>>from around the globe. It's the >>cube >>With digital coverage of IBM think 2021 brought to you by IBM. Welcome back everyone to the cube coverage of IBM Think 2021. I'm john for a host of the cube got a great guest here scott heaven or vice president of marketing at IBM for data and AI cube. Alumni has been around the wave around data, had many conversations over the years scott. Welcome back to the Cuban, I wish we were in person but we're remote for the virtual conference for think 2021. Thanks for coming on >>john great to be here. And yeah, I guess we have adapted to the world of being on the screen. >>Well, great, great to have you in. One of the things about virtualization of media is that we get more content this year. There's so many more signature stories around um, IBM think and one of the things that's really fun for us is the data conversations in a I as as the transformation and innovation equations are coming together at scale. You're seeing an accelerated piece here. My first question for you is this digital shift that's going on? The preferences are shifting to virtual now digital in the wake of Covid, what do companies need to adapt from your perspective as you see this playing out? What's your perspective? >>It's interesting to use that term. So we've been calling it the great digital shift. And uh yeah, there's an there was an interesting survey, a pretty big survey of global C suite that Mackenzie did. And they pointed out that 79% of those leaders felt that Covid highlighted the immaturity of their digital capability. And while they thought they were on the right path and they were building strong digital capabilities, the whole world of the pandemic remote work, how you engage with customers call centers going, you know, off the hooks in terms of people calling, it just goes on and on and on. And And they also pointed out that 90, I think it was 96 of them are going to speed their digital reinvention. And you mentioned data, if you think about it, it's data that a few fuels digital capabilities. Right? What good is digital if it's not data? Right? It's all data. So it's the fuel that makes it all work. And when you think about the ability to leverage all your dad, you got to democratize it, it's siloed all over the place, it's growing at six times rate over the next three years. It's really all over the place, every touch point across the digital ecosystem. Um and the only way to deal with the data in to unlock its value, particularly in predictive ways is to Ai Right? And so what we're seeing is a huge amount of investment in multi cloud, really bringing together this notion of hybrid and then applying AI as the intelligence to create a more predictable and resilient business right through a digital model, right? Yeah, it's really the investment is really going through the roof. You >>know, I think AI has been, it's been demystified over the years, been a lot of people saw the machine learning and now you got NLP and data control planes that are making it more addressable. But the real thing that comes up here, I think this year is this role between business and consumer and AI has that kind of dynamic. And I want to ask you because I was just having a conversation with one of your partner, IBM partner Samsung, KC Joy runs E V P E V P for the B to B B to G Group at Samsung. It's a huge I. O. T. Thing. And AI is a big part of that consumer and we talked about the consumer electronics business issues, how is A I different for business versus the consumer is obviously an industrial iot edge and you've got automation piece. What's the difference? I mean, someone asked you that between business and consumer AI. >>Yeah, actually, I think that's one of the areas that we really differentiate ourselves and we're putting the bulk of investments, this notion of AI for business, Right? And you know, a lot of people think of A I sometimes they think of Siri and Alexa and things that go on in your car and all that. Obviously that's a big part of applying machine learning and all that, but when we talk about AI for business, we're thinking about four core attributes. Uh One is that it needs to understand the unique language of your business and industry, right? And that's not just natural language but it's the ability to debate, it's the ability to read documents, interpret documents. Um It's the ability to really understand the context because you and I can ask the same question five or six different ways and it needs to understand the business to be able to interpret that and help answer the question unlike like Siri or Alexa where you really got to have the right semantics and you know, it won't understand the nuances as well, so understand the language of businesses. 12 is that we believe ai is the engine for automation. Um So Ai is really about automating workflows and experiences because anything that you want to automate and make more productive you have to have some predictive capabilities to it to understand what to do and you have to learn about you know, what's trying to be accomplished which is always unique and personalized. So that's the second one is about automation. The third is it is about driving trust and outcomes right in the business outcomes, which means, you know, if you were to, if some a model say scott go jump off a bridge, you know, I probably wouldn't want to do that unless it really explained to me, prove instantly that I should do that and they will but explain ability and trust is such a critical part of aI for business and then finally it needs to run everywhere. It has to integrate everything. And we believe unlike a lot of the competitors where you have to bring the data to a I we're saying leave the data where it lives and bring ai to the data so it runs anywhere from the data center to the edge. The same model, the same capabilities in a distributed environment. Um So those four kind of attributes come together to what we call A I for business. Um And that's what's gonna allow call centers and supply chains and business planning and risk and regulatory, you know, mitigation. I mean those kind of things to really come to life in a predictive way without those attributes, it's much harder to do a lot more coding and you're not gonna as much accuracy. >>Yeah, I mean what you're just walking through there is interesting and if you think about consumer, okay yeah, Alexa, go get me, you know, what's the weather like in Palo alto or whatever, you know, those kinds of all back in pretty complicated but it's not as complicated as moving data to the edge and moving computer around. And the complexity of dealing with data has always been an open discussion but now with ai such at the center point of the value pressure and becoming table stakes. I mean we're hearing companies say if you don't have an Ai innovation strategy you're going to be you know irrelevant or even delisted from the stock market. That's some radical views. But um talk about this complexity and how it's being tamed for customers because if you don't have the data exposed, you're only as good as the data that you have. And this has been a conversation we've had on the cube many times before with you and some of your peers here at IBM you can't get the data. What good is it? The insights are only as good as what you can program. So this means that date is gonna be accessible and it's also complexity to move it around. So can you unpack that equation? >>Yeah, it's the whole notion of garbage in garbage out and ai you know ai its lifeblood is data and we have equipped that we always say that there's no Ai without an I. A. An information architecture And we are well over 30,000 engagements um among our clients around A I you know we have the AI ladder which is a prescriptive approach. We've learned a ton over the years and and we said before, you know the great digital shift, well the great inhibitor is the complexity of all this data and the average large enterprise has over 1000 repositories and sources of data as things go out into the edge that's just multiply. Um there's more and more movement to put applications, you know software as a service applications on the cloud and most businesses have multiple clouds so you're further fragmenting all the data and if you look at what the gardener has said and many others, these big data projects in the past are very slow and costly and they've had limited impact. This idea of moving data replicating data. It's just not going to work as the explosion of data increases in terms of touch points in terms of types and in terms of pure velocity and also at the same time the value of data, it's lifespan is rapidly decreasing. A customer record that was created yesterday may not be as valuable a year from now or even in three months from now because things change so much. Right. >>Alright. Alright. So I gotta ask you the question then because this is kind of from a customer. What's in it for me? At the end of the day I got data problem. You take it you got my attention. Um I gotta move date. I got to edge Hybrid cloud has been defined as a bona fide. A done deal is hybrid multi clouds around the corner. But that's just a subsystem of the operating system that's business now. So Hybrid cloud is the operating model data. Supercritical. What does IBM offer? What can you offer me as a customer and why is it good you guys got some announcements with cloud pack for data specifically here? Think what's the solution? How do I solve this? What's IBM offering? >>Yeah. So I think it starts with the fact that we have a fully unified data and AI platform meaning that they're not separate thoughts. They're all unified together as one on life cycle. And it runs anywhere on any cloud data center. To the answer starts with that notion and it helps you collect, organize and analyze data and infuse ai um throughout the business. Now, when it comes to the data complexity three core principles that were put into the next version of call Pat for data, one is automation is inevitable. It's the only way to deal with all this complexity. Uh leave the data where it is, where it lives, where it thrives and bring ai to the data. And so what we are putting into the next generation of compact for data is an intelligent data fabric, right? That is fueled by A. I. And that is going to abstract a lot of the complexity out of all this. Let you keep the data where it's at and be able to discover that data intelligently, be able to catalogue it, be able to understand it right? And more importantly, to do unified queries and updates across all these distributed sources of data and bring the records together without having to take weeks and months to build new data pipelines and across that entire ecosystem, be able to enforce universal privacy and usage policies which is absolutely critical. Forrester estimates that 50 of data is not used because they're afraid that it's gonna break policy. Oh >>yeah, I mean that's a huge trust issue. I mean I I was talking to a practitioner and he's like you know, we don't even want to do some of these transactions that are interesting experiments and and cloud opportunities because of the compliance risk, they're afraid to get sued. Yeah, >>that's right. And each one of those data stores just think about the ecosystem we're talking about here of sources and consumers, data consumers, ai consumers and of course all the sources that are silent all over the place. A lot of these repositories and a lot of these different cloud violence have different policies in terms of usage and in privacy. Right? So how do you bring all that together? What we're delivering the next version of compact? Her dad is a universal privacy plane if you will, which called auto privacy and it will basically abstract all the complexity of the different policies allow you to create them and enforce it universally. And you couldn't imagine the productivity of being to deliver that versus having a hand deal with this in a manual way. Yeah, that's an example with the data fabric. You know, what's interesting >>is you're getting at these. I mean I'm hearing the conversation about the solution, it's okay. I'm not in mind going okay, what's the benefits? I hear, I hear uh speed, um I hear, you know, ease of use, compliance trust, but what you're really getting at is agility and there's a, there's a upside for agility that's moving fast and getting taking advantage of new opportunities or automating something away. But you mentioned trust peace because you know, that's where I see people afraid like, okay, if I move too fast, will I trip on over or some governance issue? Like that's a huge thing. This is a big problem. >>It's a massive problem. I mean, I think there's four, Four areas from a business perspective, right? One is think about digital experiences and we know that six and 10 customers that defect from a brand because of some bad experience usually don't return. And it's estimated that is costing the industry, you know, close to $500 billion responsive experiences, which is You have to bring the data together to be able to do that, right? The second is the regulatory and reputational risk. Um that's another 180 billion or so. Which in many cases eight of revenue just to mitigate all that risk of using data. Not only regulatory but reputational. This thing about lost productivity, how many, how many hours every week is a worker doing mundane tasks, low value work because it's not automated. Um That's like another 100 or so billion dollars of costs for enterprises um can go on with interact with planning and forecasting. Um Supply chains being inefficient. All this is being fueled by the data, right? So the more you can bring all this data together, unify it, create new views that are aggregate and nature and uncover hidden insights that you couldn't do before. Um That's the magic sauce here. Right. >>Well my last question for you on the on this product before we wrap up is there's a huge trend towards ecosystem network effect integration. Right there more more integration. People are partnering. I mean you have solutions where that rely on different people in the supply chain or value chain of a of a solution whether you're a concession at a ballpark or an enterprise you're connecting with other a piece. This is cloud, right? How does your cloud pack for data handle that integration and that trust? Because this is really the deployment scenario. Your thoughts? >>Yeah. I mean I think the core of top after data is it's going to greatly enhance productivity. It's going to lower costs of these, you know, complex data states. It's going to lower risk of all this and it's going to help you uncover hidden insights that you couldn't see before. Not only because of A I, but because when you unify the data to get more out of it, we then go on to really point out that it's a truly open platform with an open ecosystem. So we are partnering with all the cloud partners. Right. We have a vast network of software providers that can extend and intimacy customized the platform. We have Integrator partners and it's all based on open source communities. So it is fully extensible and customizable to unique needs of every customer on any cloud yuan or across the city college. All >>right, scott. That's great stuff. Thanks for coming on the cube. Great to see you scott, Wapner. Vice President Marketing at IBM for data. And they are the hottest area. Great. Great cube alumni. Great insight. Thanks scott for coming on. Thank you. Okay, I'm jennifer with the cube You're watching ibn think 2021 coverage. Thanks for watching. Yeah. >>Mm

>>from around the >>globe. It's >>the cube >>with digital >>coverage of IBM >>Think 2021 >>brought to you by IBM. Welcome back everyone to the cube coverage of IBM Think 2021. I'm john for a host of the cube. Got a great guest here scott, senior Vice president of marketing at IBM for data and ai cube alumni has been around the wave around data, had many conversations over the years. Scott welcome back to the Cuban, I wish we were in person but we're remote for the virtual conference for think 2021. Thanks for coming on >>john great to be here. And yeah, I guess we have adapted to the world of being on the screen. >>Well, great, great to have you in. One of the things about virtualization of media is that we get more content this year. There's so many more signature stories around um, IBM think and one of the things that's really fun for us is the data conversations in A I as as the transformation and innovation equations are coming together at scale, you're seeing an accelerated piece here. My first question for you is, you know, this digital shift that's going on, the preferences are shifting to virtual now digital in the wake of Covid, what do companies need to adapt from your perspective as you see this playing out? What's your perspective? >>It's interesting to use that term. So we've been calling it the great digital shift. And uh yeah, there's an there was an interesting survey, a pretty big survey of global C suite that Mackenzie did. And they pointed out that 79% of those leaders felt that Covid highlighted the immaturity of their digital capability. And while they thought they were on the right path and they were building strong digital capabilities, the whole world of the pandemic remote work, how you engage with customers call centers going off the hooks in terms of people calling, it just goes on and on and on. And And they also pointed out that 90, I think it was, 96 of them are going to speed their digital reinvention. And you mentioned data, if you think about it, it's data that a few fuels digital capabilities. Right? What good is digital if it's not data, right, It's all data. So it's the fuel that makes it all work. And when you think about the ability to leverage all your dad, you got to democratize it, It's siloed all over the place, it's growing at six times rate over the next three years. It's really all over the place, every touch point across the digital ecosystem. Um, and the only way to deal with the data in to unlock its value, particularly in predictive ways is to AI. Right. And so what we're seeing is a huge amount of investment in multi cloud, really bringing together this notion of hybrid and then applying AI as the intelligence to create a more predictable and resilient business right through a digital model, right? Yeah, it's really the investment is really going through the roof. >>You know, I think AI has been, it's been demystified over the years, been a lot of people saw the machine learning and now you got NLP and data control planes that are making it more addressable. But the real thing that comes up here, I think this year is this role between business and consumer and AI has that kind of dynamic. And I want to ask you because I was just having a conversation with one of your partner, IBM partner Samsung, KC Joy runs E V P E V P for the B to B B to G Group at Samsung. It's a huge I. O. T. Thing. And AI is a big part of that consumer and we talked about the consumer electronics business issues, how is A I different for business versus the consumer is obviously got industrial iot edge and you got automation piece, what's the difference? And someone asked you that between business and consumer Ai. >>Yeah, actually I think that's one of the areas that we really differentiate ourselves and we're putting the bulk of investments this notion of AI for business, right? And you know, a lot of people think of A I sometimes they think of Siri and Alexa and things that go on in your car and all that. Obviously that's a big part of applying machine learning and all that, but when we talk about AI for business, we're thinking about four core attributes. Uh one is that it needs to understand the unique language of your business and industry, right? And that's not just natural language but it's the ability to debate, it's the ability to read documents, interpret documents. Um It's the ability to really understand the context because you and I can ask the same question in five or six different ways and it needs to understand the business to be able to interpret that and help answer the question unlike like Siri or Alexa, where you really got to have the right semantics and you know, it won't understand the nuances as well, so understand the language of businesses. 