>>The Cube Presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. We're back. David Ante with David Nicholson. This is UiPath Forward five from Las Vegas. We're live, you know, the customers here, they're automating all the time, sucking work and the cube. We're sucking all the information out of the experts and the customers. A bar Toban is here. He's the global business, Shared services, leading automation and AI at PepsiCo. And Para Colan is back is the chief, He's the chief product officer, UiPath longtime Cube alum. Great to see you guys. Thanks for coming on. Great to see us all day. So you guys keynote today, you know, excited to have PepsiCo on. I'm not sure I've ever interviewed PepsiCo in the Cube, but tell us about your role there. >>Absolutely. So I'm part of a PepsiCo global business shared services team. I lead automation and AI capabilities. GBS has, you know, we started GBS portfolio back about three and a half years ago, and we have a six hubs across PepsiCo. And as, as a part of my role, we deliver transformational capability across the PepsiCo. >>When did it all start? >>About three and a half years ago, 2019. So >>Prior to the pandemic. Yeah. You know, versus the pandemic was a catalyst for this. Yeah. But it was at the catalyst, but maybe it sped it up a bit. Yeah. >>PepsiCo journey started with, if, if you look at the PepsiCo, you know, the automation journey, it started back in 2017, but the GBS portfolio took shape back in 2019. So prior to that, you know, PepsiCo was definitely, you know, working on lot of, you know, the automation capabilities and automation product across, you know, PepsiCo. But with the introduction of PepsiCo global business shared services team, we are, you know, centralizing a lot of transformation capability, you know, across the functions that, that we support within the >>PepsiCo and, and UI path. Was going to part of that journey all along? Or was there sort of other activities beforehand or how >>No, no, absolutely. Starting from 2017, if I, you know, remembered, you know, with the vision of our, you know, some of our senior leadership team and recognizing the value of, you know, automation in the core, you know, capability as a transformation at that time, you know, we started with just like anybody else, right? We started with, you know, proof of concept, showed some, you know, early wins and the value back to the business, start setting up some, you know, business processes and capabilities, stood up the platform, build a complete, you know, ecosystem around that, you know, platform and partnership with, you know, UI bot team. And you know, from there, here we are five years. I mean, it's, it's a, it's a, it's a, it's a very critical component to our digital transformation capability and, and yes, leverage across >>Let's talk platform. So you, you guys have made some announcements this week. You talk about the business automation platform. I remember our first forward was, you know, RPA tool. Okay. Yeah. And then you guys made acquisitions. I was there for that. So the process process cold and then people started to really expand it and it's really come in amazingly long way in a short time. So what did you guys announce today? What'd you talk about on stage 20, 22, 10? Tell us more about it. >>Absolutely, Dave. So you've seen the journey, you've been with us since the early days. You know, we were in 2017 and RPA tool that could automate a representative task that happened over and over again in the environment. And then three years ago you were here when we announced the automation platform, we said, it's not just about a task, it's about involving humans in bots to manage end to end processes. It's about discovering what automation opportunities exist. It's about using ai. Pepsi Co was actually the pioneer of using AI along with automation. You know, we were in stage together with them in, in 2019. And where we are now is we're essentially seeing people want to take the next step with automation. They're saying that it's no longer just an automation tool, It's the way we operate. It's the way we innovate in the organization. So they're really making sure that it becomes a part of their digital transformation journey that they're on. >>And they're saying that we can do the digital transformation by consolidating multiple DRP systems and CRM systems. And that'll take us seven years to do, or we can go with UI path and we can leverage the core that we can leverage the GL system that exists today. We can leverage inventory tracking system that exists today and start to build processes on top of that that can adapt to what customers are trying to do in this digital age. And that's where, you know, we've made announcements today is, is really pivot the platform to be a business automation platform. And there's sort of three layers, you know, unique but you know, connected layers of the platform. The first one is discover. And Discover is all about finding your processes, identifying the opportunities, making sure that you are managing the return on investment. What is the process? You know, how are you getting ROI on it? >>The second one is automated, and that is really where we're applying semantic automation to identify the digital building blocks of an enterprise, which is your data, your document, your screens and communication. Like putting all of that together and saying you can automate our processes, leveraging a lot of intelligence that exist in how business processes are done. And the last one is operate, which is if you're trying to execute a business process at scale, you're processing not just, you know, a task thousand times, but you are fulfilling millions of transactions. You're, you know, you're looking at trillions of records to identify what processes you need. A scalable enterprise platform that's able to ingest a lot of data, report on metrics, reporting efficiency. So that's what we've announced today is an automation platform that companies can use to put at the center of the digital transformation >>Journey. So I about the interesting thing about PepsiCo, you guys started in 2017. Yeah. So kind of early, early on. Yeah. Yeah. And you kind of been there with the progression platform. So my question to you is end up, it was, you know, we've seen the e from primarily on-prem, now it's cloud first. Yeah. How disruptive or non disruptive was that for you? Did you have to rip and replace? Did you have to sort of retool or migrate? What was that like? >>No, I mean, significant disruption, right? I mean, I mean, as, as we started our journey back in 2017, just like, you know, PRM mentioned, right? With simple rule based, you know, the automation from then now to our journey where our continue to, you know, infuse, you know, AI capability, document understanding, conversation ai, right? As a part of our end to end portfolio. At the same time, I think the cloud is providing a fantastic opportunity for us to continue to scale, right? You know, scale at, at a large. So that I think is a fantastic, you know, fantastic platform and fantastic, you know, the opportunity that we are looking forward >>To know. So how do you affect adoption inside of the organization? Can you talk about that? What's working? What's, >>It's always value driven as you know, right? I mean, the business business has to see the value. It it, it was, I mean, I would, you know, admit it was not as easy as before, but as the mindsets have started to shift, right? As the people have started to realize the value that, you know, the automation brings to, you know, the, I mean, you know, not just the, the value for the business, but actually transforming the entire portfolio, right? And, and people have started to see now that not every automation project is going to be transformation product, but for every transformation project you will find the automation at the heart and the core of it. So I, I, I think that's what has started to shift the mindset of, of uniforms. >>So how do you know when you have end to end? What are you wake up one day and say, Wow, we've achieved it. You know, is it pieces that come together? Yeah. What do you say? >>Yeah, You know, we wanna