>>Good evening everyone and welcome back to Sparkly Sin City, Las Vegas, Nevada, where we are here with the cube covering AWS Reinvent for the 10th year in a row. John Furrier has been here for all 10. John, we are in our last session of day one. How does it compare? >>I just graduated high school 10 years ago. It's exciting to be, here's been a long time. We've gotten a lot older. My >>Got your brain is complex. You've been a lot in there. So fast. >>Graduated eight in high school. You know how it's No. All good. This is what's going on. This next segment, wrapping up day one, which is like the the kickoff. The Mondays great year. I mean Tuesdays coming tomorrow big days. The announcements are all around the kind of next gen and you're starting to see partnering and integration is a huge part of this next wave cuz API's at the cloud, next gen cloud's gonna be deep engineering integration and you're gonna start to see business relationships and business transformation scale a horizontally, not only across applications but companies. This has been going on for a while, covering it. This next segment is gonna be one of those things that we're gonna look at as something that's gonna happen more and more on >>Yeah, I think so. It's what we've been talking about all day. Without further ado, I would like to welcome our very exciting guest for this final segment, trust from single store. Thank you for being here. And we also have him on from IBM Data and ai. Y'all are partners. Been partners for about a year. I'm gonna go out on a limb only because their legacy and suspect that a few people, a few more people might know what IBM does versus what a single store does. So why don't you just give us a little bit of background so everybody knows what's going on. >>Yeah, so single store is a relational database. It's a foundational relational systems, but the thing that we do the best is what we call us realtime analytics. So we have these systems that are legacy, which which do operations or analytics. And if you wanted to bring them together, like most of the applications want to, it's really a big hassle. You have to build an ETL pipeline, you'd have to duplicate the data. It's really faulty systems all over the place and you won't get the insights really quickly. Single store is trying to solve that problem elegantly by having an architecture that brings both operational and analytics in one place. >>Brilliant. >>You guys had a big funding now expanding men. Sequel, single store databases, 46 billion again, databases. We've been saying this in the queue for 12 years have been great and recently not one database will rule the world. We know that. That's, everyone knows that databases, data code, cloud scale, this is the convergence now of all that coming together where data, this reinvent is the theme. Everyone will be talking about end to end data, new kinds of specialized services, faster performance, new kinds of application development. This is the big part of why you guys are working together. Explain the relationship, how you guys are partnering and engineering together. >>Yeah, absolutely. I think so ibm, right? I think we are mainly into hybrid cloud and ai and one of the things we are looking at is expanding our ecosystem, right? Because we have gaps and as opposed to building everything organically, we want to partner with the likes of single store, which have unique capabilities that complement what we have. Because at the end of the day, customers are looking for an end to end solution that's also business problems. And they are very good at real time data analytics and hit staff, right? Because we have transactional databases, analytical databases, data lakes, but head staff is a gap that we currently have. And by partnering with them we can essentially address the needs of our customers and also what we plan to do is try to integrate our products and solutions with that so that when we can deliver a solution to our customers, >>This is why I was saying earlier, I think this is a a tell sign of what's coming from a lot of use cases where people are partnering right now you got the clouds, a bunch of building blocks. If you put it together yourself, you can build a durable system, very stable if you want out of the box solution, you can get that pre-built, but you really can't optimize. It breaks, you gotta replace it. High level engineering systems together is a little bit different, not just buying something out of the box. You guys are working together. This is kind of an end to end dynamic that we're gonna hear a lot more about at reinvent from the CEO ofs. But you guys are doing it across companies, not just with aws. Can you guys share this new engineering business model use case? Do you agree with what I'm saying? Do you think that's No, exactly. Do you think John's crazy, crazy? I mean I all discourse, you got out of the box, engineer it yourself, but then now you're, when people do joint engineering project, right? They're different. Yeah, >>Yeah. No, I mean, you know, I think our partnership is a, is a testament to what you just said, right? When you think about how to achieve realtime insights, the data comes into the system and, and the customers and new applications want insights as soon as the data comes into the system. So what we have done is basically build an architecture that enables that we have our own storage and query engine indexing, et cetera. And so we've innovated in our indexing in our database engine, but we wanna go further than that. We wanna be able to exploit the innovation that's happening at ibm. A very good example is, for instance, we have a native connector with Cognos, their BI dashboards right? To reason data very natively. So we build a hyper efficient system that moves the data very efficiently. A very other good example is embedded ai. >>So IBM of course has built AI chip and they have basically advanced quite a bit into the embedded ai, custom ai. So what we have done is, is as a true marriage between the engineering teams here, we make sure that the data in single store can natively exploit that kind of goodness. So we have taken their libraries. So if you have have data in single store, like let's imagine if you have Twitter data, if you wanna do sentiment analysis, you don't have to move the data out model, drain the model outside, et cetera. We just have the pre-built embedded AI libraries already. So it's a, it's a pure engineering manage there that kind of opens up a lot more insights than just simple analytics and >>Cost by the way too. Moving data around >>Another big theme. Yeah. >>And latency and speed is everything about single store and you