12 is that we believe ai is the engine for automation. Um So Ai is really about automating workflows and experiences because anything that you want to automate and make more productive, you have to have some predictive capabilities to it to understand what to do and you have to learn about you know, what's trying to be accomplished which is always unique and personalized. So that's the second one is about automation. The third is it is about driving trust and outcomes right? In the business outcomes, which means, you know, if you were to if some a model say scott go jump off a bridge, you know I probably wouldn't want to do that unless it really explained to me convincingly that I should do that well but explain ability and trust is such a critical part of aI for business and then finally it needs to run everywhere. It has to integrate everything. And we believe unlike a lot of the competitors where you have to bring the data to a I we're saying leave the data where it lives and bring ai to the data. So it runs anywhere from the data center to the edge, The same model, the same capabilities in a distributed environment. Um So those four kind of attributes come together to what we call a I for business. Um And that's what's gonna allow call centers and supply chains and business planning and risk and regulatory, you know, mitigation, I mean those kind of things to really come to life in a predictive way without those attributes, it's much harder to do a lot more coding and you're not gonna as much accuracy. >>Yeah, I mean what you're just walking through there is interesting and if you think about consumer, okay, yeah, Alexa, go get me, you know, what's the weather like in Palo alto or whatever, you know, those kinds of all back in pretty complicated but it's not as complicated as moving data to the edge and moving compute around. And the complexity of dealing with data has always been an open discussion. But now with ai such at the center point of the value pressure and becoming table stakes. I mean we're hearing companies say if you don't have an Ai innovation strategy, you're going to be you know, irrelevant or even delisted from the stock market. That's some radical views. But um talk about this complexity and how it's being tamed for customers because if you don't have the data exposed, you're only as good as the data that you have and this has been a conversation we've had on the cube many times before with you and some of your other peers here at IBM you can't get the data. What good is it? The insights are only as good as what you can program. So this means that date is gonna be accessible and it's also complexity to move it around. So can you unpack that equation? >>Yeah, it's the whole notion of garbage in garbage out and ai you know ai its lifeblood is data and we have equipped that we always say that there's no Ai without an I. A. An information architecture And we are well over 30,000 engagements um among our clients around A I you know we have the AI ladder which is a prescriptive approach. We've learned a ton over the years and and we said before, you know the great digital shift, well the great inhibitor is the complexity of all this data and the average large enterprise has over 1000 repositories and sources of data as things go out into the edge that's just multiply. Um there's more and more movement to put applications, you know software as a service applications on the cloud and most businesses have multiple clouds so you're further fragmenting all the data and if you look at what the gardener has said and many others, these big data projects in the past are very slow, costly and they've had limited impact. This idea of moving data replicating data. It's just not going to work as the explosion of data increases in terms of touch points in terms of types and in terms of pure velocity and also at the same time the value of data, it's lifespan is rapidly decreasing. A customer record that was created yesterday may not be as valuable a year from now or even in three months from now because things change so much. Right. >>Alright. So I gotta ask you the question then because this is kind of from a customer. What's in it for me? At the end of the day I got data problem. You take it you got my attention. Um I gotta move date. I got the edge. Hybrid cloud has been defined as a bona fide is done deals Hybrid multi clouds around the corner. But that's just a subsystem of the operating system that's business now. So Hybrid cloud is the operating model. Data. Supercritical. What does IBM offer? What can you offer me as a customer and why is it good? You guys got some announcements with cloud pack for data specifically here? Think what's the solution? How do I solve this? What's IBM offering? >>Yeah. So I think it starts with the fact that we have a fully unified data and AI platform meaning that they're not separate thoughts. They're all unified together as one on life cycle. And it runs anywhere on any cloud data center. To the answer starts with that notion. It helps you collect, organize and analyze data and infuse ai um throughout the business. Now, when it comes to the data complexity three core principles that were put into the next version of call Pat for data, one is automation is inevitable. It's the only way to deal with all this complexity. Uh leave the data where it is, where it lives, where it thrives and bring ai to the data. And so what we are putting into the next generation of compact for data is an intelligent data fabric, right? That is fueled by A. I. And that is going to abstract a lot of the complexity out of all this. Let me keep the data where it's at and be able to discover that data intelligently be able to catalogue it, be able to understand it right? And more importantly, to do unified queries and updates across all these distributed sources of data and bring the records together without having to take weeks and months to build new data pipelines and across that entire ecosystem, be able to enforce universal privacy and usage policies which is absolutely critical. Forrester estimates that 50 of data is not used because they're afraid that it's gonna break policy. Oh >>yeah. I mean that's a huge trust issue. I mean I I was talking to a practitioner and he's like, you know, we don't even want to do some of these transactions that are interesting experiments and and cloud opportunities because of the compliance risk, they're afraid to get sued. Yeah, >>that's right. And each one of those data stores, so if you think about the ecosystem we're talking about here of sources and consumers, data consumers, ai consumers and of course all the sources that are silent all over the place. A lot of these repositories and a lot of these different cloud violence have different policies in terms of usage and pump in privacy. Right? So how do you bring all that together? What we're delivering? The next version of compact for dad is a universal privacy plane if you will, which called auto privacy and it will basically abstract all the complexity of the different policies allow you to create them and enforce it universally. And you couldn't imagine the productivity of being to deliver that versus having a hand deal with this in a manual way. That's an example of what the data fabric, >>you know, what's interesting is you're getting at this? I'm hearing the conversation about the solution. It's okay. I'm not a mind going okay, what's the benefits? I hear I hear uh speed, um, I hear, you know, ease of use, compliance trust. But what you're really getting at is agility and there's a, there's a upside for agility that's moving fast and getting taking advantage of new opportunities or automating something away. But you mentioned the trust piece because that's where I see people afraid like, okay, if I move too fast, will I trip on over or some governance issue? Like that's a huge thing. This is a big problem. >>It's a massive problem. I mean, I mean, I think there's four, Four areas from a business perspective, right? One is think about digital experiences and we know that six and 10 customers that defect from a brand because of some bad experience usually don't return. And it's estimated that is costing the industry, you know close to $500 billion responsive experiences, which is, You have to bring the data together to do that, right? The second is the regulatory and reputational risk. Um that's another 180 billion or so. Which in many cases eight of revenue just to mitigate all that risk of using data. Not only regulatory reputational. This thing about lost productivity, how many, how many hours every week is a worker doing mundane tasks, low value work because it's not automated. Um that's like another 100 or so billion dollars of costs for enterprises. Um go on with interact with planning and forecasting. Um supply chains being inefficient. All this is being fueled by the data, right? So the more you can bring all this data together, unify it create new views that are aggregate and nature and uncover hidden insights that you couldn't do before. Um That's the magic sauce here. Right. >>Well, my last question for you on the on this product before we wrap up is there's a huge trend towards ecosystem network effect integration right there more more integration and people are partnering. I mean you have solutions where that rely on different people in the supply chain or value chain of a of a solution whether you're a concession at a ballpark or an enterprise you're connecting with other a piece, this is cloud, right? How does your cloud pack for data handle that integration and that trust? Because this is really the deployment scenario. Your thoughts? >>Yeah. I mean I think the core of top after data is it's going to greatly enhance productivity. It's going to lower costs of these, you know, complex data states. It's going to lower risk of all this and it's going to help you uncover hidden insights that you couldn't see before. Not only because of A I but because when you unify the data to get more out of it, we then go on to really point out that it's a truly open platform with an open ecosystem. So we are partnering with all the cloud partners. Right? We have a vast network of software providers that can extend and intimacy customized the platform. We have integrator partners and it's all based on open source communities. So it is fully extensible and customizable to the unique needs of every customer on any Juwan or across the city college. All >>right scott. That's great stuff. Thanks for coming on the cube. Great to see you scott, Wapner. Vice president Marketing at IBM for data and they are the hottest area. Great. Great cube alumni. Great insight. Thanks scott for coming on. Thank you. Okay, I'm jennifer with the cube You're watching ibn think 2021 coverage. Thanks for watching. Mhm >>mm. >>Yeah.

>>from around the globe If the cube with digital coverage of M I t. Chief data officer and Information quality symposium brought to you by Silicon Angle Media >>Hello, everyone. This is Day Volonte and welcome back to our continuing coverage of the M I t. Chief Data Officer CDO I Q event Interpol Bhandari is here. He's a leading voice in the CDO community and a longtime Cubillan Interpol. Great to see you. Thanks for coming on for this. Especially >>program. My pleasure. >>So when you you and I first met, you laid out what I thought was, you know, one of the most cogent frameworks to understand what a CDO is job was where the priority should be. And one of those was really understanding how, how, how data contributes to the monetization of station aligning with lines of business, a number of other things. And that was several years ago. A lot of change since then. You know, we've been doing this conference since probably twenty thirteen and back then, you know, Hadoop was coming on strong. A lot of CEOs didn't want to go near the technology that's beginning to change. CDOs and cto Zehr becoming much more aligned at the hip. The reporting organizations have changed. But I love your perspective on what you've observed as changing in the CDO roll over the last half decade or so. >>Well, did you know that I became chief data officer in two thousand six? December two thousand and six And I have done this job four times four major overnight have created of the organization from scratch each time. Now, in December of two thousand six, when I became chief data officer, there were only four. Chief Data Officer, uh, boom and I was the first in health care, and there were three, three others, you know, one of the Internet one and credit guns one and banking. And I think I'm the only one actually left standing still doing this job. That's a good thing or a bad thing. But like, you know, it certainly has allowed me to love the craft and then also scripted down to the level that, you know, I actually do think of it purely as a craft. That is. I know, going into a mutual what I'm gonna do. They were on the central second. No, the interesting things that have unfolded. Obviously, the professions taken off There are literally thousands off chief data officers now, and there are plenty off changes. I think the main change, but the job is it's, I think, a little less daunting in terms off convincing the senior leadership that it's need it because I think the awareness at the CEO level is much, much, much better than what it waas in two thousand six. Across the world. Now, having said that, I think it is still only awareness and don't think that there's really a deep understanding of those levels. And so there's a lot off infusion, which is why you will. You kind of think this is my period. But you saw all these professions take off with C titles, right? Chief Data officer, chief analytics officer, chief digital officer and chief technology officer. See, I off course is being there for a long time. And but I think these newer see positions. They're all very, very related, and they all kind of went to the same need which had to do with enterprise transformation, digital transformation, that enterprises chief digital officer, that's another and and people were all trying to essentially feel the elephants and they could only see part of it at the senior levels, and they came up with which have a role you know, seemed most meaningful to them. But really, all of us are trying to do the same job, which is to accelerate digital transformation in the enterprise. Your comment about you kind of see that the seat eels and sea deals now, uh, partnering up much more than in the past, and I think that's in available the major driving force full. That is, in my view, anyway. It's is artificial intelligence as people try to infuse artificial intelligence. Well, then it's very technical field. Still, it's not something that you know you can just hand over to somebody who has the business jobs, but not the deep technical chops to pull that off. And so, in the case off chief data officers that do have the technical jobs, you'll see them also pretty much heading up the I effort in total and you know, as I do for the IBM case, will be building the Data and AI Enablement internal platform for for IBM. But I think in other cases you you've got Chief date officers who are coming in from a different angle. You know, they built Marghera but the CTO now, because they have to. Otherwise you cannot get a I infused into the organization. >>So there were a lot of other priorities, obviously certainly digital transformation. We've been talking about it for years, but still in many organisations, there was a sense of, well, not on my watch, maybe a sense of complacency or maybe just other priorities. Cove. It obviously has changed that now one hundred percent of the companies that we talked to are really putting this digital transformation on the front burner. So how has that changed the role of CDO? Has it just been interpolate an acceleration of that reality, or has it also somewhat altered the swim lanes? >>I think I think it's It's It's Bolt actually, so I have a way of looking at this in my mind, the CDO role. But if you look at it from a business perspective, they're looking for three things. The CEO is looking for three things from the CDO. One is you know this person is going to help with the revenue off the company by enabling the production of new products, new products of resulting in new revenue and so forth. That's kind of one aspect of the monetization. Another aspect is the CEO is going to help with the efficiency within the organization by making data a lot more accessible, as well as enabling insights that reduce into and cycle time for major processes. And so that's another way that they have monitor. And the last one is a risk reduction that they're going to reduce the risk, you know, as regulations. And as you have cybersecurity exposure on incidents that you know just keep keep accelerating as well. You're gonna have to also step in and help with that. So every CDO, the way their senior leadership looks at them is some mix off three. And in some cases, one has given more importance than the other, and so far, but that's how they are essentially looking at it now. I think what digital transformation has done is it's managed to accelerate, accelerate all three off these outcomes because you need to attend to all three as you move forward. But I think that the individual balance that's struck for individuals reveals really depends on their ah, their company, their situation, who their peers are, who is actually leading the transformation and so >>forth, you know, in the value pie. A lot of the early activity around CDO sort of emanated from the quality portions of the organization. It was sort of a compliance waited roll, not necessarily when you started your own journey here. Obviously been focused on monetization how data contributes to that. But But you saw that generally, organizations, even if they didn't have a CDO, they had this sort of back office alliance thing that has totally changed the the in the value equation. It's really much more about insights, as you mentioned. So one of the big changes we've seen in the organization is that data pipeline you mentioned and and cycle time. And I'd like to dig into that a little bit because you and I have talked about this. This is one of the ways that a chief data officer and the related organizations can add the most value reduction in that cycle time. That's really where the business value comes from. So I wonder if we could talk about that a little bit and how that the constituents in the stakeholders in that in that life cycle across that data pipeline have changed. >>That's a very good question. Very insightful questions. So if you look at ah, company like idea, you know, my role in totally within IBM is to enable Ibn itself to become an AI enterprise. So infuse a on into all our major business processes. You know, things like our supply chain lead to cash well, process, you know, our finance processes like accounts receivable and procurement that soulful every major process that you can think off is using Watson mouth. So that's the That's the That's the vision that's essentially what we've implemented. And that's how we are using that now as a showcase for clients and customers. One of the things that be realized is the data and Ai enablement spots off business. You know, the work that I do also has processes. Now that's the pipeline you refer to. You know, we're setting up the data pipeline. We're setting up the machine learning pipeline, deep learning blank like we're always setting up these pipelines, And so now you have the opportunity to actually turn the so called EI ladder on its head because the Islander has to do with a first You collected data, then you curated. You make sure that it's high quality, etcetera, etcetera, fit for EI. And then eventually you get to applying, you know, ai and then infusing it into business processes. And so far, But once you recognize that the very first the earliest creases of work with the data those themselves are essentially processes. You can infuse AI into those processes, and that's what's made the cycle time reduction. And although things that I'm talking about possible because it just makes it much, much easier for somebody to then implement ai within a lot enterprise, I mean, AI requires specialized knowledge. There are pieces of a I like deep learning, but there are, you know, typically a company's gonna have, like a handful of people who even understand what that is, how to apply it. You know how models drift when they need to be refreshed, etcetera, etcetera, and so that's difficult. You can't possibly expect every business process, every business area to have that expertise, and so you've then got to rely on some core group which is going to enable them to do so. But that group can't do it manually because I get otherwise. That doesn't scale again. So then you come down to these pipelines and you've got to actually infuse AI into these data and ai enablement processes so that it becomes much, much easier to scale across another. >>Some of the CEOs, maybe they don't have the reporting structure that you do, or or