look at customers from, you know, from an end to end perspective. It's not just about piecemealing mealing finding a problem, solving it, really what does it deliver from, from an end to end perspective. Did you actually, you know, because a lot of times companies will say, we wanna automate X number of processes, and, and they do that and they're like, Well, we've automated a lot of processes. We're not sure what value we're getting out of it. It's the ability to measure like, what impact is this automation having on your business from an operational metric, but from a business metric as built. But then going back and saying, Well, where is the biggest pain point? Where do we have the largest value that we can give to the business back? So one of the things we actually announced today is the ability to take at an look at an idea and look at what was the estimated benefits of that idea, and then map it all the way through execution to say, what are we getting? >>We estimated we were gonna save a million dollars by doing those automation, or what have we achieved till now? Have we achieved a million dollars? Have we achieved half a million dollars by having achieved? That's true. That never happens. That, and, and, and, and it's hard to do that, like the data existed, but it's really hard for people to pull that data out. So we build out the box dashboards that give you the ROI bag, and that's why it's really important to, to make sure that, you know, you look at it not just as a technology project, but more as a investment from a business side. And so you can making a business more efficient. Yeah, >>That's, I just, I know you were jumping in, but that's super important. Cause you know, you run a lot of projects. Yeah, absolutely. And each of those projects has zone roi, then you jam it into the application portfolio. Exactly. And then everybody sort of forgets about it. You can't really track what impact it had because there's always, you know, some things that are benefit, some things are sometimes a negative. And so it's that holistic picture that you >>Trying to achieve, extremely critical point, what you hit on, right? From it's measuring the benefit and measuring the continuous benefit across, and not just from start and end, Okay, what I promised I delivered or not, but, but you have to have this continuous mindset. And so I think Yeah, definitely that that's a very, very critical to our finance team and our cfo, >>They organic mechanisms. It's constantly >>Evidence. Absolutely. Yeah. So abar, yeah. Global business shared services. Yeah. When you think of PepsiCo, yeah, of course people immediately think of Sure, Pepsi. But PepsiCo is a multi tentacled absolutely beast of a company. Absolutely. In a good way. Yeah. For organizations that are in that same category, holding companies, companies that have all sorts of different entities that are working together under one umbrella, how shareable is this idea of automation and business automation process moving forward? How, how shareable is that on the share oter? Yeah. Yeah. As far as, as far as, as far as you're concerned, are you, are you talking to some people where you're saying, Hey, I'm here, I'm here from GBS and I'm here to help, and they look at you like you're crazy because you don't understand their business? Or is this something that relatively easily applies across businesses >>That No, to your point, I mean, very valid point, right? I mean, it's, that's, that's the gbs, global business shared services mindset, right? As you move the functional areas into the Pepsi, into the Pepsi, gbs, like hr, procurement, commercial sales, supply chain, right? That's where you wanna start to find those, you know, the optimization, you know, opportunity. You wanna start to ize your processes, and that's where you will, you know, as you transition this processes within the gbs, that's what create those, you know, opportunities for you. So >>What, >>What about automation opportunities? Not in the sh I know you're in the shared arena. Yeah, yeah. But each of those business units has processes that could probably be optimized and automated. Sure. Is that something that's under your purview? We've heard, we've heard a lot about citizen developers. Yeah. I don't know if that, if that >>Applies to No, that definitely. I mean, you cannot just have focus on end to end, you know, automation. I mean, that's, that's a huge portfolio for gps at the same time supporting, you know, automation through the citizen development capability. That that's where, once again, you know, you have not provided a lot of capability and solution tools that we use, right? To continue to empower the folks who are part of our, you know, GBS team inside or outside gbs, right? It, it, it's, I think it's very, very critical. It, it, it helps people transform their career even in one ways, right? And, and, and, and you have that muscle, you have that resource, and you have the power. You definitely want to utilize that. >>So let's talk about metrics for a minute. So more data, the better. Usually I like data. Yeah. But, but if you're trying to optimize for 15 metrics, I feel like you're not gonna optimize on any, So how do you deal with that from both, as par was saying, an operational standpoint and a business standpoint? What are the things about how do you sort of get the, the teams focused on the right things? >>B business, functional leadership team drive those alignment for us as a part of a global business, shared services, we, we are hip to have connected with our business, you know, functions, right? They, they have to help us prioritize those. And to your point, I mean, yeah, you cannot attack 15 metrics at once. You have to prioritize, you have to make sure that you bring the focus to the product, you know, project, right? So, so definitely, I mean, it's, it's, it's not often 15 metrics, but top three metrics, let's, let's focus, let's zoom in and ensure we are driving it. But, >>And if you think about the system, I mean, at the end of the day, the p and l manager, he or she cares about ebit, let's say. Sure, okay. But there are so many factors, you know, in that complicated organization that are gonna affect ebitda and they're gonna be different. But somebody's gotta figure out, okay, how do they fit together in a system? And can, can UiPath help me understand that, those relationships and those dependencies? >>Absolutely. I mean, I think there's a, there's an aspect of human relationships and, and making sure that you get the right level of sponsorship from the business and, and there's a business stakeholder and, and looking at every investment and, and outcomes that you're driving based on that. But, but that is something that we, from a tools perspective, we're trying to make sure that you can measure the value throughout the entire value chain. But then getting the business sponsorship, like where we've seen automation scale is always because there's a business sponsor that's essentially saying, Here's what I'm trying to achieve and here's the, here's my goal, here's a North star and go get it and let me know how you're tracking against it. And, and our job is to make sure that we can provide the visibility, the people that are operating the, the programs to make sure they get that level of visibility. >>What's the scope of automations in your, you know, organization? Is it dozens, hundreds, >>Huge. >>That is thousands. >>We are getting there. Okay. No, definitely. I mean, we have definitely, you know, realized that it's, it's a core component to our digital transformation, right? So, so there is no, there's no stopping on it. There, there, there, there's plenty of support from top down and you know, it's a fantastic time to be at PepsiCo. Right? Especially at the PepsiCo gbs. Right, >>Right. Thanks for sharing your story. Congratulations on all the progress you guys have made. It's actually quite remarkable to see where you guys have come from. So I really appreciate it. Thank you, Dave. Thanks. Thank you Dave. Okay. Thank you for watching. This is Dave Ante for Dave Nicholson. We are right middle of day two at forward five from Las Vegas. We're live, we're right back.