know, it couldn't have happened without this kind of a partnership. >>So you've been at IBM for almost two decades, don't look it, but at nearly 17 years in how has, and maybe it hasn't, so feel free to educate us. How has, how has IBM's approach to AI and ML evolved as well as looking to involve partnerships in the ecosystem as a, as a collaborative raise the water level together force? >>Yeah, absolutely. So I think when we initially started ai, right? I think we are, if you recollect Watson was the forefront of ai. We started the whole journey. I think our focus was more on end solutions, both horizontal and vertical. Watson Health, which is more vertically focused. We were also looking at Watson Assistant and Watson Discovery, which were more horizontally focused. I think it it, that whole strategy of the world period of time. Now we are trying to be more open. For example, this whole embedable AI that CICE was talking about. Yeah, it's essentially making the guts of our AI libraries, making them available for partners and ISVs to build their own applications and solutions. We've been using it historically within our own products the past few years, but now we are making it available. So that, how >>Big of a shift is that? Do, do you think we're seeing a more open and collaborative ecosystem in the space in general? >>Absolutely. Because I mean if you think about it, in my opinion, everybody is moving towards AI and that's the future. And you have two option. Either you build it on your own, which is gonna require significant amount of time, effort, investment, research, or you partner with the likes of ibm, which has been doing it for a while, right? And it has the ability to scale to the requirements of all the enterprises and partners. So you have that option and some companies are picking to do it on their own, but I believe that there's a huge amount of opportunity where people are looking to partner and source what's already available as opposed to investing from the scratch >>Classic buy versus build analysis for them to figure out, yeah, to get into the game >>And, and, and why reinvent the wheel when we're all trying to do things at, at not just scale but orders of magnitude faster and and more efficiently than we were before. It, it makes sense to share, but it's, it is, it does feel like a bit of a shift almost paradigm shift in, in the culture of competition versus how we're gonna creatively solve these problems. There's room for a lot of players here, I think. And yeah, it's, I don't >>Know, it's really, I wanted to ask if you don't mind me jumping in on that. So, okay, I get that people buy a bill I'm gonna use existing or build my own. The decision point on that is, to your point about the path of getting the path of AI is do I have the core competency skills, gap's a big issue. So, okay, the cube, if you had ai, we'd take it cuz we don't have any AI engineers around yet to build out on all the linguistic data we have. So we might use your ai but I might say this to then and we want to have a core competency. How do companies get that core competency going while using and partnering with, with ai? What you guys, what do you guys see as a way for them to get going? Because I think some people probably want to have core competency of >>Ai. Yeah, so I think, again, I think I, I wanna distinguish between a solution which requires core competency. You need expertise on the use case and you need expertise on your industry vertical and your customers versus the foundational components of ai, which are like, which are agnostic to the core competency, right? Because you take the foundational piece and then you further train it and define it for your specific use case. So we are not saying that we are experts in all the industry verticals. What we are good at is like foundational components, which is what we wanna provide. Got it. >>Yeah, that's the hard deep yes. Heavy lift. >>Yeah. And I can, I can give a color to that question from our perspective, right? When we think about what is our core competency, it's about databases, right? But there's a, some biotic relationship between data and ai, you know, they sort of like really move each other, right? You >>Need, they kind of can't have one without the other. You can, >>Right? And so the, the question is how do we make sure that we expand that, that that relationship where our customers can operationalize their AI applications closer to the data, not move the data somewhere else and do the modeling and then training somewhere else and dealing with multiple systems, et cetera. And this is where this kind of a cross engineering relationship helps. >>Awesome. Awesome. Great. And then I think companies are gonna want to have that baseline foundation and then start hiring in learning. It's like driving the car. You get the keys when you're ready to go. >>Yeah, >>Yeah. Think I'll give you a simple example, right? >>I want that turnkey lifestyle. We all do. Yeah, >>Yeah. Let me, let me just give you a quick analogy, right? For example, you can, you can basically make the engines and the car on your own or you can source the engine and you can make the car. So it's, it's basically an option that you can decide. The same thing with airplanes as well, right? Whether you wanna make the whole thing or whether you wanna source from someone who is already good at doing that piece, right? So that's, >>Or even create a new alloy for that matter. I mean you can take it all the way down in that analogy, >>Right? Is there a structural change and how companies are laying out their architecture in this modern era as we start to see this next let gen cloud emerge, teams, security teams becoming much more focused data teams. Its building into the DevOps into the developer pipeline, seeing that trend. What do you guys see in the modern data stack kind of evolution? Is there a data solutions architect coming? Do they exist yet? Is that what we're gonna see? Is it data as code automation? How do you guys see this landscape of the evolving persona? >>I mean if you look at the modern data stack as it is defined today, it is too detailed, it's too OSes and there are way too many layers, right? There are at least five different layers. You gotta have like a storage you replicate to do real time insights and then there's a query layer, visualization and then ai, right? So you have too many ETL pipelines in between, too many services, too many choke points, too many failures, >>Right? Etl, that's the dirty three letter word. >>Say no to ETL >>Adam Celeste, that's his quote, not mine. We hear that. >>Yeah. I mean there are different names to it. They don't call it etl, we