maybe it's more of a far flung organization. Not that IBM is not far flung, but they may not have the ability to sort of inject AI. Maybe they can advocate for it. Do you see that as a challenge for some CEOs? And how do they so to get through that, what's what's the way in which they should be working with their constituents across the organization to successfully infuse ai? >>Yeah, that's it's. In fact, you get a very good point. I mean, when I joined IBM, one of the first observations I made and I in fact made it to a senior leadership, is that I didn't think that from a business standpoint, people really understood what a I met. So when we talked about a cognitive enterprise on the I enterprise a zaydi em. You know, our clients don't really understand what that meant, which is why it became really important to enable IBM itself to be any I enterprise. You know that. That's my data strategy. Your you kind of alluded to the fact that I have this approach. There are these five steps, while the very first step is to come up with the data strategy that enables a business strategy that the company's on. And in my case, it was, Hey, I'm going to enable the company because it wants to become a cloud and cognitive company. I'm going to enable that. And so we essentially are data strategy became one off making IBM. It's something I enterprise, but the reason for doing that the reason why that was so important was because then we could use it as a showcase for clients and customers. And so But I'm talking with our clients and customers. That's my role. I'm really the only role I'm playing is what I call an experiential selling there. I'm saying, Forget about you know, the fact that we're selling this particular product or that particular product that you got GPU servers. We've got you know what's an open scale or whatever? It doesn't really matter. Why don't you come and see what we've done internally at scale? And then we'll also lay out for you all the different pain points that we have to work through using our products so that you can kind of make the same case when you when you when you apply it internally and same common with regard to the benefit, you know the cycle, time reduction, some of the cycle time reductions that we've seen in my process is itself, you know, like this. Think about metadata business metadata generating that is so difficult. And it's again, something that's critical if you want to scale your data because you know you can't really have a good catalogue of data if you don't have good business, meditate. Eso. Anybody looking at what's in your catalog won't understand what it is. They won't be able to use it etcetera. And so we've essentially automated business metadata generation using AI and the cycle time reduction that was like ninety five percent, you know, haven't actually argue. It's more than that, because in the past, most people would not. For many many data sets, the pragmatic approach would be. Don't even bother with the business matter data. Then it becomes just put somewhere in the are, you know, data architecture somewhere in your data leg or whatever, you have data warehouse, and then it becomes the data swamp because nobody understands it now with regard to our experience applying AI, infusing it across all our major business processes are average cycle time reduction is seventy percent, so just a tremendous amount of gains are there. But to your point, unless you're able to point to some application at scale within the enterprise, you know that's meaningful for the enterprise, Which is kind of what the what the role I play in terms of bringing it forward to our clients and customers. It's harder to argue. I'll make a case or investment into A I would then be enterprise without actually being able to point to those types of use cases that have been scaled where you can demonstrate the value. So that's extremely important part of the equation. To make sure that that happens on a regular basis with our clients and customers, I will say that you know your point is vomited a lot off. Our clients and customers come back and say, Tell me when they're having a conversation. I was having a conversation just last week with major major financial service of all nations, and I got the same point saying, If you're coming out of regulation, how do I convince my leadership about the value of a I and you know, I basically responded. He asked me about the scale use cases You can show that. But perhaps the biggest point that you can make as a CDO after the senior readership is can we afford to be left up? That is the I think the biggest, you know, point that the leadership has to appreciate. Can you afford to be left up? >>I want to come back to this notion of seventy percent on average, the cycle time reduction. That's astounding. And I want to make sure people understand the potential impacts. And, I would say suspected many CEOs, if not most understand sort of system thinking. It's obviously something that you're big on but often times within organisations. You might see them trying to optimize one little portion of the data lifecycle and you know having. Okay, hey, celebrate that success. But unless you can take that systems view and reduce that overall cycle time, that's really where the business value is. And I guess my we're real question around. This is Every organization has some kind of Northstar, many about profit, and you can increase revenue are cut costs, and you can do that with data. It might be saving lives, but ultimately to drive this data culture, you've got to get people thinking about getting insights that help you with that North Star, that mission of the company, but then taking a systems view and that's seventy percent cycle time reduction is just the enormous business value that that drives, I think, sometimes gets lost on people. And these air telephone numbers in the business case aren't >>yes, No, absolutely. It's, you know, there's just a tremendous amount of potential on, and it's it's not an easy, easy thing to do by any means. So we've been always very transparent about the Dave. As you know, we put forward this this blueprint right, the cognitive enterprise blueprint, how you get to it, and I kind of have these four major pillars for the blueprint. There's obviously does this data and you're getting the data ready for the consummation that you want to do but also things like training data sets. How do you kind of run hundreds of thousands of experiments on a regular basis, which kind of review to the other pillar, which is techology? But then the last two pillars are business process, change and the culture organizational culture, you know, managing organizational considerations, that culture. If you don't keep all four in lockstep, the transformation is usually not successful at an end to end level, then it becomes much more what you pointed out, which is you have kind of point solutions and the role, you know, the CEO role doesn't make the kind of strategic impact that otherwise it could do so and this also comes back to some of the only appointee of you to do. If you think about how do you keep those four pillars and lock sync? It means you've gotta have the data leader. You also gotta have the technology, and in some cases they might be the same people. Hey, just for the moment, sake of argument, let's say they're all different people and many, many times. They are so the data leader of the technology of you and the operations leaders because the other ones own the business processes as well as the organizational years. You know, they've got it all worked together to make it an effective conservation. And so the organization structure that you talked about that in some cases my peers may not have that. You know, that's that. That is true. If the if the senior leadership is not thinking overall digital transformation, it's going to be difficult for them to them go out that >>you've also seen that culturally, historically, when it comes to data and analytics, a lot of times that the lines of business you know their their first response is to attack the quality of the data because the data may not support their agenda. So there's this idea of a data culture on, and I want to ask you how self serve fits into that. I mean, to the degree that the business feels as though they actually have some kind of ownership in the data, and it's largely, you know, their responsibility as opposed to a lot of the finger pointing that has historically gone on. Whether it's been decision support or enterprise data, warehousing or even, you know, Data Lakes. They've sort of failed toe live up to that. That promise, particularly from a cultural standpoint, it and so I wonder, How have you guys done in that regard? How did you get there? Many Any other observations you could make in that regard? >>Yeah. So, you know, I think culture is probably the hardest nut to crack all of those four pillars that I back up and you've got You've got to address that, Uh, not, you know, not just stop down, but also bottom up as well. As you know, period. Appear I'll give you some some examples based on our experience, that idea. So the way my organization is set up is there is a obviously a technology on the other. People who are doing all the data engineering were kind of laying out the foundational technical elements or the transformation. You know, the the AI enabled one be planning networks, and so so that are those people. And then there is another senior leader who reports directly to me, and his organization is all around adoptions. He's responsible for essentially taking what's available in the technology and then working with the business areas to move forward and make this make and infuse. A. I do the processes that the business and he is looking. It's done in a bottom upwards, deliberately set up, designed it to be bottom up. So what I mean by that is the team on my side is fully empowered to move forward. Why did they find a like minded team on the other side and go ahead and do it? They don't have to come back for funding they don't have, You know, they just go ahead and do it. They're basically empowered to do that. And that particular set up enabled enabled us in a couple of years to have one hundred thousand internal users on our Central data and AI enabled platform. And when I mean hundred thousand users, I mean users who were using it on a monthly basis. We company, you know, So if you haven't used it in a month, we won't come. So there it's over one hundred thousand, even very rapidly to that. That's kind of the enterprise wide storm. That's kind of the bottom up direction. The top down direction Waas the strategic element that I talked with you about what I said, Hey, be our data strategy is going to be to create, make IBM itself into any I enterprise and then use that as a showcase for plants and customers That kind of and be reiterated back. And I worked the senior leadership on that view all the time talking to customers, the central and our senior leaders. And so that's kind of the air cover to do this, you know, that mix gives you, gives you that possibility. I think from a peer to peer standpoint, but you get to these lot scale and to end processes, and that there, a couple of ways I worked that one way is we've kind of looked at our enterprise data and said, Okay, therefore, major pillars off data that we want to go after data, tomato plants, data about our offerings, data about financial data, that s and then our work full student and then within that there are obviously some pillars, like some sales data that comes in and, you know, been workforce. You could have contractors. Was his employees a center But I think for the moment, about these four major pillars off data. And so let me map that to end to end large business processes within the company. You know, the really large ones, like Enterprise Performance Management, into a or lead to cash generation into and risk insides across our full supply chain and to and things like that. And we've kind of tied these four major data pillars to those major into and processes Well, well, yes, that there's a mechanism they're obviously in terms off facilitating, and to some extent one might argue, even forcing some interaction between teams that are the way they talk. But it also brings me and my peers much closer together when you set it up that way. And that means, you know, people from the HR side people from the operation side, the data side technology side, all coming together to really move things forward. So all three tracks being hit very, very hard to move the culture fall. >>Am I also correct that you have, uh, chief data officers that reporting to you whether it's a matrix or direct within the division's? Is that right? >>Yeah, so? So I mean, you know, for in terms off our structure, as you know, way our global company, we're also far flung company. We have many different products in business units and so forth. And so, uh, one of the things that I realized early on waas we are going to need data officers, each of those business units and the business units. There's obviously the enterprise objective. And, you know, you could think of the enterprise objectives in terms of some examples based on what I said in the past, which is so enterprise objective would be We've gotta have a data foundation by essentially making data along these four pillars. I talked about clients offerings, etcetera, you know, very accessible self service. You have mentioned south, so thank you. This is where the South seven speaks. Comes it right. So you can you can get at that data quickly and appropriately, right? You want to make sure that the access control, all that stuff is designed out and you're able to change your policies and you'd swap manual. But, you know, those things got implemented very rapidly and quickly. And so you've got you've got that piece off off the off the puzzle due to go after. And then I think the other aspect off off. This is, though, when you recognize that every business unit also has its own objectives and they are looking at some of those things somewhat differently. So I'll give you an example. We've got data any our product units. Now, those CEOs right there, concern is going to be a lot more around the products themselves And how were monetizing those box and so they're not per se concerned with, You know, how you reduce the enter and cycle time off IBM in total supply chain so that this is my point. So they but they're gonna have substantial considerations and objectives that they want to accomplish. And so I recognize that early on, and we came up with this notion off a data officer council and I helped staff the council s. So this is why that's the Matrix to reporting that we talked about. But I selected some of the key Blair's that we have in those units, and I also made sure they were funded by the unit. So they report into the units because their paycheck is actually determined. Pilot unit and which makes them than aligned with the objectives off the unit, but also obviously part of my central approach so that I can disseminate it out to the organization. It comes in very, very handy when you are trying to do things across the company as well. So when we you know GDP our way, we have to get the company ready for Judy PR, I would say that this mechanism became a key key aspect of what enabled us to move forward and do it rapidly. Trouble them >>be because you had the structure that perhaps the lines of business weren't. Maybe is concerned about GDP are, but you had to be concerned with it overall. And this allowed you to sort of hiding their importance, >>right? Because think of in the case of Jeannie PR, they have to be a company wide policy and implementation, right? And if he did not have that structure already in place, it would have made it that much harder. Do you get that uniformity and consistency across the company, right, You know, So you will have to in the weapon that structure, but we already have it because way said Hey, this is around for data. We're gonna have these types of considerations that they are. And so we have this thing regular. You know, this man network that meat meets regularly every month, actually, and you know, when things like GDP are much more frequently than that, >>right? So that makes sense. We're out of time. But I wonder if we could just close if you could address the M I t CDO audience that probably this is the largest audience, Believe or not, now that it's that's virtual definitely expanded the audience, but it's still a very elite group. And the reason why I was so pleased that you agreed to do this is because you've got one of the more complex organizations out there and you've succeeded. And, ah, a lot of the hard, hard work. So what? What message would you leave the M I t CDO audience Interpol? >>So I would say that you know, it's it's this particular professional. Receiving a profession is, uh, if I have to pick one trait of let me pick two traits, I think what is your A change agent? So you have to be really comfortable with change things are going to change, the organization is going to look to you to make those changes. And so that's what aspect off your job, you know, may or may not be part of me immediately. But the those particular set of skills and characteristics and something that you know, one has to, uh one has to develop or time, And I think the other thing I would say is it's a continuous looming jaw. So you continue sexism and things keep changing around you and changing rapidly. And, you know, if you just even think just in terms off the subject areas, I mean this Syria today you've got to understand technology. Obviously, you've gotta understand data you've got to understand in a I and data science. You've got to understand cybersecurity. You've gotta understand the regulatory framework, and you've got to keep all that in mind, and you've got to distill it down to certain trends. That's that's happening, right? I mean, so this is an example of that is that there's a trend towards more regulation around privacy and also in terms off individual ownership of data, which is very different from what's before the that's kind of weather. Bucket's going and so you've got to be on top off all those things. And so the you know, the characteristic of being a continual learner, I think is a is a key aspect off this job. One other thing I would add. And this is All Star Coleman nineteen, you know, prik over nineteen in terms of those four pillars that we talked about, you know, which had to do with the data technology, business process and organization and culture. From a CDO perspective, the data and technology will obviously from consent, I would say most covert nineteen most the civil unrest. And so far, you know, the other two aspects are going to be critical as we move forward. And so the people aspect of the job has never bean, you know, more important down it's today, right? That's something that I find myself regularly doing the stalking at all levels of the organization, one on a one, which is something that we never really did before. But now we find time to do it so obviously is doable. I don't think it's just it's a change that's here to stay, and it ships >>well to your to your point about change if you were in your comfort zone before twenty twenty two things years certainly taking you out of it into Parliament. All right, thanks so much for coming back in. The Cuban addressing the M I t CDO audience really appreciate it. >>Thank you for having me. That my pleasant >>You're very welcome. And thank you for watching everybody. This is Dave a lot. They will be right back after this short >>break. You're watching the queue.