>>The Cube Presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. We're back. David Ante with David Nicholson. This is UiPath Forward five from Las Vegas. We're live, you know, the customers here, they're automating all the time, sucking work and the cube. We're sucking all the information out of the experts and the customers. A bar Toban is here. He's the global business, Shared services, leading automation and AI at PepsiCo. And Para Colan is back is the chief. He's the Chief product officer at UiPath, longtime Cube alum. Great to see you guys. Thanks for coming on. Great to see us all day. So you guys keynote today, you know, excited to have PepsiCo on. I'm not sure I've ever interviewed PepsiCo in the Cube, but tell us about your role there. >>Absolutely. So I'm part of a PepsiCo global business shared services team. I lead automation and AI capabilities. GBS has, you know, we started GBS portfolio back about three and a half years ago, and we have a six hubs across PepsiCo. And as, as a part of my role, we deliver transformational capability across the PepsiCo. >>When did it all start? >>About three and a half years ago, 2019. So >>Prior to the pandemic. Yeah. You know, versus the pandemic was the catalyst for this. Yeah. But it was at the catalyst, but maybe it sped it up a bit. >>Yeah. PepsiCo journey started with, if, if you look at the PepsiCo, you know, the automation journey, it started back in 2017, but the GBS portfolio took shape back in 2019. So prior to that, you know, PepsiCo was definitely, you know, working a lot of, you know, the automation capabilities and automation product across, you know, PepsiCo. But with the introduction of PepsiCo global business shared services team, we are, you know, centralizing a lot of transformation capability, you know, across the functions that, that we support within the >>PepsiCo and, and UI path was kind of part of that journey all along? Or was there sort of other activities beforehand or how did that >>No, no, absolutely. Starting from 2017, if I, you know, remembered, you know, with the vision of our, you know, some of our senior leadership team and recognizing the value of, you know, automation in the core, you know, capability as a transformation at that time, you know, we started with just like anybody else, right? We started with, you know, proof of concept, showed some, you know, early wins and the value back to the business, start setting up some, you know, business processes and capabilities, stood up the platform, build a complete, you know, ecosystem around that, you know, platform partnership with, you know, UI bot team. And you know, from there, here we are five years. I mean, it's, it's a, it's a, it's a, it's a very critical component to our digital transformation capability and, and yes, leverage across >>Let's talk platform probably. So you, you guys have made some announcements this week. You talk about the business automation platform. I remember our first forward was, you know, RPA tool. Okay. Yeah. And then you guys made acquisitions. I was there for that. So the process process cold and then people started to really expand it, and it's really come in amazingly long away in a short time. So what did you guys announce today? What'd you talk about on stage 2022 dot 10? Tell us more about it. >>Absolutely, Dave. So you've seen the journey, you've been with us since the early days. You know, we were in 2017 and RPA tool that could automate a representative task that happened over and over again in the environment. And then three years ago you were here when we announced the automation platform, we said, it's not just about a task, it's about involving humans in bots to manage end to end processes. It's about discovering what automation opportunities exist. It's about using ai. Pepsi Co was actually the pioneer of using AI along with automation. You know, we were in stage together with them in, in 2019. And where we are now is we're essentially seeing people want to take the next step with automation. They're saying that it's no longer just an automation tool, It's the way we operate. It's the way we innovate in the organization. So they're really making sure that it becomes a part of their digital transformation journey that they're on. >>And they're saying that we can to the digital transformation by consolidating multiple RP systems and CRM systems. And that'll take us seven years to do, or we can go with UI path and we can leverage the core that we can leverage the GL system that exists today. We can leverage the inventory tracking system that exists today and start to build processes on top of that that can adapt to what customers are trying to do in this digital age. And that's where, you know, we've made announcements today is, is really pivot the platform to be a business automation platform. And there's sort of three layers, you know, unique but you know, connected layers of the platform. The first one is discover. And Discover is all about finding your processes, identifying the opportunities, making sure that you are managing the return on investment. What is the process? >>You know, how are you getting ROI on it? The second one is automated, and that is really where we're applying semantic automation to identify the digital building blocks of an enterprise, which is your data, your document, your screens and communication. Like putting all of that together and saying you can automate in our processes, leveraging a lot of intelligence that exist in how business processes are done. And the last one is operate, which is if you're trying to execute a business process at scale, you're processing not just, you know, a task thousand times, but you are fulfilling millions of transactions. You're, you know, you're looking at trillions of records to identify what processes you need, a scalable enterprise platform that's able to ingest a lot of data report on metrics report and efficiency. So that's what we've announced today is an automation platform that companies can use to put at the center of the digital transformation journey. >>So like about the interesting thing about PepsiCo, you guys started in 2017. Yeah. So kind of early, early on. Yeah. Yeah. And you kind of been there with the progression of platform. So my question to you is, and it was, you know, Yeah, we've seen the e from primarily on-prem now it's cloud first. Yeah. How disruptive or non disruptive was that for you? Did you have to rip and replace? Did you have to sort of retool or migrate? What was that like? >>No, I mean, significant disruption, right? I mean, I mean, as, as we started our journey back in 2017, just like, you know, PRM mentioned, right? With simple rule based, you know, the automation from then now to our journey where our continue to, you know, infuse, you know, AI capability, document understanding, conversation ai, right? As a part of our end to end profile. At the same time, I think the cloud is providing a fantastic opportunity for us to continue to scale, right? You know, scale at, at large. So that I think is a fantastic op, you know, fantastic platform and fantastic, you know, the opportunity that we are looking forward >>To. So how do you affect adoption inside of the organization? Can you talk about that? What's working? What's, >>It's always value driven as you know, right? I mean, the business business has to see the value. It it, it was, I mean, I would, you know, admit it was not as easy as before, but as the mindsets have started to shift, right? As the people have started to realize the value that, you know, the automation brings to, you know, the, I mean, you know, not just the, the value for the business, but actually transforming the entire portfolio, right? And, and people have started to see now that not every automation project is going to be transformation product, but for every transformation project you will find the automation at the heart and the core of it. So I, I, I think that's what has started to shift the mindset of, of uniforms. >>So how do you know when you have end to end? What are you still wake up one day and say, Wow, we've achieved it. You know, is it pieces that come together? Yeah. What do you say? >>Yeah, You know, we wanna