call it replication, whatnot. But the point is hassle >>Data is getting more hassle. More >>Hassle. Yeah. The data is ultimately getting replicated in the modern data stack, right? And that's kind of one of our thesis at single store, which is that you'd have to converge not hyper specialize and conversation and convergence is possible in certain areas, right? When you think about operational analytics as two different aspects of the data pipeline, it is possible to bring them together. And we have done it, we have a lot of proof points to it, our customer stories speak to it and that is one area of convergence. We need to see more of it. The relationship with IBM is sort of another step of convergence wherein the, the final phases, the operation analytics is coming together and can we take analytics visualization with reports and dashboards and AI together. This is where Cognos and embedded AI comes into together, right? So we believe in single store, which is really conversions >>One single path. >>A shocking, a shocking tie >>Back there. So, so obviously, you know one of the things we love to joke about in the cube cuz we like to goof on the old enterprise is they solve complexity by adding more complexity. That's old. Old thinking. The new thinking is put it under the covers, abstract the way the complexities and make it easier. That's right. So how do you guys see that? Because this end to end story is not getting less complicated. It's actually, I believe increasing and complication complexity. However there's opportunities doing >>It >>More faster to put it under the covers or put it under the hood. What do you guys think about the how, how this new complexity gets managed or in this new data world we're gonna be coming in? >>Yeah, so I think you're absolutely right. It's the world is becoming more complex, technology is becoming more complex and I think there is a real need and it's not just from coming from us, it's also coming from the customers to simplify things. So our approach around AI is exactly that because we are essentially providing libraries, just like you have Python libraries, there are libraries now you have AI libraries that you can go infuse and embed deeply within applications and solutions. So it becomes integrated and simplistic for the customer point of view. From a user point of view, it's, it's very simple to consume, right? So that's what we are doing and I think single store is doing that with data, simplifying data and we are trying to do that with the rest of the portfolio, specifically ai. >>It's no wonder there's a lot of synergy between the two companies. John, do you think they're ready for the Instagram >>Challenge? Yes, they're ready. Uhoh >>Think they're ready. So we're doing a bit of a challenge. A little 32nd off the cuff. What's the most important takeaway? This could be your, think of it as your thought leadership sound bite from AWS >>2023 on Instagram reel. I'm scrolling. That's the Instagram, it's >>Your moment to stand out. Yeah, exactly. Stress. You look like you're ready to rock. Let's go for it. You've got that smile, I'm gonna let you go. Oh >>Goodness. You know, there is, there's this quote from astrophysics, space moves matter, a matter tells space how to curve. They have that kind of a relationship. I see the same between AI and data, right? They need to move together. And so AI is possible only with right data and, and data is meaningless without good insights through ai. They really have that kind of relationship and you would see a lot more of that happening in the future. The future of data and AI are combined and that's gonna happen. Accelerate a lot faster. >>Sures, well done. Wow. Thank you. I am very impressed. It's tough hacks to follow. You ready for it though? Let's go. Absolutely. >>Yeah. So just, just to add what is said, right, I think there's a quote from Rob Thomas, one of our leaders at ibm. There's no AI without ia. Essentially there's no AI without information architecture, which essentially data. But I wanna add one more thing. There's a lot of buzz around ai. I mean we are talking about simplicity here. AI in my opinion is three things and three things only. Either you use AI to predict future for forecasting, use AI to automate things. It could be simple, mundane task, it would be complex tasks depending on how exactly you want to use it. And third is to optimize. So predict, automate, optimize. Anything else is buzz. >>Okay. >>Brilliantly said. Honestly, I think you both probably hit the 32nd time mark that we gave you there. And the enthusiasm loved your hunger on that. You were born ready for that kind of pitch. I think they both nailed it for the, >>They nailed it. Nailed it. Well done. >>I I think that about sums it up for us. One last closing note and opportunity for you. You have a V 8.0 product coming out soon, December 13th if I'm not mistaken. You wanna give us a quick 15 second preview of that? >>Super excited about this. This is one of the, one of our major releases. So we are evolving the system on multiple dimensions on enterprise and governance and programmability. So there are certain features that some of our customers are aware of. We have made huge performance gains in our JSON access. We made it easy for people to consume, blossom on OnPrem and hybrid architectures. There are multiple other things that we're gonna put out on, on our site. So it's coming out on December 13th. It's, it's a major next phase of our >>System. And real quick, wasm is the web assembly moment. Correct. And the new >>About, we have pioneers in that we, we be wasm inside the engine. So you could run complex modules that are written in, could be C, could be rushed, could be Python. Instead of writing the the sequel and SQL as a store procedure, you could now run those modules inside. I >>Wanted to get that out there because at coupon we covered that >>Savannah Bay hot topic. Like, >>Like a blanket. We covered it like a blanket. >>Wow. >>On that glowing note, Dre, thank you so much for being here with us on the show. We hope to have both single store and IBM back on plenty more times in the future. Thank all of you for tuning in to our coverage here from Las Vegas in Nevada at AWS Reinvent 2022 with John Furrier. My name is Savannah Peterson. You're watching the Cube, the leader in high tech coverage. We'll see you tomorrow.