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston. Everybody watching the Cube, the leader and on the ground tech coverage. My name is David Locke. They still minimus here. John Barrier is also in the house. We're covering the active FIO data driven 19 event. Second year for this conference. It's all about data. It's all about being data driven. Charlie Quanis here. He's the director of data and a I offering management and IBM. Charlie, thanks for coming on The Cube. >> Happy to be here. Thank you. >> So active Theo has had a long history with IBM. Effect with company got started at a time the marketplace took a virtual ization product and allowed them to be be first really and then get heavily into the data virtualization. They since evolved that you guys are doing a lot of partnerships together. We're going to get into that, But talk about your role with an IBM and you know, what is this data and a I offering management thing? >> He absolutely eso data and a I is our business unit within IBN Overall Corporation, our focus and our mission is really about helping our customers drive better business outcomes through data. Leveraging data in the contacts and the pursuit of analytics and artificial intelligence are augmented intelligence. >> So >> a portion of the business that I'm part of his unified governance and integration and you think about data and I as a whole, you could think about it in the context of the latter day. I often times when we talk about data and I we talk about the foundational principles and capabilities that are required to help companies and our customers progress on their journey. They II and it really is about the information architecture that we help them build. That information architectures essentially a foundational prerequisite around that journey to a i. R. Analytics and those layers of the latter day I r. Collecting the data and making sure you haven't easily accessible to the individual's need it organizing the data. That's where the unified governance in Immigration folio comes into play. Building trusted business ready data, high quality with governance around that making shorts available to be used later, thie analyzed layer in terms of leveraging the data for analytics and die and then infuse across the organization, leveraging those models across the organization. So within that context of data and I, we partnered with Active Theo at the end of 2018. >> So before we get into that, I have started dropped. You know, probably Rob Thomas is, and I want a double click on what you just said. Rob Thomas is is famous for saying There is no way I without a training, no, no artificial intelligence without information architecture so sounds good. You talk about governance. That's obviously part of it. But what does that mean? No A without a. >> So it is really about the fundamental prerequisites to be able to have the underlying infrastructure around the data assets that you have. A fundamental tenet is that data is one of your tremendous assets. Any enterprise may have a lot of time, and effort has been spent investing and man hours invested into collecting the data, making sure it's available. But at the same time, it hasn't been freed up to be. A ploy used for downstream purpose is whether it's operational use cases or analytical cases, and the information architecture is really about How do you frame your data strategy so that you have that data available to use and to drive business outcomes later. And those business outcomes, maybe results of insights that are driven out of the way the data but they got could also be part of the data pipeline that goes into feeding things like application development or test data management. And that's one of the areas that were working with that feeling. >> So the information architecture's a framework that you guys essentially publish and communicate to your clients. It doesn't require that you have IBM products plugged in, but of course, you can certainly plug in. IBM products are. If you're smart enough to develop information architect here presumably, and you got to show where your products fit. You're gonna sell more stuff, but it's not a prerequisite. I confuse other tooling if I wanted to go there. The framework is a good >> prerequisite, the products and self of course, now right. But the framework is a good foundational. Construct around how you can think about it so that you can progress along that journey, >> right? You started talking about active fio. You're relationship there. See that created the Info sphere Virtual data pipeline, right? Why did you developed that product or we'll get into it? >> Sure, it's all part of our overall unified covers and integration portfolio. Like I said, that's that organized layer of the latter day I that I was referring to. And it's all about making sure you have clear visibility and knowing what they had assets that you have. So we always talk about in terms of no trust in use. No, the data assets you have. Make sure you understand the data quality in the classification around that data that you have trust the data, understand the lineage, understand how it's been Touch Haussmann, transformed building catalog around that data and then use and make sure it's usable to downstream applications of down street individuals. And the virtual data pipeline offering really helps us on that last category around using and making use of the data, the assets that you have putting it into directly into the hands of the users of that data. So whether they be data scientist and data engineers or application developers and testers. So the virtual data pipeline and the capabilities based on activity sky virtual appliance really help build a snapshot data provide the self service user interface to be able to get into the hands of application developers and testers or data engineers and data scientist. >> And why is that important? Is it because they're actually using the same O. R. O R. Substantially similar data sets across their their their their work stream. Maybe you could explain that it's important >> because the speed at which the applications are being built insights are being driven is requiring that there is a lot more agility and ability to self service into the data that you need. Traditional challenges that we see is you think about preparing to build an application or preparing to build an aye aye model, building it, deploy it and managing it the majority of the time. 80% of the time. Todd spilled front, preparing the data talking, trying to figure out what data you need asking for and waiting for two weeks to two months to try to get access to that data getting. And they're realizing, Oh, I got the wrong data. I need to supplement that. I need to do another iteration of the model going back to try to get more data on. That's you have the area that application developers and data scientists don't necessarily want to be spending their >> time on. >> And so >> we're trying to shrink >> that timeframe. And how do we shrink? That is by providing business users our line of business users, data scientist application developers with the individuals that are actually using the data to provide their own access to it, right To be able to get that snapshot that point in time, access to that point of production data to be able to then infuse it into their development process. They're testing process or the analytic development process >> is we're we're do traditional tooling were just traditional tooling fit in this sort of new world because you remember what the Duke came out. It was like, Oh, that enterprise data warehouses dead. And then you ask customers like What's one of the most important things you're doing in your big data? Play blind and they'd say, Oh, yeah, we need R w. So I could now collect more data for lower costs keep her longer low stuff. But the traditional btw was still critical, but well, you were just describing, you know, building a cube. You guys own Cognos Obviously, that's one of the biggest acquisitions that I'm being made here is a critical component. Um, you talk about data quality, integration, those things. It's all the puzzle fits together in this larger mosaic and help us understand that. Sure >> and well, One of the fundamental things to understand is you have to know what you have right, and the data catalogue is a critical component of that data strategy. Understanding where your enterprise assets sit, they could be structured information that may be a instruction information city and file repositories or e mails, for example. But understanding what you have, understanding how it's been touched, how it's been used, understanding the requirements and limitations around that data understanding. Who are the owners of that data? So building that catalog view of your overall enterprise assets fundamental starting point from a governess standpoint. And then from there, you can allow access to individuals that are interested in understanding and leveraging that date assets that you may have in one pool here challenges data exists across enterprise everywhere. Right silos that may have rose in one particular department that then gets murdered in with another department, and then you have two organization that may not even know what the other individual has. So the challenge is to try to break down those silos, get clarity of the visibility around what assets so that individuals condemned leverage that data for whatever uses they may have, whether it be development or testing or analytics. >> So if I could generalize the problem, Yeah, too much data, not enough value. And I'll talk about value in terms of things that you guys do that I'm inferring. Risk reduction. Correct uh, speed to insights. Andan. Ultimately, lowering costs are increasing revenue. That's kind of what it's all >> the way to talk about business outcomes in terms of increase revenue, decrease costs or reduce risk, right in terms of governance, those air the three things that you want to unlock for your customers and you don't think about governance and creating new revenue streams. We generally don't think about in terms of reducing costs, but you do think about it oftentimes in terms of reducing your risk profile and compliance. But the ability to actually know your data built trust and then use that data really does open up different opportunities to actually build new application new systems of engagement uses a record new applications around analytics and a I that will unlock those different ways that we can market to customers. Cell two customers engage our own employees. >> Yes. So the initial entry into the organism the budget, if you will, is around that risk reduction. Right? Can you stand that? I got all this data and I need to make sure that I'm managing a corner on the edicts of my organization. But you actually seeing we play skeptic, you're really seeing value beyond that risk reduction. I mean, it's been nirvana in the compliance and governance world, not just compliance and governance and, you know, avoiding fees and right getting slapped on the wrist or even something worse? Sure, but we can actually, through the state Equality Initiative and integration, etcetera, etcetera Dr. Other value. You actually seeing that? >> Yes. We are actually, particularly last year with the whole onslaught of GDP are in the European Union, and the implications of GDP are here in the U. S. Or other parts of the world. Really was a pervasive topic on a lot of what we were talking about was specifically that compliance make sure you stay on the right side of the regulation, but the same time investing in that data architecture, information, architecture, investing in the governance programme actually allowed our customers to understand the different components that are touching the individual. Because it's all about individual rights and individual privacy. It's understanding what they're buying, understanding what information we're collecting on them, understanding what permissions and consent that we have, the leverage their information really allowed. Our customers actually delivered that information and for a different purpose. Outside of the whole compliance mindset is compliance is a difficult nut to crack. There's requirements around it, but at the same time, they're our best effort requirements around that as well. So the driver for us is not necessarily just about compliance, But it's about what more can you do with that govern data that you already have? Because you have to meet those compliance department anyway, to be able to flip the script and talk about business value, business impact revenue, and that's everything. >> Now you So you're only about what, six months in correct this part of the partnership? All right, so it's early days, but how's it going and what can we expect going forward? >> Don't. Great. We have a terrific partner partnership with Octavio, Like tippy a virtual Or the IBM virtual data pipeline offering is part of our broader portfolio within unified governance and fits nicely to build out some of the test data management capability that we've already had. Optimal portfolio is part of our capability. Said it's really been focused around test data management building synthetic data, orchestrating test data management as well. And the virtual data pipeline offering actually is a nice compliment to that to build out our the robust portfolio now. >> All right, Charlie. Well, hey, thanks very much for coming in the house. The event >> has been terrific. It's been terrific. It's It's amazing to be surrounded by so many people that are excited about data. We don't get that everywhere. >> They were always excited about, Right, Charlie? Thanks so much. Thank you. Thank you. All right. Keep it right there, buddy. We're back with our next guest. A Valon Day, John. Furry and student Amanda in the house. You're watching the cube Active eo active Fio data driven. 2019. Right back

>> Live from Barcelona, Spain. It's the queue covering Sisqo, Live Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back, Everyone Live here in Barcelona, Spain's two Cubes Coverage of Sisqo Live Europe. Twenty nineteen. I'm John Foreal echoes David Lock. Our next guest is Liz Santoni, senior vice president general manager of the Eye Okay Group at Cisco, formerly is part of the engineering team Cube Alumni. Great to see you again. Thanks for coming >> on. Great to be here, >> so you're >> just good to see you guys. >> You're in the centre. A lot of news. I ot of the network redefining networking on stage. We heard that talk about your role in the organization of Sisko and the product that you now have and what's going on here. >> So run R I O T business group similar to what we do with the end data center off that it has the engineering team product management team. We build products solutions that includes hardware, software, silicon. Take him out to market. Really an eye. OT It's about, you know, the technology conversation comes second. It's like, What can you deliver in terms of use, case and business outcomes that comes first, and it's more about what technology can enable that. So the conversations we have with customers are around. How can he really solve my kind of real problems? Everything from one a girl, my top line? I want to get closer to my customers because the closer I get to my customers, I know them better. So obviously can turn around and grow my top line. And I want to optimize everything from internal process to external process because just improves my bottom line at the end >> of the day. So you a lot of news happening here around your team. But first talk about redefining networking in context to your part, because edge of the network has always been what is, you know the edge of the network. Now it's extending further. I. O. T. Is one of those things that people are looking at a digit digitization standpoint, turning on Mohr intelligence with the factory floor or other areas. How how are how is I ot changing and what is it today? >> So you gave an example of, you know, digitizing something like a factory floor, right? So let's talk about that. So what customers in the factory floor want to do. They've already automated a number of this factory floors, but what they want to do is get more efficient. They want better eo. They want better quality. They want to bring security all the way down to the plant floor because the more and more you connect things, the more you just expanded your threat surface out pretty significantly so they want to bring security down to the plant floor. Because the's are environments that are not brand new, they have brown feel equipment there, green field equipment. They want to be able to have control of where what device gets in the network. With things like device profiling, they want to be able to do things like create zones so that they could do that with things like network segmentation. So when and if an attack does happen, they can contain the attack as much as possible. All right now what you need in terms ofthe a factory floor, automation, security, to be able to scale tohave that flexibility That's no different than what you have in the Enterprise already. I mean, we've been working with our idea and enterprise customers for years, and, you know, they it's about automation and security. It's about simplicity. Why not extend that out? The talent that it has, the capability that has it really is a connective tissue, that you're extending your network from that carpeted space, or you're clean space into outside of the office or into the non carpeted space. So it's perfect in terms of saying it's about extending the network into the nontraditional space that probably it doesn't go into today. >> Well, right. And it's a new constituency, right? So how are you sort of forging new relationships, new partnerships? What is described, what that's like with operations technology? >> I mean, that Cisco. We have great partnerships with the Tea organisation. I mean, we've got more than eight hundred forty thousand customers and our sales teams are product. Teams do a good job in terms of listening to customers. We're talking more and more to the line of business. We're talking more and more to the operational teams >> because of the end of >> the day. I want to be candid. You know, going to a manufacturing floor. I've never run a plan. Floor right? There are not very many people in the team who conceived in a plant manager before they know they're processes. They're concerned about twenty four seven operation. Hey, I want to be in compliance with the fire marshal, physical safety of my workers. We come in with that. I p knowledge that security knowledge that they need it's a partnership. I mean, people talk about, you know, t convergence. Usually convergence means that somebody's going to lose their job. This is Maura Night, an OT partnership, and most of these digitization efforts usually come in for the CEO level. Laura Chief Digitization Officer. We've got good relationships there already. Second part is Sister has been in this. We're quite some time. Our team's already have relationships at the plant level at the grid level operator level. You know, in the in the oil and gas area what we need to build more and more of that because building more and more that is really understanding. What business problems are they looking to solve? Then we can bring the technology to it. >> Liz, what's that in the Enable Menu? Mission Partnership? That's a good point. People, you know, someone wins, someone loses. The partnership is you're enabling your bringing new capability into the physical world, from wind wind farms to whatever What is the enablement look like? What are some of the things that happen when you guys come into these environments that are being redefined and reimagined? Or for the first time, >> Yeah, I would say, you know, I use what our customers said this morning and what he said was, it has the skills that I >> need, all right. >> They have the eyepiece skills. They have a security seals. These are all the things that I need. I want my guys to focus on kind of business processes around things that they know best. And so we're working with a CZ part of what we're putting this extended enterprise extending in ten based networking to the i o T edge means ight. Hee already knows our tools are capabilities. We're now saying we can extend that Let's go out, figure out what those use cases are together. This is why we're working with the not just the working with our channel partners as well. Who can enable these implementations on i o t implementations work? Well, >> part of >> this is also a constant, you know learning from each other. We learned from the operational teams is that hey, you can start a proof of concept really well, but he can really take it to deployment unless you address things around the complexity, the scale and the security. That's where we can come in and help. >> And you can't just throw your switches and routers over the fence. And so okay, here you go. You have to develop specific solutions for this world, right? And when you talk about that a little bit, absolutely. So >> if you look at the networking industrial networking portfolio that we have, it's built on the same catalysts, itis our wireless, a peace, our firewall. But they're more customized for this non carpeted space, right? You've got to take into consideration that these air not sitting in a controlled environment, so we test them for temperature, for shock, for vibration. But it's also built on the same software. So we're talking about the same software platform. You get the same automation features you get, the same analytics features. It's managed by DNA center. So even though