look at customers from, you know, from an end to end perspective. It's not just about piecemealing finding a problem, solving it, really what does it deliver from, from an end to end perspective. Did you actually, you know, because a lot of times companies will say, we wanna automate X number of processes, and, and they do that and they're like, Well, we've automated a lot of processes. We're not sure what value we're getting out of it. It's the ability to measure like, what impact is this automation having on your business from an operational metric, but from a business metric as well. But then going back and saying, Well, where is the biggest pain point? Where do we have the largest value that we can give to the business back? So one of the things we actually announced today is the ability to take at an look at an idea and look at what was the estimated benefits of an idea, and then map it all the way through execution to say, what are we getting? >>We estimated we were gonna save a million dollars by doing those automation, or what have we achieved till now? Have we achieved a million dollars? Have we achieved half a million dollars by having achieved? That's, that never happens. That, and, and, and, and it's hard to do that, like the data existed, but it's really hard for people to pull that data out. So we build out the box dashboards that give you the ROI bag. And that's why it's really important to, to make sure that, you know, you look at it not just as a technology project, but more as a investment from a business side. And so you can, making a business more efficient. You >>Know, that's, I just, I know you were jumping in, but that's super important. Cause you know, you run a lot of projects Absolutely. And each of those projects has zone roi, then you jam it into the application portfolio. Exactly. And then everybody sort of forgets about it. You can't really track what impact it had because there's always, you know, some things that are benefit, some things are sometimes a negative. And so it's that holistic picture that >>You trying >>To achieve, extremely critical point, what you hit on, right? From it's measuring the benefit and measuring the continuous benefit across, and not just from start and end, Okay, what I promised I delivered or not, but, but you have to have this continuous mindset. And, and so I think yeah, definitely that, that's a very, very critical to our finance team in our cfo, >>Organiza, they're organic mechanisms and it's constantly >>Absolutely. Yeah. So abar, yeah. Global business shared services. Yeah. When you think of PepsiCo, yeah, of course people immediately think of Sure, Pepsi. But PepsiCo is a multi tentacled absolutely beast of a company. Absolutely. In a good way. Yeah. For organizations that are in that same category, holding companies, companies that have all sorts of different entities that are working together under one umbrella, How shareable is this idea of automation and business automation process moving forward? How, how shareable is that on the share oter? Yeah. Yeah. >>As >>Far as, as far as, as far as you're concerned, are you, are you talking to some people where you're saying, Hey, I'm here, I'm here from gvs and I'm here to help, and they look at you like you're crazy because you don't understand their business? Or is this something that relatively easily applies across >>Businesses that No, to your point, I mean, very valid point, right? I mean, it's, that's, that's the gbs, global business shared services mindset, right? As you move the functional areas into the Pepsi, in, into the PepsiCo gbs like hr, procurement, commercial sales, supply chain, right? That's where you gonna start to find those, you know, the optimization, you know, opportunity. You wanna start to standardize your processes, and that's where you will, you know, as you transition this processes within the gbs, that's what create those, you know, opportunities for you. >>What, >>What, what about automation opportunities? Not in the, I know you're in the sharing arena. Yeah, yeah. But each of those business units has processes that could probably be optimized and automated. Sure. Is that something that's under your purview? We've heard, we've heard a lot about citizen developers. Yeah. I don't know if that, if that >>Applies to No, that definitely. I mean, you cannot just have focus on end to end, you know, automation. I mean, that's, that's a huge portfolio for gps at the same time supporting, you know, automation through the citizen development capability. That that's where, once again, you know, you have had, provides a lot of capability and solution tools that we use, right? To continue to empower the folks who are part of our, you know, GBS team inside or outside gbs, right? It, it's, I think it's very, very critical. It, it, it helps people transform their career even in one ways, right? And, and, and, and you have that muscle, you have that resource, and you have that power. You definitely want to utilize that. >>So let's talk about metrics for a minute. So more data the better. Usually I like data. Yeah. But, but if you're trying to optimize for 15 metrics, I feel like you're not gonna optimize on any, So how do you deal with that from both as Paramo saying an operational standpoint and a business standpoint? What are the things about how do you sort of get the, the teams focused on the right things, >>Bi business, functional leadership team drive those alignment for us as a part of a global business, shared services, we, we are hip to have connected with our business, you know, functions, right? They, they have to help us prioritize those. And to your point, I mean, yeah, you cannot attack 15 metrics at once. You have to prioritize, you have to make sure that you bring the focus to the product. You have a project, right? So, so definitely, I mean, it's, it's, it's not often 15 metrics, but top three metrics, let's, let's focus, let's zoom in and ensure we are driving it. But then >>If you think about the system, I mean, at the end of the day, the p and l manager, he or she cares about ebit, let's say. Sure, okay. But there are so many factors, you know, in that complicated organization that are gonna affect ebitda. Yeah. And they're gonna be different. Yeah. But somebody's gotta figure out, okay, how do they fit together in a system? And, and can, can UiPath help me understand that, those relationships and those dependencies? >>Absolutely. I mean, I think there's a, there's an aspect of human relationships and, and making sure that you get the right level of sponsorship from the business and, and there's a business stakeholder and, and looking at every investment and, and outcomes that you're driving based on that. But, but that is something that we, from a tools perspective, we're trying to make sure that you can measure the value throughout the entire value chain. But then getting the business sponsorship, like where we've seen automation scale is always because there's a business sponsor that's essentially saying, Here's what I'm trying to achieve and here's the, here's my goal, here's the North star and go get it and let me know how you're tracking against it. And, and our job is to make sure that we can provide the visibility, the people that are operating the, the programs to make sure they get that level of visibility. >>What's the scope of automations in your, you know, organization? Is it dozens, hundreds, huge. That is thousands. >>We are getting there. >>Okay. >>No, definitely. I mean, we have definitely, you know, realized that it's, it's a core component to our digital transformation, right? So, so there is no, there's no stopping. I mean there, there, there, there's plenty of support from top down and you know, it's a fantastic time to be at PepsiCo. Right? Especially at the PepsiCo ubs, Right. >>So, Right. Thanks for sharing your story, Pam. Congratulations on all the progress you guys have made. It's actually quite remarkable to see where you guys have come from. So I really appreciate it. Thank you Dave. Thank you Dave. Okay. Thank you for watching. This is Dave Ante for Dave Nicholson. We are right middle of day two at forward five from Las Vegas. We're live, we're right back.