(soft electronic music) >> Welcome to this CUBE Virtual Conversation. I'm your host, Rebecca Knight. Today, I'm joined by Hermanth Manda. He is the Executive Director, IBM Data and AI, responsible for Cloud Pak for Data. Thanks so much for coming on the show, Hermanth. >> Thank you, Rebecca. >> So we're talking now about the release of Cloud Pak for Data version 3.5. I want to explore it for, from a lot of different angles, but do you want to just talk a little bit about why it is unique in the marketplace, in particular, accelerating innovation, reducing costs, and reducing complexity? >> Absolutely, Rebecca. I mean, this is something very unique from an IBM perspective. Frankly speaking, this is unique in the marketplace because what we are doing is we are bringing together all of our data and AI capabilities into a single offering, single platform. And we have continued, as I said, we made it run on any cloud. So we are giving customers the flexibility. So it's innovation across multiple fronts. It's still in consolidation. It's, in doing automation and infusing collaboration and also having customers to basically modernize to the cloud-native world and pick their own cloud which is what we are seeing in the market today. So I would say this is a unique across multiple fronts. >> When we talk about any new platform, one of the big concerns is always around internal skills and maintenance tasks. What changes are you introducing with version 3.5 that does, that help clients be more flexible and sort of streamline their tasks? >> Yeah, it's an interesting question. We are doing a lot of things with respect to 3.5, the latest release. Number one, we are simplifying the management of the platform, made it a lot simpler. We are infusing a lot of automation into it. We are embracing the concept of operators that are not open shelf has introduced into the market. So simple things such as provisioning, installation, upgrades, scaling it up and down, autopilot management. So all of that is taken care of as part of the latest release. Also, what we are doing is we are making the collaboration and user onboarding very easy to drive self service and use the productivity. So overall, this helps, basically, reduce the cost for our customers. >> One of the things that's so striking is the speed of the innovation. I mean, you've only been in the marketplace for two and a half years. This is already version 3.5. Can you talk a little bit about, about sort of the, the innovation that it takes to do this? >> Absolutely. You're right, we've been in the market for slightly over two and a half years, 3.5's our ninth release. So frankly speaking, for any company, or even for startups doing nine releases in 2.5 years is unheard of, and definitely unheard of at IBM. So we are acting and behaving like a startup while addressing the go to market, and the reach of IBM. So I would say that we are doing a lot here. And as I said before, we're trying to address the unique needs of the market, the need to modernize to the cloud-native architectures to move to the cloud also while addressing the needs of our existing customers, because there are two things we are trying to focus, here. First of all, make sure that we have a modern platform across the different capabilities in data and AI, that's number one. Number two is also how do we modernize our existing install base. We have six plus billion dollar business for data and AI across significant real estates. We're providing a platform through Cloud Pak for Data to those existing install base and existing customers to more nice, too. >> I want to talk about how you are addressing the needs of customers, but I want to delve into something you said earlier, and that is that you are behaving like a startup. How do you make sure that your employees have that kind of mindset that, that kind of experimental innovative, creative, resourceful mindset, particularly at a more mature company like IBM? What kinds of skills do you try to instill and cultivate in your, in your team? >> That's a very interesting question, Rebecca. I think there's no single answer, I would say. It starts with listening to the customers, trying to pay detailed attention to what's happening in the market. How competent is it reacting. Looking at the startups, themselves. What we did uniquely, that I didn't touch upon earlier is that we are also building an open ecosystem here, so we position ourselves as an open platform. Yes, there's a lot of IBM unique technology here, but we also are leveraging open source. We are, we have an ecosystem of 50 plus third party ISVs. So by doing that, we are able to drive a lot more innovation and a lot faster because when you are trying to do everything by yourself, it's a bit challenging. But when you're part of an open ecosystem, infusing open source and third party, it becomes a lot easier. In terms of culture, I just want to highlight one thing. I think we are making it a point to emphasize speed over being perfect, progress over perfection. And that, I think, that is something net new for IBM because at IBM, we pride ourselves in quality, scalability, trying to be perfect on day one. I think we didn't do that in this particular case. Initially, when we launched our offense two and a half years back, we tried to be quick to the market. Our time to market was prioritized over being perfect. But now that is not the case anymore, right? I think we will make sure we are exponentially better and those things are addressed for the past two and one-half years. >> Well, perfect is the enemy of the good, as we know. One of the things that your customers demand is flexibility when building with machine learning pipeline. What have you done to improve IBM machine learning tools on this platform? >> So there's a lot of things we've done. Number one, I want to emphasize our building AI, the initial problem that most of our customers concerned about, but in my opinion, that's 10% of the problem. Actually deploying those AI models or managing them and covering them at scales for the enterprise is a bigger challenge. So what we have is very unique. We have the end-to-end AI lifecycle, we have tools for all the way from building, deploying, managing, governing these models. Second is we are introducing net new capabilities as part of a latest release. We have this call or this new service called WMLA, Watson Machine Learning Accelerator that addresses the unique challenges of deep learning capabilities, managing GPUs, et cetera. We are also making the auto AI capabilities a lot more robust. And finally, we are introducing a net new concept called Federator Learning that allows you to build AI across distributed datasets, which is very unique. I'm not aware of any other vendor doing this, so you can actually have your data distributed across multiple clouds, and you can build an aggregated AI model without actually looking at the data that is spread across these clouds. And this concept, in my