we're customizing the hardware for this environment, the software platform that you get is pretty much the same, so it can come in and manage both those environments. But it also needs an understanding of what, What's the operational team looking to solve for? >> Because I want to ask you about the psychology of the buyer in this market because OT there run stuff that's just turn it on. But in the light ball, make it work. Well, I got to deploy something, so they're kind of expectations might be different. Can you share what the expectations are for the kind of experience that they wanna have with Tech? >> I used a utility is a great example and our customer from energy. I think, explain this really well, this is thing that we learned from our customers, right? I haven't been in a substation. I've been in a data center multiple times, but I haven't been in a substation. So when they're talking about automating substation, we work with customers. We've been doing this over the last ten years. We've been working with that energy team for the last two years. They taught us, really, how they secure and managing these environments. You're not going to find a CC in this environment, So when you want to send somebody out to like sixty thousand substations and you want to check on Hey, do do I still have VPN connectivity? They're not going to be able to troubleshoot it. What we did is based on the customer's ask, put a green light on there and led that shines green. All the technician does is look at it and says it's okay. If not, they called back in terms of trouble shooting it. It was just a simple example of where it's. It's different in terms of how they secure and manage on the talent that they have is different than what's in the space. So you've got to make sure that your products also cover what the operational teams need because you're not dealing with the C. C A. Or the I P experts, >> a classic market fit product market fit for what they're expecting correct led to kick around with green light. I mean, >> you know, everybody goes that such an easy thing inside was >> not that perceptive to us. >> What's the biggest thing you've learned as you move from Cisco Engineering out to the new frontier on the edge here? What? What are the learnings that you've seen actually growing mark early. It's only going to get larger, more complicated, more automation. Morey, I'm or things. What's your learning? What have you seen so far? That's the takeaway. >> So I'll see, you know, be I'm still an Cisco Engineering. The reason we're in Coyote is that a secure and reliable network that it's the foundation of any eye. Ot deployment, right? You can go out and best buy the best sensor by the best application by the best middle where. But if you don't have that foundation that's secure and reliable, those, Iet projects are not going to take off. So it's pretty simple. Everyone's network is thie enabler of their business outcome, and that's why we're in it. So this is really about extending that network out, but at the same time, understanding. What are we looking to solve for, right? So in many cases we worked with third party party hers because some of them know these domains much better than we do. But we know the AIP wear the eye patch and the security experts, and we bring that to the table better than anybody else. >> And over the top, definite showing here for the second year that we've covered it here in definite zone, that when you have that secure network that's programmable really cool things and develop on top of it. That's what great opportunity >> this is. I'm super excited that we now have an i o. T. Definite in. You know, it's part of our entire Cisco. Definite half a million developers. You know, Suzy, we and team done a fabulous job. There's more and more developers going to be starting to develop at the I o. T edge at the edge of the network. Right. So when you look at that is our platforms today with dioxin saw on top of it. Make this a software platform that developers Khun can actually build applications to. It's really about, you know, that we're ready. Highest fees and developers unleashing those applications at the i o. T edge. And with Susie making that, you know, available in terms of the tools, the resource is the sand box that you can get. It's like we expect to see more and more developers building those applications at the >> edge. We gotta talk about your announcements, right? Oh, >> yeah. Exciting set >> of hard news. >> So we launch for things today as part of Extending Ibn or in ten based networking to the I. O. T. S. The first one is we've got three new Cisco validated design. So think of a validated design as enabling our customers to actually accelerate their deployments. So our engineering teams try to mimic a CZ muchas possible a customer's environment. And they do this pre integration, pre testing of our products, third party products and we actually put him out by industry. So we have three new ones out there for manufacturing, for utilities and for mode and mobile assets. That's one. The second one is we're launching two new hardware platforms on next generation catalysts Industrial Ethernet switch. It's got modularity of interfaces, and it's got nine expansion packs. The idea is making as flexible as possible for a customer's deployment, because these boxes might sit in an environment not just for three years, like in a campus, they could sit there for five for seven for ten years. So, as you know, they are adding on giving them that flexibility that concave a bit based system and just change the expansion modules. We also launch on next generation industrial router. Actually, is the industries probably first and only full six capable industrial router, and it's got again flexibility of interfaces. We have lt. We have fiber. We have copper. You want deal? Lt. You can actually slap an expansion pack right on top of it. When five G comes in, you just take the Lt Munch a lot. You put five G, so it's five G ready >> engines on there >> and it's based on Io Exit us sexy. It's managed by DNA center and its edge enabled. So they run dialects. You, Khun, build your applications and load him on so >> you can >> build them. Third >> parties have peace here. >> The definite pieces. That third one is where we now have, you know, and I OT developer center in the definite zone. So with all the tools that are available, it enables developers and IAS peas, too. Actually, we build on top of Io Axe today. In fact, we actually have more than a couple of three examples that are already doing that. And the fourth thing is we depend on a large ecosystem of channel partners, So we've launched an Io ti specialization training program to enable them to actually help our customers implementation go faster. So those are the four things that we brought together. The key thing for us was designing these for scale flexibility and security >> capabilities available today. Is that right? >> Absolutely. In fact, if you go in worshipping in two weeks and you can see them at the innovation showcase, it's actually very cool. >> I was going to mention you brought ecosystem. Glad you brought that. I was gonna ask about how that's developing. I could only imagine new sets of names coming out of the industry in terms of building on these coyotes since his demand for Io ti. It's an emerging market in terms of newness, with a lot of head room. So what's ecosystem look like? Missouri patterns and Aya's vsv ours as they take the shape of the classic ecosystem? Or is it a new set of characters? Or what's the makeup of the >> island's ecosystem, >> I would say is in many ways, if you've been in the eye ot world for sometime, you'll say, You know, it's not like there's a whole new set of characters. Yes, you have more cloud players in there, you You probably have more s eyes in there. But it's been like the distributor's Arvin there. The machine builders thie ot platforms. These folks have been doing this for a long time. It's more around. How do you partner and where do you monetize? We know where you know the value we bring in we rely on. We work very closely with this OT partners machine builders s eyes the cloud partners to go to market and deliver this. You're right. The market's going to evolve because the whole new conversation is around. Data. What do I collect? What do I computer the edge? Where do I go around it to? Should I take it to my own premises? Data centers. Should I take it to the cloud who gets control over the data? How do I make sure that I have control over the data as a customer and I have control over who gets to see it? So I think this will be a revolving conversation. This is something we're enabling with one of our Connecticut platforms, which are not launch. It's already launched in terms of enabling customers to have control over the data and managed to bring >> all the portfolio of Cisco Security Analytics management to the table that puts anything in the world that has power and connectivity to be a device to connect into its system. This is the way it's just I mean, how obvious going Beat commits a huge >> I'm grateful that it's great that you think it's obvious. That's exactly what we're trying to tell our customers. >> How to do is >> about extending >> the way >> we do. It's the playbook, right? Each business has its own unique. There's no general purpose. Coyote is their correct pretty much custom because, um, well, thanks for coming on this. Appreciate it when I ask you one final question. You know, I was really impressed with Karen. Had a great session on wall kind of session yesterday. Impact with women. We interviewed you a Grace offered twenty fifteen. Cisco's doing amazing work. You take a minute to talk about some of the things that Cisco's doing around women in computing. Women in stem. Just great momentum, great success story, great leadership. >> I would say Look at her leadership at Chuck's level, and I think that's a great example in terms of He brings people on, depending on what they can, what they bring to the table, right? They just happened to be a lot of women out there. And the reality is I work for a company that believes in inclusion, whether it's gender race, different experiences, different a different thoughts, different perspective because that's what truly in terms of you can bring in the culture that drives that innovation. I've been sponsoring our women in science and engineering, for I can't remember the last for five years. It's a community that continues to grow, and and the reality is we don't sit in there and talk about, you know, what was me and all the things they're happening. What we talk about is, What are the cool new technologies that are out there? How do I get my hands on him? And yeah, there we talk about some things where women are little reticent and shy to do so. What we learn from other people's experiences, many time the guy's air very interested. So what? You sit them there and talking to said, Trust me, it's not like a whining and moaning section. It's more in terms of where we learned from each other >> years talking and sharing ideas, >> absolute >> innovation and building things. >> And we've got, you know, you look we look around that's a great set of women leaders throughout the company. At every single level at every function. It's ah, it's It's great to be there. We continue to sponsor Grace offer. We have some of the biggest presence at Grace Offer. We do so many other things like connected women within the company. It's just a I would say fabulous place to be. >> You guys do a lot of great things for society. Great company, great leadership. Thank you for doing all that's phenomenal. We love covering it, too. So we'll be affect cloud now today in Silicon Valley. Women in data science at Stanford and among them the >> greatest passion of our things. Straight here. >> Thanks for coming on this. The Cube live coverage here in Barcelona. Francisco Live twenty eighteen back with more. After the short break, I'm jump area with evil Aunt. Be right back

(techy music) >> Hello, everyone, I'm John Furrier, here in theCUBE Studios in Palo Alto for a special CUBE Conversation. Breaking news here in Silicon Valley and at Cisco Systems. News around Cisco partnering with Amazon Web Services, and here to talk about it is Fabio Gori, senior director of cloud solutions marketing at Cisco. Good to see you, welcome to theCUBE. >> Hello, John, how are you doing? >> So, big news, Cisco and AWS collaborate to accelerate innovation. A first kind of its kind of announcement. Love the pioneering aspect of this announcement. Obviously Amazon Web Services is the leading cloud provider who's been into hybrid cloud lately because they've been talking about that as their connection point into the enterprise. You guys are the leader in the enterprise at networking and other services. I don't even know how much market share you have these days, but you guys pretty much own the enterprise. Everyone kind of knows that. This deal with Amazon, you guys are doing the first hybrid Kubernetes on AWS. Talk about the announcement, what's the, why is this so important to Cisco? >> So, you named it, the solution name is actually a bit of a mouthful, but you mentioned the three keywords: is hybrid, is Kubernetes, is AWS, and this is the first solution of this kind that really integrates these two environments in a way that will be exceptionally beneficial for organizations that want to accelerate their innovation path, which ultimately means delivering applications faster without having to worry about constraints in terms of where to develop, where to deploy. This will really set them free to take their decisions. >> You know, one of the things we've been speculating on theCUBE a lot around cloud... There's been tons of debates, hybrid cloud, private cloud, multi-cloud, public cloud. All this stuff's been going on. One thing that's been very clear is the public cloud has demonstrated speed, agility, faster time to value, and for app developers that's been great. Cloud native, if you're born in the cloud it's just a great environment. If you've been on-premise and you had that legacy and/or existing pre-cloud environment, that trend has been more toward cloud operations, so not so much everything's moving to the cloud, although, you know, Andy Jassy would love to see everything move to Amazon, and that's his goal, but stuff stays on-prem, it's going to be for a while, but the cloud operations on-premise is essentially cloud but on-premise. So, that's this new hybrid dynamic. This is what enterprises have been re-imagining their infrastructure on. This is where a lot of the energy has been. How does that, that your solution for Kubernetes with Amazon, solve that problem? Does it help customers get to the cloud faster? Is it an operating model? Explain the nuance of how customers-- >> It's fundamentally all of that. If you think about it, and your introduction is spot-on, is customers really want to use the public cloud, right? The services in the public cloud, why? Because it gives them speed. I think that's a big change that we've seen since, I would say a few quarters, right, where people started really trading off speed and innovation for cost, right? Originally it was like, "I want to shut down my data center. "The cloud is going to be cheaper." Well, it's not about cheaper, it's faster, and people want to develop new digital experiences, which boils down to building applications faster. So, what ultimately they want to do is making the infrastructure on-prem looking like a bit more like the public cloud. Now, it's never going to be just like the public cloud with all the bells and whistles and innovation, but it's got to be such that you can actually take the best innovation of the public cloud, Kubernetes first, to the on-prem, rather than the other way around, right? That's our North Star, that's our belief, and Kubernetes is clearly a big winner in the container market. The way to develop new applications is based on containers. Kubernetes is the orchestrator right now in the marketplace. Every single big cloud provider has launched Kubernetes-based services in various forms, and so the enterprises are now looking at businesses of every size. They are trying to figure out how to really develop this capability on-prem, because in the end, as you know, it's never kind of black and white, right? We're still working with mainframes, long life to the mainframes. Going to be around for 20 years, probably. We're going to have traditional databases, ERP systems and the like for a very, very long time. What do you do, right? Everything that you develop new in the cloud needs to ultimately connect back to the existing systems because that's what you need. >> So, the simplicity of this is interesting. I want to just rewind that for a second. So, you're taking the best of Amazon, the container service, and alas, a container service with Kubernetes, bringing, making that available on-premises through the Cisco container platform-- >> Correct. >> So, this is the linchpin, so it's almost like-- >> Correct. >> You're not trying to take Cisco and say, "Oh, we're cloudified." >> Correct. >> You're taking the Cisco environment, which everyone runs, and some people think it runs great and-- >> Correct. >> They're not going to change that overnight, (chuckles) but you now enable them to take what they're doing here and make it compatible with the cloud and on-ramp to the cloud? >> So, the idea is fundamentally not so much taking EKS on-prem, that's not the thing, but the idea is having a container platform that fundamentally gives you pretty much a transparent way of interacting with the other side, and when I say transparent I really mean the linchpin of the solution, which is around the identity and authentication, right? What we've done that really differentiates this, that makes this so unique right now is that we have integrated IAM, you know, identity and authorization, sorry, and authentication in common. So, you're going to use the same set of keys on both sides, which of course is a developer dream because you don't have to use different type of keys in authentication models if you're a user. It's the same thing, and it's a dream for IT operation, because of course this is much simpler, as well as for the CSO and the security team. This makes it extremely secure, reduces the risk so that you have really a very consistent, integrated kind of solution, which is good-- >> So, there's engineering involved on Cisco's side. Can you elaborate on-- >> Actually, it's been a collaboration between the two sides, so-- >> Okay, explain, explain the partnership. >> So, absolutely, it's actually a collaboration. So, we've been collaborating to build this integrated architecture, right? It is a Cisco solution, but developed in collaboration with AWS, right, and so what we've been doing is fundamentally looking at how EKS was going to available to the container platform-- >> Mm-hm. >> Right, so that you'll be able to fundamentally orchestrate your containers in the most efficient way, regardless of where the containers actually end up being, which is actually what we're hearing from customers. Customers want to just take the containers that are coming from the developers and be free to develop whatever they want. Sorry, to deploy whatever they want. >> So, the containers are key here. So, the container service-- >> Yeah. >> And Kubernetes, which orchestrates containers, works across with the identity layer allows for, what, seamless interaction? Is that the key for developers is that I can... Take me through a quick use case. Explain it with an example. >> I don't know, you may take a new application in the banking, on the banking side, or you can take some new artificial intelligence kind of applications, or machine learning. What you fundamentally can do now is deciding, well, first of all what kind of tools you want to use. Do you want to use the AWS cloud with all the development tools, do you want to use yours? It doesn't matter, at the end there is an endover between the developers and the IT operations team, and the IT operations team, now with this solution, can fundamentally, quickly and easily provision clusters wherever they want, right, and they do it on the basis of their specific parameters, their specific goals, what do you want? It could be cost, it could be security, it could be reliability. Whatever it is, right? >> Mm-hm. >> It doesn't matter, this is not about the religion of whether it's public cloud or on-prem. It's just using the best of both worlds and deploying wherever it makes sense. >> You know, Andy Jassy and I always talk when we, at re:Invent. He always comes back