is changing. To solve that particular problem you no longer just have your data people on one side. You have a data person, you have the business person that's trying to distill it, you have the developer, you have the operator, so the concept of DevOps to try and synchronize between these two players is now really evolved and this is the first time you're hearing it, right at The Cube. It's the Biz Data DevOps. That's the new way we actually start to tell this. >> Dave: Explain that, explain that to us. >> Very simple. It starts with business requirements. So the business reflects the user and the consumer and they come with not just generics, they come with very specific requirements that then automatically and immediately says what are the most valuable data sources I need either from my enterprise or externally? Because the minute I understand those requirements and the persistence of those requirements, I'm now shaping the way the solution has to be implemented. Data first, not data as an afterthought. That's why we call it the data first method. The developers then, when they're building the cloud infrastructure, they really understand the type of resilience, the type of compliance, the type of meshing that you need to do and they're doing it from the outside. And because of the fact that they're dealing with data, the operation people automatically understand that they have to deal with the right to recovery and so on and so forth. So now we're having this. >> Makes sense. You're not throwing it over the wall. >> Exactly. >> That's where the DevOps piece comes in. >> And you're also understanding the velocity of data, through the enterprise as well as the gaps that you have as an enterprise because you're, when you go into a digital world you have to accumulate a lot more data and then you have to be able to match that and you have to be able to do identity resolution to get to a customer to understand all the dimensions of it. >> Well in the digital world, data is the core, so and it's interesting what you were saying Moe about essentially the line of business identifying the data sources because they're the ones who know how data affects monetization. >> Yes. >> Inder Paul Mendari, when he took over as IBM Chief Data Officer, said you must from partnerships with the line of business in order to understand how to monetize, how data contributes to the monetization and your DevOps metaphor is very important because everybody is sort of on the same page is the idea right? >> That's right. >> And there's a transformation here because we're working very close with Inder Paul's team and the emergence of a Chief Data Officer in many enterprises and we actually kind of had a program that we still have going from last year which is kind of the Chief Data Officer success program where you can help get at this because the classic IT structure has kind of started to fail because it's not data oriented, it's technology oriented, so by getting to a data oriented organization and having a elevated Chief Data Officer, you can get aligned with the line of business, really get your hands on the data and we prescribe the data topology, which is actually the back cover of that book, shows an example of one, because that's the new center of the universe. The technologies can change, this data can live on premise or in the cloud, but the topology should only change when your business changes-- (drowned out) >> This is hugely important so I want to pick up on something Ginny Rometti was talking about yesterday was incumbent disruptors. And when I heard that I'm like, come on no way. You know, instant skeptic. >> Tim: And that's what, that's what it is. >> Right and so then I started-- >> Moe: Wait, wait, discover. >> To think about it and you guys, what you're describing is how you take somebody, a company, who's been organized around human expertise and other physical assets for years, decades, maybe hundreds of years and transform them into a data oriented company-- >> Tim: Exactly. >> Where data is the core asset and human expertise is surrounding that data and learn to say look, it's not an, most data's in silos. You're busting down those silos. >> Exactly. >> And giving the prescription to do that. >> Exactly, yeah exactly. >> I think that's what Tim actually said this very, you heard us use the word re-prescriptive. You heard us use the word methodology, data first method or The Garage method and what we're really starting to see is these patterns from enterprises. You know, what works for a startup does not necessarily translate easily for an enterprise. You have to make it work in the context of the existing baggage, the existing processes, the existing culture. >> Customer expectations. >> Expectations, the scale, all of those type dimensions. So this particular notion of a prescription is we're taking the experiences from Hertz, Marriott, American Airlines, RVs, all of these clients that really have made that leap and got the value and essentially started to put it in the simple framework, seven elements to those frameworks, and that's in the adoption, yeah. >> You're talking this, right? >> Yeah. >> So we got two documents here, the Cloud Adoption Playbook, which Moe you authored, co-authored. >> Moe: With Tim's help. >> Tim as well and then this Field Guide, the IBM Data and Analytic Strategy Field Guide that Tim you also contributed to this right? >> Yeah, I wrote some of it yeah. >> Which augments the book, so I'll give you the description of it too. >> Well I love the hybrid cloud data topology in the back. >> That's an example of a topology on the back. >> So that's kind of cool. But go ahead, let's talk about these. >> So if you look at the cover of that book and piece of art, very well drawn. That's right. You will see that there are seven elements. You start to see architecture, you start to see culture and organization, you start to see methodology, you start to see all of these different components. >> Dave: Governance, management, security, emerging tech. >> That's right, that really are important in any type of transformation. And then when you look at the data piece, that's a way of taking that data and applying all of these dimensions, so when a client comes forward and says, "Look, I'm having a data challenge "in the sense of how do I transform access, "how do I share data, how to I monetize?," we start to take them through all of these dimensions and what we've been able to do is to go back to our starting comment, accelerate the transformation, sorry. >> And the real engagement that we're getting pulled into now in many cases and getting pulled right up the executive chains at these companies is data strategy because this is kind of the core, you've got to, so many companies have a business strategy, very good business strategies, but then you ask for their data strategy, they show you some kind of block diagram architecture or they show you a bunch of servers and the data center. You know, that's not a strategy. The data strategy really gets at the sources and consumption, velocity of data, and gaps in the data that you need to achieve your business outcome. And so by developing a data strategy, this opens up the patterns and the things that we talk to. So now we look at data security, we look at data management, we look at governance, we look at all the aspects of it to actually lay this out. And another thought here, the other transformation is in data warehousing, we've been doing this for the past, some of us longer than others, 20 or 30 years, right? And our whole thing then was we're going to align the silos by dumping all the data into this big data warehouse. That is really not the path to go because these things became like giant dinosaurs, big monolithic difficult to change. The data lake concept is you leave the data where it is and you establish a governance and management process over top of it and then you augment it with things like cloud, like Hadoop, like other things where we can rapidly spin up and we're taking advantage of things like object stores and advanced infrastructures and this is really where Moe and I connect with our IBM Club private platforms, with our data capabilities, because we can now put together managed solutions for some of these major enterprises and even show them the road map and that's really that road map. >> It's critical in that transformation. Last word, Moe. >> Yeah, so to me I think the exciting thing about this year, versus when we spoke last year, is the maturity curve. You asked me this last year, you said, "Moe where are we on the maturity curve of adoption?" And I think the fact that we're talking today about data strategies and so on is a reflection of how people have matured. >> Making progress. >> Earlier on, they really start to think about experimenting with ideas. We're now starting to see them access detailed deep information about approaches and methodologies to do it and the key word for us this year was not about experimentation or trial, it's about acceleration. >> Exactly. >> Because they've proven it in that garage fashion in small places, now I want to do it in the American Airlines scale, I want to do it at the global scale. >> Exactly. >> And I want, so acceleration is the key theme of what we're trying to do here. >> What a change from 15, 20 years ago when the deep data warehouse was the single version of the truth. It was like snake swallowing a basketball. >> Tim: Yeah exactly, that's a good analogy. >> And you had a handful of people who actually knew how to get in there and you had this huge asynchronous process to get insights out. Now you guys have a very important, in a year you've made a ton of progress, yea >> It's democratization of data. Everyone should, yeah. >> So guys, really exciting, I love the enthusiasm. Congratulations. A lot more work to do, a lot more companies to affect, so we'll be watching. Thank you. >> Thank you so much. >> Thank you very much. >> And make sure you read our book. (Tim laughs) >> Yeah definitely, read these books. >> They'll be a quiz after. >> Cloud Adoption Playbook and IBM Data and Analytic Strategy Field Guide. Where can you get these? I presume on your website? >> On Amazon, you can get these on Amazon. >> Oh you get them on Amazon, great. Okay, good. >> Thank you very much. >> Thanks guys, appreciate it. >> Alright, thank you. >> Keep it right there everybody, this is The Cube. We're live from IBM Think 2018 and we'll be right back. (upbeat electronic music)