opinion, is going to get a lot more traction as we move forward. >> One of the things that IBM has always been proud of is the way it partners with ISVs and other vendors. Can you talk about how you work with your partners and foster this ecosystem of third-party capabilities that integrate into the platform? >> Yes, it's always a challenge. I mean, for this to be a platform, as I said before, you need to be open and you need to build an ecosystem. And so we made that a priority since day one and we have 53 third party ISVs, today. It's a chicken and egg problem, Rebecca, because you need to obviously showcase success and make it a priority for your partners to onboard and work with you closely. So, we obviously invest, we co-invest with our partners and we take them to market. We have different models. We have a tactical relationship with some of our third party ISVs. We also have a strategic relationship. So we partner with them depending on their ability to partner with us and we go invest and make sure that we are not only integrating them technically, but also we are integrating with them from a go-to-market perspective. >> I wonder if you can talk a little bit about the current environment that we're in. Of course, we're all living through a global health emergency in the form of the COVID-19 pandemic. So much of the knowledge work is being done from home. It is being done remotely. Teams are working asynchronously over different kinds of digital platforms. How have you seen these changes affect the team, your team at IBM, what kinds of new kinds of capabilities, collaborations, what kinds of skills have you seen your team have to gain and have to gain quite quickly in this environment? >> Absolutely. I think historically, IBM had quite a, quite a portion of our workforce working remotely so we are used to this, but not at the scale that the current situation has compelled us to. So we made a lot more investments earlier this year in digital technologies, whether it is Zoom and WebEx or trying to use tools, digital tools that helps us coordinate and collaborate effectively. So part of it is technical, right? Part of it is also a cultural shift. And that came all the way from our CEO in terms of making sure that we have the necessary processes in place to ensure that our employees are not in getting burnt out, that they're being productive and effective. And so a combination of what I would say, technical investments, plus process and leadership initiatives helped us essentially embrace the changes that we've seen, today. >> And I want you to close us out, here. Talk a little bit about the future, both for Cloud Pak for Data, but also for the companies and clients that you work for. What do you see in the next 12 to 24 months changing in the term, in terms of how we have re-imagined the future of work. I know you said this was already version nine. You've only been in the marketplace for, for not even three years. That's incredible innovation and speed. Talk a little bit about changes you see coming down the pike. >> So I think everything that we have done is going to get amplified and accelerated as we move forward, shift to cloud, embracing AI, adopting AI into business processes to automate and amplify new business models, collaboration, to a certain extent, consolidation of the different offerings into platforms. So all of this, we, I obviously see that being accelerated and that acceleration will continue as we move forward. And the real challenge I see with our customers and all the enterprises is, I see them in two buckets. There's one bucket which are resisting change, like to stick to the old concepts, and there's one bucket of enterprises who are embracing the change and moving forward, and actually get accelerating this transformation and change. I think it will be successful over the next one to five years. You know, it could be under the other bucket and if you're not, I think it's, you're going to get, you're going to miss out and that is getting amplified and accelerated, as we speak. >> So for those ones in the bucket that are resistant to the change, how do you get them onboard? I mean, this is classic change management that they teach at business schools around the world. But what are some advice that you would have to those who are resisting the change? >> So, and again, frankly speaking, we, at IBM, are going through that transition so I can speak from experience. >> Rebecca: You're drinking the Kool-Aid. >> Yeah, when, when I think, one way to address this is basically take one step at a time, like as opposed to completely revolutionizing the way you do your business. You can transform your business one step at a time while keeping the end objective as your goal, as your end goal. So, and it just want a little highlight that with full factor, that's exactly what we are enabling because what we do is we enable you to actually run anywhere you like. So if most of your systems, most of your data and your models, and analytics are on-premise, you can actually start your journey there while you plan for the future of a public cloud or a managed service. So my advice is pretty simple. You start the journey, but you can take, you can, you don't need to, you don't need to do it as a big bang. You, it could be a journey, it could be a gradual transformation, but you need to start the journey today. If you don't, you're going to miss out. >> Baby steps. Hey Hermanth Manda, thank you so much for joining us for this Virtual CUBE Conversation >> Thank you very much, Rebecca. >> I'm Rebecca Knight, stay tuned for more of theCUBE Virtual. (soft electronic music)

>> Live from Time Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI. (theCUBE theme music) Brought to you by IBM. >> Hello everybody, welcome back to theCUBE's special presentation. We're covering IBM's announcement. Changing the Game, Winning with AI is the theme of IBM. And IBM has these customer meet-ups, analyst meet-ups, partner meet-ups and they do this in conjunction with Strata every year. And theCUBE has been there covering 'em. I'm Dave Vellante with us is James Wade, who's the Director of Application Hosting at Guidewell, and Hemanth Manda, who's the Director of Platform Offerings at IBM. Gentlemen, welcome to theCUBE thanks for coming on. >> Thank you. >> Hemanth, let's start with you. Platform offerings. A lot of platforms inside of IBM. What do you mean platform offerings? Which one are you responsible for? >> Yeah, so IBM's data and analytics portfolio is pretty wide. It's close to six billion dollar business. And we have hundred plus products. What we are trying to do, is we're trying to basically build a platform