to the same refrain, he always hits the same notes: "We listen to our customers, we're driven by the customers. "They take us where we want to go." >> Yeah. >> I know Cisco's been very customer-centric as well. How is the customers' reaction? What have they been telling you around why the solutions to develop... I mean, because we know Shadow IT's been going on with Amazon-- >> Yeah. >> For, you know, almost a decade. They put their credit card, they sneak up on Amazon, build some stuff, and look how easy, and then bring back to the IT department saying, "Hey, look what I did in the cloud, now you implement it." "Whoa, we've got network policies." So, there's been kind of that kind of tension, kind of R&D, if you will-- >> Yeah. >> But it's still happening. That kind of goes away here with this kind of announcement. How is the customer needs been profiled as you look at the announcement? What's the key reasons why they want this solution, and why did Amazon glob onto it, because they're not going to do something unless-- >> Yeah. >> It's a customer need. >> Yeah. >> Talk about that. >> Well, I would say, you know, it's really meeting the customer where they are, right, and again, we have two environments that, you know, have been inspired by different kind of criteria, right? There's a lot about application modernization, there's a lot about security, all about compliance on-prem. Of course the cloud is also very secure. I think we're over these kind of artificial discussions, but as AWS will say, it's a shared responsibility model, right? They guarantee the security of the cloud, and you're responsible for the security in the cloud, and so ultimately what people want to have is how can we actually integrate these two worlds in a consistent fashion, right, so that I have a consistent environment. That's really the keyword here, consistent environment where I have comomino networking between these things, wherever they are, comomino securing them, including authentication, identity and authentication, comomino monitoring this application, because the alternative is building another silo, and that's what people don't want to do. >> Mm-hm. >> Right? If I add another silo I may add innovation, but it comes to a very high cost. >> Yeah. >> People want to add innovation without disruption. They want to have this consistency and just extend the way they do things, of course going into a devops model and getting faster and faster, because that's the way to compete. >> Now, I think IT operations is an area, with the development enablement you guys have had, and with the work you guys have been doing DevNet and DevNet Create, this notion of programmability-- >> Yeah. >> You're right in line with the wave that everyone wants to ride, which is lower the cost of mundane tasks and/or scripts and things of that nature, command line interface, that's kind of like a hodge podge, make the network programmable and automate, and make the developer freer to do better things seems to be the trendline, so with that in mind, does this fit that horizontally scalable vision of the cloud? Do you see this having impact into say network sales, application, where's the key impact points for the customer, what impacts them? >> It's a huge impact, right, and depends whether you're taking like a tactical view of things, like literally application by application, or classes of application, or you're really thinking about where is this trend kind of taking you, right? Now, if you take the former kind of approach, then you're starting kind of identifying a whole bunch of different issues, like again, for instance the security one. The networking one is huge, right? People go, I don't know, Office 365 and they get disappointed. Why, because all the traffic gets tromboned through the data center because that's how things were. >> Choked them. >> Right? Now you're completely changing the application on top, and you discover that the infrastructure underneath hasn't been designed to accommodate those kind of traffic flows-- >> Yeah. >> Right, and so you're starting solving problem by problem. The fact of the matter is with the rise of the cloud the infrastructure and the processes in IT need to change altogether. Its infrastructure, its processes with of course the rise of devops, its relentless automation, right, potentially driven by, you know, more and more machine learning, and you know, AI kind of capabilities unfold. >> Just talk about that, because this is a big discussion, because I'm interviewing a lot of CIOs or CXOs or senior IT practitioners, and the ones that are successful are the ones who recognize the wave. Some people take different steps, they'll experiment, they'll do some tests. Some will just go all in and revamp, but they all recognize the one point. They've got to re-architect and re-imagine-- >> Yeah. >> The It infrastructure-- >> Yeah. >> Up and down, and the cloud is a big force and function-- >> Yeah. >> A role of data, programmability, automation. Now new concepts in some cases. Containers we all have been around for a while, but how do you guys talk to your customers, because this is something-- First of all, do you believe in that, and two, what do you talk to your customers about when you're saying, "Look, the hard truth "is how we got here is not how we go forward." >> Absolutely, well, you know, there are different ways. You can either boil the ocean, or for instance you take a solution like this. If you take a solution like this, you can actually sit down and discuss how to build a solution and architect a solution like this in collaboration with AWS. It took establishing four key principles, right? The first one has got to be hybrid, right, which means you need to strive to build this consistent environment between the two domains. Second, it has to be production-grade. We're speaking with customers adopting Kubernetes. They're saying that they get to a point where they need to integrate 20 opensource tools. Now, I wonder whether that's going to take you anywhere over the long term once you scale, you know, your operation. Can you actually do it with that kind of approach? Third, and this is a big one, you have to be able to manage this new hybrid reality, managing not just the new apps, but the old apps as well, and fourth, it's got to be extensible. You're starting from, like-- >> Yeah. >> Containers and authentication, how about everything else, right? How about cloud management and orchestration? How about application performance management, because now apps are getting everywhere, and of course, you know, that's probably the next episode of theCUBE that we can do together, they're going to the edge. >> (chuckles) Yeah. >> So, it's getting very, very complicated. So, even with a simple, well, "simple" example like this, you're starting seeing some principles that you need to establish, and that should inspire how you actually transform your infrastructure and operation. The worst thing that you can do is taking a tactical approach and just going step-by-step, and then, you know, move by move. >> Well, let's definitely do that CUBE. A couple of segments we'll have to do more of a deep dive with some slides. Certainly the edge is going to be a big point, but I want to ask you the impact to your customer base, because I think this is a game-changing announcement. I mean, Amazon Web Services, they don't do a lot of Barney deals. They don't do, you know, a lot of deals that look good on paper. They're very specific about how they do their business development, so it's a huge win for them, I think, and for you guys, but I think Cisco customers are going to be impacted, so please explain the impact to Cisco customers. What does it mean to me, I'm a Cisco customer. I've got routers, I've got switches, I've got UCS servers. I got all kinds of stuff in there. How does this impact my life, what changes, do I throw away gear, do I buy new gear, do I buy software? How do I buy the service, am I buying Amazon, do I have to now... Explain all that, how does the customer engage with the solution, and what's the impact to their environment? >> Well, that's a very big question. (laughs) Let me frame it a little bit, right? First of all, how are they impacted? They're impacted by the cloud altogether, right, and very often they're using multiple clouds, so we know it's multiple services, so they need to start thinking in terms of those principles that we said before. From a company standpoint, of course we've been well known over the last 30, 35 years, right, not to leave everybody behind. We're trying to, of course, accommodate the change of the infrastructure, and for instance, how do you move from CLI to more programmability through, for instance, you know, the rise of IBN, which is the intent-based networking where you have more policy-based models that help you fundamentally automate in the network, whether it's about, you know, connecting your data centers or connecting your branches, you have to fundamentally adopt more and more automation into your strategy, and so what we're doing is we're fundamentally helping customers making this kind of transformation. You mentioned DevNet, I think that's like the tip of the iceberg of also a new Cisco wave, right, where it's all about, if you want, transforming the talent that's been working with us in the company and outside the company, and having them taking it to the next level where instead of, you know, going classic CLI you're more and more kind of thinking in an automated fashion, because you have to get fast. The only thing that really matters is getting faster. >> I noticed you guys aren't just... Give you guys a lot of props here because you guys have a lot of meat on the bones with this announcement. Simplifying container orchestration with the Cisco hybrid solution for Kubernetes on AWS. You know, Linux Foundation wants to see it that way, Amazon's that way, but you guys have a lot of code up and running on the Sandboxes, and for the folks watching, developer.cisco.com/aws. developer.cisco.com/aws. You already got Sandboxes up already. >> Absolutely. >> Five labs for cloud native, you got the EKS-- >> Yep. >> Cloud thing up and running. >> Yeah, and we'll continue adding more and more material. The cloud is a different world, right? People want to experiment it, and by the way, if you think about how we're packaging and pricing the solution, you can actually start in a very modular way, right? You can just go with the software if you want, or you can buy the software and the hardware underneath. You can go with one, three, or five years. You can get demos of the solution. If you want, it's a different way of experimenting Cisco, but we're there. I mean, we made the change. We're totally for adding a softer motion to an already strong kind of hardware component that has been traditionally our strength, and if you think about it, having the full stack we can do some magic. If you buy Cisco software, like this solution, and then you put in Cisco hardware, such as HyperFlex and ACIR data center infrastructure-- >> Yeah. >> That a lot of customers are using you get fundamentally greater performance, you get a single number to call-- >> Yeah. >> Which is actually great. >> You know, it's interesting Fabio, and I talked with Lou Tucker years ago and then, well, continue to talk to him every year, as well as Susie Wee, and we see this on the cloud native, born on the cloud side, IT doesn't exist in a lot of these cloud native companies because the developers do all the IT, so you guys are seeing a surge in DevNet and DevNet Create where the Cisco ecosystem, your customers are turning into developers naturally-- >> Absolutely. >> And so we've seen that shift at Cisco-- >> Yeah. >> And that has happened internally. You guys recognize that the developer ecosystem, not the cloud native, but the application developers and-- >> Yeah. >> That your command line interface guys-- >> Yeah. >> And gals are turning into developers because-- >> Absolutely. >> Slinging code these days is pretty straightforward. >> Absolutely, if you look at our, actually my friend Susie Wee and how she is pitching this change. She talks about DevNet ops, others talk about DevSec ops. Whatever that is, you know, whatever kind of terminology you're using, it boils down to the same concept. You have to automate the way that you manage the infrastructure, right? >> Mm-hm. >> Infrastructure needs to become more responsive and faster. You can open five or six trouble tickets just to provision, you know, a container to a developer that's not going to carry it in the future. >> Yeah, it's kind of against them. >> It's got to be fast. >> Yeah, and then, you know, making the network programmable is the devops movement that's coming 2.0. >> Absolutely. >> And you guys are aware of, I know you are. It's interesting to see how Amazon relates to that. When you talk about that to AWS, what's the conversation like? Do they like, they obviously get it, and they're smart, they must get it immediately. >> I mean, absolutely, the reason why we're having this collaboration is very simple. I mean, they get the same requests from the customer. We're fundamentally speaking to the same people. Yeah, there may be differences sometimes, you know, the developer versus the IT operation, but in the end it boils down to the request, "Hey, you know, the public cloud is fantastic, "but I also want to have a solution for on-prem," right? "I have my needs," and if you're not totally burned into the cloud you have to, you want to have investment protection. You want to have, you know, your on-prem environment for whatever reason, right, and it's not about religion, it's about economics, it's about, you know, viability of certain solutions and the likes. >> Well, great news, congratulations. Fabio, great announcement with Amazon Web Services, good deal, hybrid cloud. Now, you guys, also at Cisco, you guys aren't married to one cloud, so I've got to ask the hard question. With impact to Google, Microsoft, you guys have relationships. How does this match up from an integration standpoint with other clouds? Is it deeper, is it more coming on the other clouds? Can you just kind of give us a description of the evolution of Cisco with the other clouds in this hybrid architecture? >> You know, I want to stay true to one of the principles that we mentioned and we orbited around this conversation, right, for the last 15 minutes, and that is we're customer-centric, right? Customers want to use the clouds that they want to use, we're there to help them, right? Now, AWS is of course, if you look at the share it's a pretty big market leader, but we will work with all the providers that our customers want to use. That's actually the North Star that we have. Now, if you look at the kind of, if you want, products or stacks or architectures, you will see that there is a huge degree of commonality across all of this, right? So, we're using kind of the same baseline software, but configuring slightly different ways for a, different way, for a simple reason, right, because the clouds are different, and not just the clouds are different, the cloud providers are different. So, we're paying full respect, you sit down, you discuss objectives, and then you actually go after those goals. >> Yeah, you just got to get out there and do those... You got to just do the work and integrate in. >> So, you have to expect a slight degree of integration-- >> Yeah. >> Because of the nature of the cloud business and the cloud providers, but I think when you look from a customer standpoint, what they want and what they're asking Cisco to do, they want to have commonalities. >> Mm-hm. >> Right? They want to have the same mean of networking, the same mean of securing these environments. They want to have the same way of extracting analytics, especially for application performance, and they want to have comomino managing and orchestrating all of these resources because the alternative is fundamentally getting lost into different tools and different clouds that by design cannot work in other environments, and so that's what customers want, and that's what we're pursuing as a company. >> Fabio, talk about the announcement in terms of just summarizing it real quick. You talked to a lot of customers, you've been doing press tours all day today, analysts, financial Wall Street, all the whole nine yards. Now you're here on theCUBE. What's the summary, what's the big walkaway looking back now after the announcement? Talk about the impact, what is this about? What is actually happening in your mind? How are people reacting to it, how big will this be? >> You know, I have two things in mind when I give myself that kind of question, right? The first one is I have this concept in my mind of making Kubernetes the engine of your innovation, right? This is about really transforming this new container orchestration technology that sounded esoteric until (chuckles) a few months ago into the cornerstone of the innovation, right? We've been talking about hybrid for a long while, but we believe that it's about mostly taking the best of the public cloud and making it work on-prem, rather than going the other way around, that's for sure, and I would say in general is this is a big first step into closing that gap between the infrastructure and the applications, which is kind of by definition closing the public cloud, but when it comes to the on-prem world we're still pretty far away, right, and so clearly there's a lot of competition in the marketplace, and we want to win that battle to close this gap, and closing that gap means fundamentally enabling customers to innovate and developing their new digital experience faster, and that's actually the nature of their business. >> Yeah, and they get value. >> It's not an IT conversation anymore, it's a business. >> And the value extraction and creation from new applications, and I think you've got to give credit to the Kubernetes community, because what's great about Kubernetes and then watching that evolve. We were there-- >> Yeah. >> theCUBE present at creation when it started, you know, hanging around OpenStack and all the different activities around the Linux Foundation before it went there, was that you had containers obviously happening, but the industry got behind kind of a defacto standard. >> Yeah. >> We've seen this before, TCPIP sounds like one of those things that just became a defacto standard and then it became a standard-- >> Well, another example with Linux itself, right? I mean, once, you know, big companies started going behind it and offering enterprise cloud support we saw really very, very rapid ramp-up. I think we're seeing the same with Kubernetes. I think now there are a bit less doubts about where the world is going. This is a clearly a winner, and people, I think, are now-- >> Yeah, and it's clear you guys are getting behind it. It's just Amazon doesn't do deals, like I said, unless it's a serious thing, so congratulations. You guys are getting behind Kubernetes. >> Yeah. >> Congratulations. >> Yeah, thank you for that. >> All right, Fabio Gori. Here inside the studio with Cisco, breaking down the hot news, game-changing news, Cisco's partnering with AWS with Kubernetes to really bring a level of industry standard and seamless integration between on-premises and the cloud, and excited to keep bringing you more action. Coming up we're going to be at the CNCF event, Kubcon, check us out there and also Amazon re:Invent, theCUBE will have multiple sets there. I'm John Furrier here in Palo Alto for this CUBE Conversation, thanks for watching. (techy music)

