>>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.