through IBM Cloud Private for Data. Bring capabilities that cuts across our portfolio and build upon it. We also make it open. Support multiple clouds and support other partners who wants to run on the platform. So that's what I'm leading. >> Okay, great and we'll come back and talk about that. But James, tell us more about Guidewell. Where are you guys based? What'd you do and what's your role? >> Guidewell is the largest insurer in the sate of Florida. We have about six and a half million members. We also do about 38, 39% of the government processing for MediCare, MediCaid claims. Very large payer. We've also recently moved in over the provider space. We actually have clinics throughout the state of Florida where our members can go in and actually get services there. So we're actually morphing as a company, away from just an insurance company, really to a healthcare company. Very exciting time to be there. We've doubled in size in the last six years from a six billion dollar company to a, I mean from an eight billion dollar company to an 18 billion dollar company. >> So both health insurer and provider, bringing those two worlds together. And the thinking there is just more efficient, you'd be able to drive efficiencies obviously out of your business, right? >> Yup, yes. I mean, the ultimate goal for us is just to have better health outcomes for our members. And the way you deliver that is, one, you do the insurance right, you do it well. You make sure that their processed and handled properly, that they're getting all the services that they need. But two, from a provider space, how do you take the information that you have about your members and use them in a provider space to make sure they're getting the right prescriptions at the right time, for the right situations that they're having, whatever's going on in their life. >> And keeping cost down. I mean, there's a lot of finger pointing in the industry. If you bring those two areas together, you know, now they got a single throat to choke, >> That's right, we get that too. (laughing) >> Buck stops with you. Okay, and you're responsible for the entire application portfolio across the insurance and the clinical side? >> Yes, I have, you know, be it both sides, we have Guidewell as the holding company, we have multiple companies underneath it. So all of those companies roll up into a single kind of IT infrastructure. And I manage that for them, for the entire company. >> Okay. Talk about the big drivers in you business. Obviously on the insurance side, it's the claims system is the life blood, the agency system to deal with, the channel. And now of course, you've got the clinical thing to worry about, but so, talk about sort of the drivers of your business and what's changing. >> Right, I mean, the biggest change we've had, obviously in last few years, has been the Affordable Care Act. It changed the way that, you know, from a group policy where if you're a big corporation and you work for a big corporation, that company actually buys insurance for you and provides it to their employees. Well now the individual market has grown significantly. We're still a group policy insurance company, don't get me wrong, we have a great portfolio of companies that we work with, but we also now sell directly to individuals. So they're in the consumer space directly. And that's just a different way of interacting with folks. You have to have sales sites. You have to have websites that are up, where folks can come and browse your products. You have to interface with government websites. Like CMS has their site where they set up and you're able to buy products through that. So it's really changed our marketing and sales channels completely. And on the back side, the volume of growth, I mean, with the new individual insurance market we've grown in size significantly in our number of members. And that's really stressed our IT systems, it's stressed our database environment. And it's really stressed our ability to kind of analyze the thing that we're doing. And make sure that we're processing claims efficiently and making sure that the members are getting what they expect from us. So, the velocity and change in size has really stressed us. >> Yeah, so you got the Affordable Care Act and some uncertainties around that, the regulations around that. You've got things like EMR and meaningful use that you got to worry about. So a lot of complexity in the application portfolio. And Hemanth, I imagine this is not a unique discussion that you have with some of your insurance clients and healthcare folks, although, you guys are a little different in that you're bringing those two worlds together. But your thoughts on what you're seeing the marketplace. >> Yeah, so I mean, this is not unique because the data is exploding and there are multiple data sources spread across multiple clouds. So in terms of trying to get a sense of where the data is, how to actually start leveraging it, how to govern it, how to analyze it, is a problem that is across all industry verticals. And especially as we are going through digital transformation right, trying to leverage and monetize your data becomes even more important. So. >> Yeah, so, well let's talk a little bit about the data. So your data, like a lot of companies, you must have a lot of data silos. And we have said on theCUBE a lot, that the innovation engine in the future is data. Applying machine intelligence to that data. Using cloud models, whether that cloud is in a private cloud or a public cloud or now even at the edge. But having a cloud-like experience for scale and agility is critical. So, that seems to be the innovation, whereas, last 20, 30 years the innovation has been you know kind of Moore's Law and being able to get the latest and greatest systems, so I can get data out of my data warehouse faster. So change in the innovation engine driven by data what are you seeing James? >> I mean, absolutely. Again, we go back to the mission of the company. It's to provide better health outcomes for our members, right. And IT, and using the data that we collect more effectively and efficiently, allows us to do that. I mean we, if you take, you know, across the board, you may have four or five doctors that you're working with and they've prescribed multiple things to you, but they're not talking. They have no idea what your other doctor is doing with you, unless you tell 'em and a lot of people forget. So just as an example, we would know as the payer, what you've been prescribed, what you've been using for multiple years. If we see something, using AI, machine learning, that you've just been prescribed is going to have a detrimental