San Francisco it's the Red Hat summit 2018 brought to you by Red Hat okay welcome back everyone this is the cube live in San Francisco for Red Hat summit 2018 I'm John for the co-host of the cube and this week for three days of wall-to-wall coverage my co-host analyst is John Tory the co-founder of check reckoning and advisory and community development services firm industry legend formerly VMware's Bentley he was at the Q in 2010 our first ever cube nine years ago John Day one wrap up let's analyze what we heard and dissect and and put Red Hat into day one in the books but you know clearly it's a red-letter day for red hat so to speak your thoughts big day for open shift I think and hybrid cloud right we just saw a lot of signs here that we'll talk about that it's real there's real enterprises here real deployments in the cloud multi-cloud on-site hybrid cloud and i think there's really no doubt about that they really brought a brought the team out and you know red hat's become a bellwether relative to the tech industry because if you look at what they do there's so many irons on the fires but more the most important is that they have huge customer base in the enterprise which they've earned over a decades of work being the open source renegade to the open source darling and Tier one citizen they got a huge install basin they got to manage this so they can't just throw you know spaghetti at the wall they gotta have big solutions they're very technical company very humble but they do make some good tech bets absolutely we'll be talking with the folks from core OS tomorrow they have a couple of other action you know things we'll be talking about a lot of interesting partnerships the the most you know the thing here Linux is real and it's is the 20-year growth and that it's real in the enterprise and I mean the top line think the top line slowed and John is is is kubernetes than the gnu/linux for the cloud and I got to say there's some reality there yeah it's there's no doubt about it I mean then I've got my notes here just my summary for the day is on that point the new wave is here okay the glue layer that kubernetes and containers provide on top of say Linux in this case OpenShift a you know alternative past layer just a few years ago becomes the centerpiece of red hats you know architecture really providing some amazing benefits so I think what's clear is that this new shift this new wave is massive and we've heard on the cube multiple references to tcp/ip HTTP these are seminal moments where there's a massive inflection point where the games just radically changes for the better wealth creation happens startups boom new brands emerged that we've never heard of that just come out of the woodwork entrepreneurial activity hits an all-time high and they all these things are coming yeah I said John I was really impressed if we talk to a number of folks who are involved with technologies that some people might call legacy right we the Java programmers the IBM WebSphere folks they've been you you look at these technologies solid proven tested but yet still over here and adapted for today right and they talked about how they're fitting into openshift how they're fitting into modern application development and you're not leaving those people behind they're really here and you know the old joke going back to say Microsoft when Steve Ballmer was the CEO hell will freeze over when Linux isn't in in Microsoft ecosystem look today no further than what's going on in their developer Commerce called Microsoft build where Linux is the centerpiece of their open-source strategy and Microsoft has transformed themselves into a total open-source world so you know now you got Oracle with giving up Java II calling a Jakarta essentially bringing Java into an the Eclipse community huge move it's a kind of a nuance point but that's another signal of the shifts going on out in the open where communities aren't just yesterday's open source model a new generation of open source actors are coming in a new model I think the CNC F is showing it the Linux Foundation proves that you can have commercialization downstream with open source projects as that catalyst point as a big deal and I think that is happening at a new new level and it's super exciting to see yeah I mean open source is the new normal sure that that works it's in the enterprise but that doesn't mean that open source disappears it actually means that open source and communities and companies coming together to drive innovation actually gets more and more important I kind of thought well you know it's open source well everybody does open source but actually the the dynamics we're seeing of these both large companies partnering with small companies foundations like you talked about the Linux cutlasses various parts the Linux Foundation cloud boundary foundation etc right are really making a big impact well we had earlier on assistant general counsel David Levine and bringing about open source I think one key thing that's notable is this next generation of open source wave comes is the business model of open source and operationalizing it in not just server development lifecycle but in the business operation so for example spending resources on managing proprietary products with that have open source components separate from the community is a resource that you don't have to spend anymore if you just contribute everything to open source that energy can go away so I think open source projects and the product monetization component not new concepts is now highlighted as a bonafide competitive advantage across the company not just proven but like operationally sound legally verified certified and I think also you have to look at the distribution of open source versus the operation and management of open source we see a lot of management managed kubernetes coming out and in fact we didn't talk about today Microsoft big announcement here at the show Microsoft is on Azure is running a managed open ship not not kubernetes they already have kubernetes they're running a managed open ship another way of adding value to an open open source platforms to date directly to the IT operator honestly do you think these kind of deals would happen if you go back four years three years ago oh no way as you're running an open shift absolutely I mean were you crazy the you know the kingdom is turned upside down absolutely this is a notable point I want to get your reaction is because I see this absolutely as validation to the new wave being here with kubernetes containers as a de facto rallying point an inflection point big deals are happening IBM and Red Hat big deal we just talked about them with the players here two bellwether saying we're getting behind containers and two bays in a big way from that relationship essentially it changes the game literally overnight for IBM changes the game for Red Hat I think a little bit more for IBM than Red Hat already gets a ton of benefit but IBM instantly gets a cloud strategy that has a real scalable product market to it Arvind the the head of research laid that out and IBM now can go and compete with major players on deals with the private cloud more deals are coming absolutely this is the beginning now that everyone snapped into place is saying okay kubernetes and containers we now understand this the rallying cry a de facto standard I think a formation is going to happen in the next six to 12 months of major major major players now I mean we are in a not one size does not fit all world John so I mean we will continue to see healthy ecosystems I mean mesosphere and DT cos is still out there Dockers still out there right you will see very functional communities and and functioning application platforms and cloud platforms but you got to say the momentum is here I mean look at amine docker mace those fears look at when things like this happened this is my opinion so I'm just gonna say it out there when you have de facto standards that happen like this it's an opportunity to differentiate so I think what's gonna happen is docker meso sphere and others including the legacy guys like IBM and in others they have to differentiate their products they have to compete software companies so I think docker I think is come tonight at docker con but my opinion looking at from the outside is I think Dockers realized looking we can't make money from containers kubernetes is happening we're a great standard in that let's be a software company let's differentiate around kubernetes so this is just more pressure or more call-to-action to deliver good software hey it's never been of somebody said it's never been a better time to be an IT and IT infrastructure right this is a you think that the tools we have available to us super-powerful another key point I want to get your reaction on with kubernetes and containers this kind of de facto standardization is breathing new life into good initiatives and legacy projects so you think about OpenStack okay OpenStack gets a nice segmented approach is now clear with a where the swim lanes are you're an app developer you go over here and if you are a network and infrastructure guy you're going here but middleware a from talk to the Red Hat guys here we talk to IBM those legacy and apps can put a container around it and don't have to be thrown away and take their natural course now I think it's gonna be a three line through this holy a second life is for legacy and stuff and then to cloud is and it's in second inning because now you have the enablement for cloud your reaction the enablement of cloud Ibn iBM has cloud and then the market shares of nm who you believe they're not in that they're in the top three but they're not double digits according to synergy research and he bought us a little bit higher but still if you compare public cloud they're small they look at IBM's and tire and small base and saying if they have a specialty cloud that can be assembled quit Nellie yeah and scaled and maybe instantly successfully overnight yeah I think a few years ago you know there was a lot different always a few years back it always looks confusing right a few years back we were still arguing public cloud private cloud as private cloud ed is what is a true private cloud is that even valuable I still see people on Twitter making fun of everything anybody who's not 100% into the full public cloud which means they must not have talked to you know a lot of IT folks who have to business to run today so I think you're saying it's a it's a it's a multivalent world multi-cloud there's going to be differentiated clouds there's going to be operational clouds there's gonna be financial clouds and just it's it seems clear that you know from the perspective of right now here in San Francisco and 2018 that that you know the purpose of public-private hybrid seems pretty clear just like the purpose of like I said we're gonna in two weeks we'll be an openstack summit I mean the purpose of that seems pretty clear it's it's funny it's like I had this argument and each Assateague he thinks everything should go the public cloud goes eaten has one of the public clouds but he's kind of right and I and I and we talked about this way I with him I said if everything is running cloud operation we're talking about cloud ops we're talking about how its managed how its deployed code bases across the board if everything is clarified from an OP raishin standpoint the Dearing on Prem and cloud and IOT edge is there's no difference stuffs moving around so you almost treats a data center as an edge network so now it's sexually all cloud in my mind so then and also you do have to keep in mind time time horizons right anybody who has to do work the today this quarter right has to keep in mind what's what what portfolio of business deeds and tools do I have right now versus what it's gonna look like in a few years all right so I want to get your thoughts on your walk away from today I'll start my walk away from day one was talking some of the practitioners Macquarie Bank and Amadeus to me they're a tell signed the canary in the coalmine what's happening horizontally scalable synchronous infrastructure the new model is here now we're seeing them saying things like it's a streaming world not just Kafka for streaming data streaming services levels of granularity that at workers traded with containers and kubernetes up and down the stack to me architects who think that way will have a preferred advantage over everybody else that to me was like okay we're seeing it play out I guess I totally agree right the future isn't evenly distributed my takeaway though is there's certainly a future here and the people we talked to today are doing real-world enterprise scale multi-cloud micro services and modern architectures incorporating their legacy applications and components and that and they're just doing it and they're not even breaking a sweat so I think IT has really changed ok day one coverage continues day two tomorrow we have three days of wall-to-wall coverage day two and then finally day three Thursday here in San Francisco this is the cubes live coverage go to the cube dotnet to check out all the videos they're gonna be going up as soon as they are done live here and check out all the cube alumni and check out Silicon angle comm for all news coverage then of course you got tech reckoning Jon's company's the co-founder of for John Fourier and John Shroyer that's day one in the books thanks for watching see you tomorrow

live from the Fairmont Hotel in San Jose California it's the queue at big data sv 2015 hello everyone welcome back this is the cube our flagship program we go out to the events they strike this evil noise i'm john furrier we're here with IBM to talk about big data big data analytics and we're doing a first-ever crowd chat simulcast of live feed with IBM so guys we're going to try this out it's like go to crouch at dan / Hadoop next and join the conversation and our guests here Rob Thomas vice president product development big data analyst at IBM and beth smith general manager of IBM analytics platform guys welcome to welcome to the cube thank you welcome back and so IBM mostly we're super excited to next week as I was the interconnect you're bigger than you guys mashed up three shows for the mega shows and and Aerosmith's playing so it's going to say I'm from the Boston air so I'm really excited about you know Aerosmith and all the activities of social lounge and and whatnot but we've been following you guys the transformation of IBM is really impressive you guys certainly think a lot of heat in the press in terms of some of the performance size in the business but it's pumping right now you guys seem to have great positioning the stories are hanging together a huge customer base huge services so we're at the Big Data world which is tends to be startup driven from the past few years over the past phase one the big cuppies came in and started saying hey you know there's a big market our customers see demand and that so I got your take on on as we're coming in to interconnect next next week what is the perspective of big data asli Watson has garnered headlines from powering toys to jeopardy to solving huge world problems that's a big data problem you guys are not new to Big Data so when you look at this big data week here and Silicon Valley what's the take sure so I'll start often embedded Bethke night in so our big focus is how we start to bring data to the masses and we start to think in terms of personas data science and plays an increasingly important role around big data how people are accessing that the developer community and then obviously the line of business community which is the client set that I've been serving four years but the announcements that we've made this week around Hadoop are really focused on the first two personas in terms of data scientists how they start to get better value out of Hadoop leveraging different tools we'll talk about what some of those are and so we're really starting to change it about Hadoop results me about insight it's not about infrastructure infrastructure is interesting but it's really about what you're getting out of it so that's why we're approaching it that way it's how well it has naturally the IBM strategy around data cloud and engagement and data is really about using the insights which like Rob said it's about the value can get from the data and how that can be used in to transform professions and industries and I think when we bring it back to Big Data and the topic of a doob I think frankly it has gotten to a point that clients are really beginning to say it's time to scale they're seeing the value in the technology what it can bring how it gives them some diversity in their data and analytics platform and they're ready to announce scale on their workloads as a part of it so the theme is Hadoop next okay so that takes us right to the next point which is okay what's next is a phase one okay we got some base position validation okay this new environments customers don't want that so what so what is next i mean we're earring things like in memories hot aussie spark has proven that there's an action in member that that kind of says okay analytics at the speed of business is something that's important you guys are all over that and we've heard some things from you guys so so what's how do we get to the next part where we take Hadoop as an infrastructure opportunity and put it into practice for solutions at what what are the key things that you guys see happening that must happen for the large customers to be successful so I think that actually ties into the announcements we made this week around the open data platform because that's about getting that core platform to ensure that their standardization around it there's interoperability around it and then that's the base and that vendors and clients are coming together do that and to really enable and facilitate the community to be able to standardize around that then it's about the value on top of that around it etc it's about the workloads and what could be brought to bear to extend up that how do you apply it to real time streaming how do you add things like machine learning how do you deal with things like text analytics I mean we have a we have a client situation where the client took 4 billion tweets and were able to analyze that to identify over a hundred and ten million profiles of individuals and then by integrating and analyzing that data with the internal data sources of about seven or eight different data sources they were able to narrow into 1.7 million profiles that matched at at least ninety percent precision you know now they've got data that they can apply on buying patterns and stuff it's about that it's about going up the stack we're going to talk for hours my mind's exploding privacy creepy I mean a personas is relevant now you talk about personalization I mean collective intelligence has been an AI concepts we try not to be creepy okay cool but now so that brings us to the next level I mean you guys were talk about cognitives on that is a word you guys kick around also systems of engagement systems of records an old term that's been around in the old data warehousing dates fenced-off resources of disk and data but now with systems of engagement real-time in the moment immersive experience which is essentially the social and/or kind of mobile experience what does that mean how do you guys get there how do you make it so it's better for the users more secure or I mean these are hot button issues that kind of lead us right to that point so I'll take you to that a couple ways so so first of all your first question round head tube next so Hadoop was no longer just an IT discussion that's what I've seen changed dramatically in the last six months I was with the CEO of one of the world's largest banks just three days ago and the CEO is asking about Hadoop so there's a great interest in this topic and so so why so why would a CEO even care I think one is people are starting to understand the use cases of the place so that talks about entity extraction so how you start to look at customer records that you have internally in your systems are record to your point John and then you you know how do you match that against what's happening in the social world which is more or the engagement piece so there's a clear use case around that that changes how clients you know work with their with their customers so so that's one reason second is huge momentum in this idea of a logical data warehouse we no longer think of the data infrastructure as oh it's a warehouse or it's a database physically tied to something not tied to just what relational store so you can have a warehouse but you can scale in Hadoop you can provision data back and forth you can write queries from either side that's what we're doing is we're enabling clients to modernize their infrastructure with this type of a logit logical data warehouse approach when you take those kinds of use cases and then you put the data science tools on top of it suddenly our customers can develop a different relationship with their customers and they can really start to change the way that they're doing business Beth I want to get your comments we have the Crouch at crowd chat / a dupe next some commentary coming in ousley transforming industries billion tweets killer for customer experience so customer experience and then also the link about the data science into high gear so let's bring that now into the data science so the logical you know stores okay Nick sands with virtualization things are moving around you have some sort of cognitive engines out there that can overlay on top of that customer experience and data science how are they inter playing because this came out on some of the