[Music] from the cube studios in Palo Alto in Boston it's the cube covering the IBM think brought to you by IBM welcome back to the cubes coverage of IBM think 2020 the digital version of IBM think Bill Smith is here he's the general manager of IBM Global Financing bill thanks for coming on thank you very much for having me up I'm looking forward to it yeah me too so you know I remember the days of the the glory days of IBM you know leasing I used to run the leasing program for a couple of years at IDC and it was just it was an awesome time but things have changed a lot I mean iBM has really transformed its financing army what do we need to know about today's IBM Global Financing well some things are still saying but as you said a lot is different we constantly are celebrating our 40th anniversary this year a big part of our business is now software and services financing a lot of project man Singh we still do a lot of hardware business but it's a much much smaller portion of our thirty billion dollar asset base so it's a great business it was a great business back then when you were involved in it the very profitable and and interesting business today as it was then as I said big difference though a lot of software and services yeah well I've of course I would have mentioned that most if not all mainframes are still leased but now you've expanded it to many many more areas what can you tell us about you know some of the financial metrics you know what's the profile of the business look like yeah sure it's a it's a big business it looks a lot like a bank and we're around 30 billion in asset we do business and you know 40 plus countries around the world 26% return on equity most of the portfolio's very high percentage of that portfolio is investment rate so a couple other key metrics is we we actually issue our own debt we became an SCC registrant a couple years ago we have a you know many debt holders we only have one owner and one equity owner and that's IBM it's a very good business but 2% of IBM's revenue but about 10% of IBM's from yeah well so now this is an important aspect that I want to join to it when people you know look at the IBM balance sheet they'll you know go out or whatever Yahoo Finance and say oh my gosh look at all this debt must be you know I know of course the redhead acquisition is part of that but you're carrying a lot of the debt as part of the financing operation but people need to understand it's a very profitable and very high quality debt and if we could just address that one of the big benefits to becoming an SCC registrant is the amount of transparency that we were able to provide the investors so unlike other captive financing companies they just get rolled in to different units or parts of the books you know we actually report in the segment reporting every quarter we certify just like they you know public company would we're still a wholly owned subsidiary but the level of transparency is really great for the investors which is why you know debt holders were able to Willington by our paper it's still a very client based business we do very specialized structures we only do business and NIT as I told the board many times I'd be on board many times we don't do planes trains and automobiles we only do we only do I see and and really you know 99 percent of our businesses is IBM only so you talked about branching into software and services I'm interested in how the the client base has has transformed as a result of that sure you know there's a lot of digital transformations going on there's still a lot of ERP implementations around the world very large project so we we described it as project financing so if client will come to us and say bill we'd like to match the benefit of this very large GBS or services engagement that the IBM team is leading we like to match the benefit when we have the cash outlay so we'll put a structure together that will delay the payment for when those benefits begin to come online for the enterprise and then match payment with when benefits are actually received it's proven to be a very very effective financing instrument for us but highly effective economic instruments for the clients also gives if I'm you know contracting with IBM services you've got a major incentive for the services organization to deliver value as soon as possible and that aligns everybody doesn't it it absolutely does you know we have a lot of business partners where they'll do similar structures as well so other integrators you know if the redhead acquisition and and clients moving to a hybrid cloud model sometimes there's a migration that will take place between the traditional legacy systems and when they move that cloud well that bubble of been we take Dera so will will finance that migration effort for the client and again to match their cash outlays with when they receive the benefit that I've left from that cloud migration in the day there were tons of leasing companies who would take the risk and predict the residual values and then they'd take the paper and and and then it was just an awesome business and of course the government provided some incentives to do that with the investment tax credit what about things like refurbished equipment is that's still something that you do today or is that a thing of the mainframe pass that's great yeah that's a great question you know it's a it's still a really important and a sustainable business for us we we take equipment back that comes off of a lease or sometimes alone but typically a lease and we will refurbish that or reman factor that equipment and then put it back into market oftentimes it goes into our services organization for them to use with their clients the global technology services typically you know we will we will matram a fact or a remarket about 29,000 IT devices a week 16,000 tons of idea quipment around the in a year around the world so these remanufacturing refurbishing centers so it's a even though the hardware business has come down in its percentage of IBM's business compared to software and services it's still a very very big business as you can see by the the size of the number of equipment and the tonnage what about some of the initiatives that are so you mentioned you know the digital transformation a lot going on with cloud machine intelligence I mean those big projects you know some of them are still multi-year you know seven weeks people say oh there's no more multi-year projects but digital transformations are multi-year projects even though you might take them in chunks but I'm going to capitalize those can I finance them as well what role does does IBM finance play in that you absolutely can and and that is a big big part of our business today though the the client will they look I've got a very large digital transformation project going to take place in four countries we are looking for an opportunity to match those cash outlays with when those countries come online or when we begin to receive the benefits we also want you've been and some of the software that goes with this digital transformation and we also want to spin and the IT infrastructure that's required so we may put those services software and hardware on a different financial instruments but it looks like you know one total