impact to something else that you're doing, we can alert you. We can send you SMS messages, we can send you emails, we could alert your doctors. Just to say, hey this could be a problem and it could cause a prescription collision and you can end up in the hospital or worse. And that's just one example of the things that we look at everyday to try to better the outcome for our members. But, you know, that's just the first layer. What else can you do with that? Are there predictive medicines? Are there things we could alert your doctors to, that we're seeing from other places, or populations, that kind of match, you know, your current, you know, kind of what you look like, what you do, what you think, what you're using. All the information we have about you, can we predict health outcomes down the future and let your doctors know? So, exciting time to be in this industry. >> Let's talk about the application architecture to support that outcome, because you know, you're not starting from a green field. You probably got some Cobalt running and it works, you can't mess with that stuff. And traditionally you built, especially in a regulated industry, you're building applications that are hardened. And as I said you have this data silo that really, you know, it's like, it works, don't touch it. How much of a challenge is it for you to enter this sort of new era? And how are you getting there? I'd like to understand, IBM's role as well. >> Well we, it's very challenging, number one. You have your, I don't want to call it legacy 'cause that makes it sound bad, but you do have kind of your legacy environments where we're collecting the information. It's kind of like the silos that have gathered the information, the sales information, the claims information, that type of stuff. But those may not be the best systems currently, to actually do the processing and the data analysis and having the machine learning run against it. So we have, you know, really complex ETL, you know, moving data from our kind of legacy environments in to these newer open source models that you guys support with, you know, IBM Cloud Private for Data. But basically, moving into these open source areas where we can kind of focus our tools on it and learn from that data. So that, you know, having your legacy environment and moving it to the new environment where you can do this processing, has been a challenge. I mean the velocity of change in the new environment, the types of databases that are out there Hadoop and then the products that you guys have that run through the information, that's one of the bigger challenges that we have. Our company is very supportive of IT, they give us plenty of budget, they give us plenty of resources. But even with all of the support that we get, the velocity of change in the new environment, in the AI space and the machine learning, is very difficult to keep up with. >> Yeah and you can't just stop doing what your doing in the existing environment, you still got to make changes to it. You got regulatory, you got hippo stuff that you've got to deal with. So you can't just freeze your code there. So, are things like containers and, you know, cloud native techniques coming into play? >> Absolutely, absolutely. We're developing all, you know, we kind of drew a line in the sand, our CIO about two years ago, line in the sand, everything that we develop now is in our cloud-first strategy. That doesn't necessarily mean it's going to go into the external cloud. We have an internal cloud that we have. And we have a very large power environment at Guidewell. Our mainframe is still sort of a cloud-like infrastructure. So, we developed it to be cloud native, cloud-first. And then if it, you know, more than likely stays in our four walls, but there's also the option that we can move it out. Move it to various clouds that are out there. As an IBM Cloud, Amazon, Microsoft, Google, any of those clouds. So we're developing with a cloud-first strategy all of the new things. Now, like you said, the legacy side, we have to maintain. I mean, still the majority of our business is processing claims for our members, right, and that's still in that kind of legacy environment. Runs on a mainframe in the power environment today. So we have to keep it up and running as well. >> How large of organization are you, head count wise? >> We have about 2,100 IT people at Guidewell. Probably a 17,000 person organization. So there is a significant percentage of the population of our employees that are IT directly. >> I was at a, right 'cause it is a IT heavy business, always has been. I was at a conference recently and they threw out a stat that the average organization has eight clouds. And I said, "we're like a 60 person company "and we have eight clouds." I mean you must have 8,000 clouds. (laughing) Imagine when you through in the SAS and so forth. But, you mentioned a number of other clouds. You mentioned IBM Cloud and some others. So, it's a multi-cloud world. >> Yes, yes. >> Okay, so I'm interested in how IBM is approaching that, right. You're not just saying, okay, IBM Cloud or nothing, I think, you know. And cloud is defined on-prem, off-prem, maybe now at the edge, your thoughts. >> Yeah, so, absolutely, I think that is our strategy. We would like to support all the clouds out there, we don't want to discriminate one versus the other. We do have our own public cloud, but what our strategy is, to support our products and platforms on any cloud. For example, IBM Cloud Private for Data, it can run in the data center, it can provide the benefits of the cloud within your firewall. But if you want to deploy it on any other public cloud infrastructures, such as Amazon or Red Hat OpenStack, we do support it. We are also looking to expand that support to Microsoft and Google in the future. So we are going forward with the multi-cloud strategy. Also, if you look at IBM's strength, right, we have significant on-premise business, right, that's our strength. So we want to basically start with enterprise-out. So by focusing on private cloud, and making sure that customers can actually move their offerings and products to private cloud, we are essentially providing a path for our customers and clients to move cloud, embrace cloud. So that's been our approach. >> So James, I'm interested in how you guys look at cloud-first. When you say cloud-first, first of all, I'm hearing, it's not about where it goes, it's about the experience. So we're going to bring the cloud model to the data, wherever the data lives. It's in the public cloud, of course it's cloud. If we bring it on-prem, we want a cloud-like experience. How do you guys looks at that