retail event at New York City that happened last week good point of purchase personalization customer experience hated science it's all rolling together and what does that mean unpack that for us and simplify it if you can oh wows complexing is a big topic you know it's a big topic so a couple of different points so first of all I think it is about enabling the data scientists to be able to do what they their specialty is and the technologies have advanced to allow them to do that and then it's about them having the the data and the different forms of data and the analytics at their fingertips to be able to apply that I the other point in it though is that the lines are blurring between the person that is the data scientist and the business user that needs to worry about how do they attract new customers or how do they you know create new business models and what do they use as a part of do you think we're also seeing that line blurring one of the things that we're trying to do is is help the industry around growing skills so we actually have big data University we have what two hundred and thirty thousand participants and this online free education and we're expanding that topic now to again go up the stack to go into the things that data scientists want to deal with like machine learning to go into things that the business user really wants to now be able to capture it's a part of it trying to ask you guys kind of more could be a product question and/or kind of a market question at IBM's Ted at IBM event in he talked about a big medical example in one of her favorite use cases but she made a comment in their active data active date is not a new term for the data geeks out there but we look at data science lag is really important Realty near real time is not going to make it for airplanes and people crossing the street with mobile devices so real real time means like that second latency is really important speed so active date is a big part of that so can you guys talk about passive active data and how that relates to computing and because it's all kind of coming to get it's not an obvious thing but she highlighted that in her presentation because I see with medical medical care is obviously urgent you know in the moment kind of thing so if you would what does that all mean I mean is that something custom Street paying attention to is it viable is it doable so certainly a viable I mean it's a huge opportunity and i'd say probably most famous story we have around that is the work that we did at the university of toronto at the Hospital for Sick Children where we were using real-time streaming algorithms and a real-time streaming engine to monitor instance in the neonatal care facility and this was a million data points coming off of a human body monitoring in real time and so why is that relevant I mean it's pretty pretty basic actually if you extract the data you eat yell it somewhere you load in a warehouse then you start to say well what's going on it's way too late you know we're talking about you know at the moment you need to know what's happening and so it started as a lot was in the medical field would you notice there's some examples that you mentioned but real time is now going well beyond the medical field you know places from retail at the point of sale and how things are happening to even things like farming so real time is here to stay we don't really view that as different from what I would describe as Hadoop next because streaming to me as part of what we're doing with a dupe and with spark which we'll talk about in a bit so it's certainly it is it is the new paradigm for many clients but it's going to be much more common actually if i can add there's a client North Carolina State University it's where I went to school so it's a if it's a client that I talk about a lot but they in addition to what they do with their students they also work with a lot of businesses own different opportunities that may that they may have and they have a big data and analytics sort of extended education business education project as a part of that they are now prepared to be able to analyze one petabyte in near real time so the examples that you and Rob talked about of the real world workloads that are going to exist where real time matters are there there's no doubt about it they're not going away and the technology is prepared to be able to handle the massive amount of data and analytics that needs to happen right there in real time you know that's a great exact point I mean these flagship examples are kind of like lighthouses for people to look at and kind of the ships that kind of come into the harbor if you will for other customers as you always have the early adopters can you guys talk about where the mainstream market is right now I'll see from a services standpoint you guys have great presence and a lot of accounts where are these ships coming into which Harper where the lighthouse is actually medical you mentioned some of those examples are bringing in the main customers is it the new apps that are driving it what innovations and what are the forces and what are the customers doing in the main stream right now where are they in the evolution of moving to these kind of higher-end examples so I mean so Hadoop I'd say this is the year Hadoop where clients have become serious about Hadoop like I said it's now become a board-level topic so it's it's at the forefront right now I see clients being very aggressive about trying out new use cases everybody really across every interest industry is looking for one thing which is growth and the way that you get growth if you're a bank is you're not really going to change your asset structure what you're going to change is how you engage with clients and how you personalized offers if your retailer you're not going to grow by simply adding more stores it might be a short term growth impact but you're going to change how you're engaging with clients and so these use cases are very real and they're happening now Hadoop is a bore group discussion or big day I just didn't see you formula we should have more Hadoop or is it you know I see I've seen it over and over again I'll tell you where you see a lot from his companies that are private equity-owned the private equity guys have figured out that there's savings and there's innovation here every company i worked with that has private equity ownership Hadoop is a boardroom discussion and the idea is how do we modernize the infrastructure because it's it's because of other forces though it's because of mobile it's because of cloud that comes to the forefront so absolutely so let's take Hadoop so I do bits great bad just great a lot of innovations going on there boardroom in these private equity because one they're cutting edge probably they're like an investment they want to see I realized pretty quickly now speed is critical right I would infer that was coming from the private equity side speed is critical right so speed to value what does that mean for ibn and your customers how do you guys deliver the speed to value is that's one of the things that comes out on all the premises of all the conversations is hey you can do things faster now so value on the business side what do you guys see that sure so a a lot of different ways to approach that so we believe that as I said when I said before it's not just about the infrastructure it's about the insight we've built a lot of analytic capabilities into what we're doing around a dupe and spark so that clients can get the answers faster so one thing that we're going to be we have a session here at strata this week talking about our new innovation big R which is our our algorithms which are the only our algorithms that you can run natively on Hadoop where your statistical programmers can suddenly start to you know analyze data and you know drive that to decision make it as an example so we believe that by providing the analytics on top of the infrastructure you can you can change how clients are getting value out of that so how do we do it quickly we've got IBM SoftLayer so we've got our Hadoop infrastructure up on the cloud so anybody can go provision something and get started and ours which is not something that was the case even a couple years ago and so speed is important but the tools and how you get the insight is equally important how about speed 22 value from a customer deployment standpoint is it the apps or is it innovating on existing what do you sing well I think it's both actually um and and so you talked earlier about system of engagement vs system of record you know and I think at the end of the day for clients is really about systems of insight which is some combination of that right we tend to thank the systems of engagement or the newer things and the newer applications and we tend to thank the systems of record are the older ones but I think it's a combination of it and we see it show up in different ways so I'll take an example of telco and we have a solution on the now factory and this is now about applying analytics in real time about the network and the dynamics so that for example the operator has a better view of what's happening for their customers they're in users and they can tell that an application has gone down and that customers have now switched all of a sudden using a competitive application on their mobile devices you know that's different and that is that new applications or old or is it the combination and I think at the end of the day it really comes to a combination I love these systems of insight i'm just going to write that down here inside the inside the crowd chat so i got to talk about the the holy grail for big data analytics and big data from your perspective ideas perspective and to where you guys are partnering I'll see here there's a show of rich targets of a queue hires acquisitions partnerships I mean it's really a frill ground certainly Silicon Valley and and in the growth of a big data cloud mobile and social kind of these infrared photography biz is a message we've heard so what is the holy grail and then what are you guys looking for in partnerships and within the community of startups and or other alliances sure you want to start with the Holy Grail me yeah so so you know I think at the end of the day it is about using technology for business value and business outcome I you know I really think that's what said the spirit of it and so if I tell you why we have for example increased our attention and investment around this topic it's because of that it's because of what Rob said earlier when he said the state that clients are now in um so that's what I think is really important there and I think it's only going to be successful if it's done based own standards and something that is in support of you know heterogeneous environments I mean that's the world of technology that we live in and that's a critical element of it which leads to why we are a part of the Open Data Platform initiative so on the on the the piece of analytics I was just cus our comment about our for example I was just mentioning the crowd chat I had Microsoft just revolution analytics which is not our which is different community is there a land-grab going on between the big guys of you know IBM's a big company what do you guys see in that kind of area terms acquisition targets yeah man I think the numbers would say there's not a land-grab I don't think the MMA numbers have changed at a macro level at all in the last couple years I mean we're very opportunistic in our strategy right we look for things that augment what we do I think you know it's related to partner on your comment your question on partnering but we do acquisitions is not only about what that company does but it's about how does it fit within what IBM already does because we're trying to you know we're going after a rising tide in terms of how we deliver what clients need I think some companies make that mistake they think that if they have a great product that's relevant to us maybe maybe not but it's about how it fits in what we're doing and that's how we look at all of our partnerships really and you know we partner with global systems integrators even though we have one with an IBM we partner with ISVs application developers the big push this week as I described before is around data scientists so we're rolling out data science education on Big Data university because we think that data scientists will quickly find that the best place to do that is on an IBM platform because it's the best tools and if they can provide better insight to their companies or to their clients they're going to be better off so I was so yes that was the commenting on and certainly the end of last week and earlier this week about that Twitter and it's a lot of common in Twitter's figured out and people are confused by Twitter versus facebook and I know IBM has a relation but we're so just that's why pops in my head and I was are saying HP Buddha's got a great value and so I was on the side of Twitter's a winner i love twitter i love the company misunderstood certainly i think in this market where there's waves coming in more and more there's a lot of misunderstanding and i think i want to get your perspective you can share with the folks out there what is that next way because it's confusing out there you guys are insiders IBM i would say like twitter is winning doing very well certainly we're close to you guys we are we're deeply reporting on IBM so we can see the momentum and the positioning it's all in line what we see is that is where the outcomes will end up being for customers but there's still a lot of naysayers out there certainly you guys had your share as as to where's as an example so what is the big misunderstanding that you think is out there around the market we're in and what's the next wave as always waves coming in if you're not out in front that next wave usually driftwood as the old expression goes so what is that big misunderstanding and this kind of converged from a hyper targeted with analytics this is all new stuff huge opportunities huge shifts and inflection point as Bob picciano said on the cube is its kind of both going on the same time shift and it point so what's misunderstood and what's that next big waves so let me start with the next big way is that I'll back into the misunderstanding so the next big wave to me is machine learning and how do you start to take the data assets that you have and through machine learning and the application of those type of algorithms you start to generate better insights or outcomes and the reason i think is the next big wave is it's it may be one of the last competitive motes out there if you think about it if you have a a corpus of data that's unique to you and you can practice machine learning on that and have that you know either data that you can sell or to feed into your core business that's something that nobody else can replicate so it becomes incredibly powerful so one example I'll share with you and I want to bring you my book but it's actually not getting published next week since so maybe next week but so Wiley's publishing a book I wrote and one of the examples I give is a company by the name of co-star which I think very few people have heard of co-star is in the commercial real estate business they weren't even around a decade ago they have skyrocketed you know from zero to five hundred million dollars in revenue and it's because they have data on four million commercial properties out there who else has that absolutely nobody has that kind of reach and so they've got a unique data asset they can apply things like machine learning and statistics to that and therefore anybody who wants to do anything commercial real estate has to start with them so I pointed you're starting to get the point where you have some businesses where data is the product it's not an enabler it's the actual product I think that's probably one of the big misunderstandings out there is that you know data is just something that serves our existing products or existing services we're moving to a world where data is the product and that's the moat I wrote a post in 2008 called data is the new development kit and what you're basically saying is that's the competitive advantage a business user can make any innovation observation about data and not be a scientist and change the game that's what you were saying earlier similar right that's right okay so next big wave misunderstanding what do you wait bet what's your take on what are people not getting what is Wall Street what is potential the VCG really on the front end of some of the innovation but what is the general public not getting I mean we are in shift and an inflection what's it what's the big shift and misunderstanding going on so so I I would tend to you know actually agree with with Rob that I think folks aren't yet really appreciating and I guess I would twist it a little bit and say the insight instead of just the data but but they're not realizing what that is and what it's going to give us the opportunity for you know I would retire early if I actually could predict everything that was going to happen but but you know yeah but if you think about it you know if you think about you know mid to late 90s and what we would have all fault that the internet was going to allow us to do compared to what it actually allowed us to do is probably like night and day and I think the the time we're in now when you take data and you take mobility and you take cloud and you take these systems of engagement and the fact the way people individuals actually want to do things is is similar but almost like on steroids to what we were dealing with in the mid-90s or so and so you know the possibilities are frankly endless and and I think that's part of what people aren't necessarily realizing is that they have to think about that insight that data that actually has some value to it in very different ways there's a lot of disruptive enablers out Dunham's there's a lot to look at but finding which ones will be the biggest right it's hard I mean you get paid a lot of money to do that is if you can figure it out and keep it a secret um but you didn't you machine learning is now out there you just shared with us out competitive advantage so everyone knows know everyone kind of new kind of in the inside but but not everybody's using it right i mean i think another example a company like into it has done a great job of they started off as a software company they've become a data company i think what you what i've observed in all these companies is you can build a business model that's effectively recession proof because data becomes the IP in the organization and so I don't I actually you know I think for us those are the live in the world we this is well understood I don't think it's that well understood yet yeah insiders mic right and you know when we first started doing big data research and working with thousands of clients around the world there were there were six basic use cases it started of course with the customer the the end customer and the customer 360 and that sort of thing and went through a number of different things around optimization etc but the additional one is about those new business models and you know that is clearly in the last 12 to 18 months has become a lot more of what the topic is when I'm talking to clients and I think we will see that expand even more as we go in the future we've a lot of activity on the crowd chatter crowd chatter net / Hadoop necks and I'll mentioned we can probably extend time on that if you guys want to keep it keep it going conversation is awesome and we did getting the hook here so we'll remove the conversation to crouch at totnes Esther Dube next great thought leadership and I can go on this stuff for an hour you guys are awesome great to have you on the cube and so much to talk about a lot of ground will certainly see it in to connect go final question for you guys is what do you guys see for this week real quick summarize what do you expect to see it unfold for a big data week here at Silicon Valley Big Data asked me so I think you know a lot of the what we talked about machine learning is going to be a big topic I think there'll be a lot of discussion around the open data platform that Beth mentioned before it's a big move that we made along with another group supporting the apache software foundation I think that that's a big thing for this week but it should be exciting alright guys thanks for coming out to be IBM here inside the cube we're live in Silicon Valley would be right back with our next guest after the strip break I'm Jennifer this is the cube we write back