bill for the client and it and its global it's a global footprint so we're able to handle the different currencies around the world and and again most importantly match those cash outlays with when the benefits are received so bill you know as long as I've been in this business the IT investments from a CFOs perspective have always been viewed as a higher risk granted higher reward but but you know the the CFOs would say okay you're gonna have to have a little higher IRR for this one because you know the business moves so fast technology changes so quickly how are you seeing the CIO - CFO conversation evolve what's your advice to see iPods in terms of how they talk to two CFO's that's another really good question so I was just on with actually new client this morning one was the F of the other one was a treasurer and they were asking my opinion about this financial instrument and and and getting some advice actually the conversation went look it's not really cost the debt issue the cost of money is always part of the economic decision but oftentimes those clients use financing instrument as a way to manage the asset manage the asset throughout the life the project they also want to focus on the delivery the quality of the delivery I think that takes place during these very very large project financing engagements so the CFO specifically said look I really like business case it's quite clear when we're gonna receive these benefit what I'd like to know Bill is how do you view the risk of the implementation and you know we were able to share with them the risk work that we do with with GBS team our level of confidence that it will be done on time and on budget and the skill level of the of the partner team that's been assigned so it actually has allowed us to have a different conversation with different group or senior level at the account CFO Treasury sometimes the controller you play an important role in de-risking the the business case and as well I mean I would imagine right now in there you know these on certain times that you know IBM Global Financing can provide liquidity to businesses who need it that you you know are confident you know are stable business but might need some help you know getting through this pandemic we can and as you said the what makes us a little different is you know we make credit decisions on what we call arm's length credit visions you know for a standalone albeit at the financing company so we're very very focused on maintaining the right investment grade of the portfolio we're going to make really really good prudent risk decisions you know that being said we have some fabulous IBM clients that have been clients for a long time we work very closely with them understanding their financial structures what's what's important to them and they're very transparent with us about you know with financial challenges they have so we'll continue to provide that liquidity we are going to be very prudent but we'll certainly help those really good clients well last question it's kind of where do you see this going what's your kind of vision for IBM global global finance and give us a little glimpse of the future sure you know I think you'll see us continue to migrate in the direction of the IBM company moves the IBM company is aggressively moving towards a hybrid cloud model we'll continue to provide those migration services will continue to do you know some short-term financing a part of the business we didn't talk about was the commercial financing we provide short-term working capital through IBM 6000 isness partners so to help them with their free cash flow running their businesses you know that's a pretty big business for us we'll do about you know 14 billion or so in financing to that commercial financing business so I'll see that continue as well and then finally I'm sure you'll see us continue to grow the software and services financing as well and we'll stay with the very very high anything rate for whatever is left of IBM's Hardware portfolio point you made about the partner financing is huge like you said it helps them bridge their free cash flow it makes IBM a more attractive partner for through those resellers and partners it does and we've been in that business for a very very long time oftentimes we are one of the you know largest predators for those partners so the liquidity that we provide Danville allow them to run their businesses day to day with that short term working capital is something that we're very committed to you over the long term for IBM product and services so IBM Global Financing a very important and strategic part of IBM's business a differentiator a very few companies actually can provide that type of service to their clients and so bill really appreciate you coming to the Kuban and sharing that with with our audience great to have you back yeah very much Brad you've been a real pleasure - our pleasure as well thank you for watching everybody this is Dave Volante for the cube our continuous coverage of IBM think 2020 we'll be right back right after this short break you're watching the cube [Music] you

>> Live from Orlando, Florida, it's theCUBE, covering Cisco Live 2018. Brought to you by Cisco, NetApp, and theCUBE's ecosystem partners. >> Hello, everyone. Welcome back to theCUBE's live coverage here in Orlando, Florida for Cisco Live 2018. I'm John Furrier with Stu Miniman. Our next guest, Eric Herzog, Chief Marketing Officer and Vice President Global Channel Sales for IBM Storage. CUBE alum, great to see you. Thanks for comin' by. >> Great, we always love comin' and talkin' to theCUBE. >> Love havin' you on. Get the insight, and you get down and dirty in the storage. But I gotta, before we get into the storage impact, the cloud, and all the great performance requirements, and software you guys are building, news is that the CEO of Cisco swung by your booth? >> Yes, Chuck did come by today and asked how-- Chuck Robbins came by today, asked how we're doin'. IBM has a very broad relationship with Cisco, beyond just the storage division. The storage division, the IOT division, the collaboration group. Security's doin' a lot of stuff with them. IBM is one of Cisco's largest resellers through the GTS and GBS teams. So, he came by to see how were doin', and gave him a little plug about the VersaStack, and how it's better than any other converge solutions, but talked about all of IBM, and the strong IBM Cisco relationship. >> I mean, it's not a new relationship. Expand on what you guys are doin'. How does that intersect with division that he put on stage yesterday with the keynote. He laid out, and said publicly, and put the stake in the ground, pretty firmly, "This is the old way." Put an architecture, a firewall, a classic enterprise network diagram. >> Right, right. >> And said, "That's the old way," and put in a big circle, with all these different kinda capabilities with the cloud. It's a software defined world. Clearly Cisco moving up the stack, while maintaining the networking shops. >> Right. >> Networking and storage, always the linchpin of cloud and enterprise computing. What's the connection? Share the touch points. >> Sure, well I think the key thing is everyone's gotta realize that whether you're in a private cloud, a hybrid cloud, or a public