cloud-like experience? Is it utility pricing, is it defined in sort of agility terms? Maybe you could elaborate. >> Actually, we're trying to go with the agility piece first, right. The hardest thing right now is to keep up with the pace that customers demand. I mean, you know, my boss Paul Stallings always talks about, you know, consumer-grade is now the industrial strength. Now you go home at night, your network at home is very fast to your PC. Your phone, you just hit an app, you always expect it to work. Well, we have to be able to provide that same level of support and reliability in the applications we're deploying inside of our infrastructure. So, to do that, you have to be fast, you have to be agile. And our cloud-first being, how do you get things to market faster, right. So you can build service faster build out your networks faster and build you databases faster. Already have like defined sizes, click a button and it's there. On-demand infrastructure, much like they do in the public loud, We want to have that internally. But second, and our finance department would tell you, is that, you know, most important is the utility piece. So once you can define these individuals modules that you can hit a button and immediately spin up and instantiate, you should be able to figure out what that cost the company. How do you define what a server cost? Total cost of ownership through the lifetime that server is for the company. Because if we can lower thar cost, if we can do these things very well, automate 'em, get the data where it needs to be, spin up quickly, we can reduce our administrative cost and then pass those savings right back to our members. You know, if we can find a way to save your grandmother $20 a month off her health insurance, that can make a lot of difference in a person's life, right. Just by cutting our cost on the IT side, we can deliver savings back to the company. And that's very key to us. >> And in terms of sort of what goes where, I guess it's a function of the physics, right, if there's latencies involved, the economics, which you mentioned are critical obviously in your business. And I guess the laws, you know, the edicts of the government-- >> Yes and the various contracts that you sign with companies. I mean, there's some companies that we deal with it in the state of Florida that want their data to stay in that sate of Florida. Well if you move it out to a various cloud provider, you don't know which data center that it's in. So you have to go, there's the laws and regulations based on your contracts. But you're exactly right. It's what have you signed up for, what've you agreed to, what are your member comfortable with as to where the data can actually go? >> How does IBM help Guidewell and other companies sort of mange through that complexity? >> Yeah, absolutely. So I think, in addition to what James mentioned, right, it's also about agility. Because for example, if you look at insurance applications, there's a specific time period where you probably would expect 10x of load, right. So you should be able to easily scale up and down. And also, as you're changing your business model, if you have new laws, or if you want to go after new businesses, you should be able to easily embrace that, right. So cloud provides sort of flexibility and elasticity and also the agility. So that's one. The other thing that you mentioned around regulation, especially in healthcare and also too with financial services industry. So what we're trying to do is, on our platform, we would like to actually have industry-specific accelerators. We've been working with fortune 500 companies for the last 30, 40 years. So we've gained a depth of knowledge that we currently have within our company. So we want to basically start exposing the accelerators. And this is on our roadmap and will be available fairly quickly. So that's one approach we're taking. The other approach we're taking is, we're also working with our business partners and technology partners because we do believe, in today's world, you cannot go after an opportunity all by yourself. You need to build an ecosystem and that's what we're doing. We're trying to work with, basically, specialty vendors who might be focused on that particular vertical, who can bring the depth in knowledge that we might not be having. And work with them and team up, so that they can build their solutions on top of the platform. So that's another approach that we're taking. >> So I got to ask you, I always ask this question of customers. Why IBM? >> I mean, this, you guys have been a part of our business for so long. You have very detailed sales guys that are embed really with our IT folks. You understand our systems. You understand what we do, when we do it, why we do it. You understand our business cycle. IBM really invests in their customers and understanding what they're doing, what they need to be done. And quite honestly, you guys bring some ideas to the table we haven't even thought of. You have such a breadth of understanding, and you're dealing with so many other companies, you'll see things out there that could be a nugget that we could use. And IBM's never shied of bringing that to us. Just a history and a legacy of really bringing innovative solutions to us to really help our business. And very companies out there really get to know a company's business, as well as IBM does. >> Hemanth I'll give you the last word. We got Change the Game, Winning with AI tonight You go to IBM.com/winwithAI and register there. I just did, I'm part of the analyst program. So, Hemanth, last word for you. >> Yeah, so, I think the world is changing really fast and unless enterprises embrace cloud and embrace artificial intelligence and cloud base their data to monetize new business models, it very hard to compete. Like, digital transformation is impacting every industry vertical, including IBM. So, I think going after this opportunistically is critical. And IBM Cloud Private for Data, the platform provides this. And please join us today, it's going to be a great event. And I look forward to meeting you guys, thank you. >> Awesome, and definitely agree. It's all about your digital meets data, applying machine intelligence, machine learning, AI, to that data. Being able to run it in a cloud-like model so you can scale, you can be fast. That's the innovation sandwich for the future. It's not just about the speed of the processor, or the size of the disk drive, or the flash or whatever is. It's really about that combination. theCUBE bringing you all the intelligence we can find. You're watching CUBE NYC. We'll be right back right after this short break. (theCUBE theme music)