cloud configuration, storage is that rock solid foundation. If you don't have a good foundation, the building will fall right over, and it's great that you've got cloud with its flexibility, it's ability to transform, the ability to modernize, move data around, but if what's underneath doesn't work, the whole thing topples over, and storage is a cruel element to that. Now, what we've done at IBM is we have made all of our solutions on the storage side, VersaStack, our all-flash arrays, all of our software defined storage, our modern data protection, everything is what we'll say is cloudified. K, it's, I designed for multiple cloud scenarios, whether it be private, hybrid, or public, or, as you've probably seen, in some the enterprise accounts, they actually use multiple public cloud providers. Whether it be from a price issue, or a legal issues, because they're all over the world, and we're supporting that with all our solutions. And, our VersaStack, specifically, just had a CVD done with Cisco, Cisco Validated Design, with IBM Cloud Private on a VersaStack. >> Talk about the scale piece, because this becomes the key differentiator. We've talked about on theCUBE, many of the times with you around, some of the performance you guys have, and the numbers are pretty good. You might wanna do a quick review on that. I'm not lookin' for speech and feeds. Really, Eric, I'd like to get your reaction, and view, and vision, on how the scale piece is kicking, 'cause clients want scale optionality. They're gonna have a lot of stuff on premise. They have cloud goin' on, multi cloud on the horizon, but they gotta scale. The numbers are off the charts. You're seein' all these security threats. I mean, it's massive. How are you guys addressing the scale question with storage? >> So, we've got a couple things. So first of all, the storage itself is easily scalable. For example, on our A9000 all-flash array, you just put a new one, automatically grows, don't have to do anything, k? With our transparent cloud tiering, you can set it up, whether it be our Spectrum Scale software, whether it be our Spectrum Virtualize software, or whether it be on our all-flash arrays, that you could automatically just move data to whatever your cloud target may be. Whether that be something with an object store, whether that be a block store, and it's all automated. So, the key thing here on scalability is transparency, ease of use, and automation. They wanna automatically join new capacity, wanna automatically move data from cloud to cloud, automatically move data from on premise to cloud, automatically move data from on premise to on premise, and IBM's storage solutions, from a software perspective, are all designed with that data mobility in mind, and that transportability, both on premise, and out to any cloud infrastructure they have. >> What should Cisco customers know about IBM storage, if you get to talk to them directly? We're here at Cisco Live. We've talked many times about what you guys got goin' on with the software. Love the software systems approach. You know we dig that. But a Cisco deployment, they've been blocking and tackling in the enterprise for years, clouds there. What's the pitch? What's the value proposition to Cisco clients? >> So, I think they key thing for us talkin' to a Cisco client is the deep level of integration we have. And, in this case, not just the storage division, but other things. So, for example, a lot of their collaboration stuff uses under pitting software from IBM, and IBM also uses some software from Cisco inside our collaboration package. In our storage package, the fact that we put together the VersaStack with all these Cisco Validated Designs, means that the customer, whether it be a cloud product, for example, on the VersaStack, about 20 of our public references are all small and medium cloud providers that wheel in the VersaStack, connect 'em, and it automatically grows simply and easily. So, in that case, you're looking at a cloud provider customer of Cisco, right? When you're looking at a enterprise customer of Cisco, man, the key thing is the level of integration that we have, and how we work together across the board, and the fact that we have all these Cisco Validated Designs for object storage, for file storage, for block storage, for IBM Cloud Private. All these things mean they know that it's gonna work, right outta the box, and whether they deploy it themselves, whether they use one of our resellers, one of our channel partners, or whether they use IBM services or Cisco service. Bottom line, it works right out of the box, easy to go, and they're up and running quickly. >> So, Eric, you talked a bunch about VersaStack, and you've been involved with Cisco and their UCS since the early days when they came up, and helped drive, really, this wave of converged infrastructure. >> Right. >> One of the biggest changes I've seen in the last couple years, is when you talk to customers, this is really their private cloud platform that they're building. When it first got rolled out, it was virtualization. We kinda added a little bit of management there. What, give us your viewpoint as to kinda high-level, why's this still such an important space, what are the reasons that customers are rolling this out, and how that fits into their overall cloud story? >> Well, I think you hit it, Stu, right on the head. First of all, it's easy to put in and deploy, k? That is a big check box. You're done, ready to go. Second thing that's important is be able to move data around easily, k? In an automated fashion like I said earlier, whether that be to a public cloud if they're gonna tier out. If I'm a private cloud, I got multiple data centers. I'm moving data around all the time. So, the physical infrastructure and data center A is a replica, or a DR center, for data center B, and vice versa. So, you gotta be able to move all this stuff around quickly easy. Part of the reason you're seeing converge infrastructure is it's the wave of what's hit in the server world. Instead of racking and stacking individual servers, and individual pieces of storage, you've got a pre-packed VersaStack. You've got Cisco networking, Cisco server, VMware, all of our storage, our storage software, including the ability to go out to a cloud, or with our ICP IBM Private Cloud, to create a private cloud. And so, that's why you're seeing this move towards converge. Yes, there's some hyperconverged out there in the market, too, but I think the big issue, in certain workloads, hyperconverged is the right way to go. In other workloads, especially if you're creating a giant private cloud, or if you're a cloud provider, that's not the way to go because the real difference is with hyperconverged you cannot scale compute and storage independently, you scale them together, So, if you need more storage, you scale compute, even if you don't need it. With regular converge, you scale them independently, and if you need more storage, you get more storage. If you need more compute-- If you need both, you get both. And that's a big advantage. You wanna keep the capex and opex down as you create this infrastructure for cloud. 'Member, part of the whole idea of cloud are a couple things. A, it's supposed to be agile. B, it's supposed to be super flexible. C, of course, is the modern nomenclature, but D is reduce capex and opex. And you wanna make sure that you can do that simply and easily, and VersaStack, and our relationship with Cisco, even if you're not using a VersaStack config, allows us to do that for the end user. >> And somethin' we're seeing is it's really the first step for customers. I need to quote, as you said, modernize the platform, and then I can really start looking at modernizing my applications on top of that. >> Right. Well, I think, today, it's all about how do you create the new app? What are you doin' with containers? So, for example, all of our arrays, and all of our arrays that go into a VersaStack, have free persistent storage support for any containerize environ, for dockers and kubernetes, and we don't charge for that. You just get it for free. So, when you buy those solutions, you know that as you move to the container world, and I would argue virtualization is still here to stay, but that doesn't mean that containers aren't gonna overtake it. And if I was the CEO of a couple different virtualization companies, I'd be thinkin' about buyin' a container company 'cause that'll be the next wave of the future, and you'll say-- >> Don't fear kubernetes. >> Yeah, all of that. >> Yeah, Eric Herzog's flying over to Dockercon, make a big announcement, I think, so. (laughing) >> Evaluation gonna drop a little bit. I gotta ask you a question. I mean, obviously, we watch the trends that David Floy and our team, NVMe is big topic. What is the NVMe leadership plan for you, on the product side, for you? Can you take a minute to share your vision for what that is gonna be? >> Sure, well we've already publicly announced. We've been shipping an NVMe over fabric solution leveraging InfiniBand since February of this year, and we demoed it, actually, in December at the AI Conference in New York City. So, we've had a fabric solution for NVMe already since December, and then shipping in February. The other thing we're doing is we publicly announced that we'd be supporting the other NVMe over fabric protocols, both fabric channel and ethernet by the end of the year. We publicly already announced that. We also announced that we would have an end to end strategy. In this case, you would be talking about NVMe on the fabric side going out to the switching and the host infrastructure, but also NVMe in a storage sub-system, and we already publicly announced that we'd be doing that this year. >> And how's the progress on that plan? You feel good about it? >> We're getting there. I can't comment yet, but just stay tuned on July 1st, and see what happens. >> So, talk about the Spectrum NAS, and other announcements that you have. What's goin' on? What are the big news? What's happening? >> Well, I think that, yeah, the big thing for us has been all about software. As you know, for the analysts that track the numbers, we are, and ended up in 2017, as tied as the number one storage software company in the world, independent of our system's business. So, one of the key powers there is that our software works with everyone's gear, whether it be a white box through a distributor or reseller, whether it be our direct competitors. Spectrum Protect, which is a, one of the best enterprise backup packages. We backup everybody's gear, our gear, NetApp's gear, HP's gear, Pure's gear, Hitachi's gear, the old Dell stuff, it doesn't matter to us, we backup everything. So, one of the powers that IBM has, from a software perspective, is always being able to support not only our own gear, but supporting all of our competitors as well. And the whole white box market, with things that our partners may put together through the distributors. >> I know somethin' might be obvious to you, but just take me through the benefits to the customer. What's the impact to the customer? Obviously, supporting everything, it sounds like you guys have done that with software, so you're agnostic on hardware. >> Right. >> So, is it a single pane of glass? What's the benefit to the customer with that software capability? >> Yeah, I feel there's a couple things. So, first of all, the same software that we sell as standalone software, we also sell on our arrays. So if you're in a hybrid configuration, and you're using our Flashsystem V9000 in our Storwize family, that software also works with an EMC, or NetApp box. So, one license, one way to do everything, one set of training, which in a small shop is not that important, but in a big shop, you don't have to manage three licenses, right? You don't have to get trained up on three different ways to do things, and you don't have to, by the way, document, which all the big companies would do. So it dramatically simplifies their life from an opex perspective. Makes it easier for them to run their business. >> Eric, we'd love to get your opinion on just how's Cisco doin' out there? It's a big sprawling company. I looked at the opening keynote, the large infrastructure business doing very well in the data center, but they've got collaboration, they do video, they're moving out in the cloud. Wanna see your thoughts as to how are they doing, and still making sure they take care of core networking, while still expanding and going through their own transformation, that they're talkin' very public about. How do we measure Cisco as a software company? >> Well, we see some very good signs there. I mean, we partner with 'em all the time, as I mentioned, for example, in both the security group and our collaboration group, and I'm not talkin' storage now, just IBM in general, we leverage software from them, and they leverage software from us. We deliver joint solutions through our partners, or through each of the two service organizations, but we also have products where we incorporate their software into ours, and they incorporate software in us. So, from our perspective, we've already been doing it beyond their level, now, of expanding into a much greater software play. For us, it's been a strong play for us already because of the joint work we've been doing now for several years on software that they've been selling in the more traditional world, and now pushing out into the broader areas, like cloud, for example. >> Awesome work. Eric, thanks for coming on. I gotta ask you one final, personal, question. >> Sure. >> You got the white shirt on, you usually have a Hawaiian shirt on. >> Well, because Chuck Robbins came by the booth, as we talked about earlier today, felt that I shouldn't have my IBM Hawaiian shirt on, however, now that I've met Chuck, next time, at next Cisco Live, I'll have my IBM Hawaiian shirt on versus my IBM traditional shirt. >> Chuck's a cool guy. Thanks for comin' on. As always, great commentary. You know your stuff. >> Great, thank you. >> Great to have the slicing and dicing, the IBM storage situation, as well as the overall industry landscape. At Cisco Live, we're breakin' it down, here on theCUBE in Orlando. Second day of three days of coverage. I'm John Furrier, Stu Miniman, stay with us for more live coverage after this break.

>> Announcer: Live from Las Vegas, (electronic music) it's theCUBE. Covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, and this is day two of our wall-to-wall coverage of IBM Think. We've been doing IBM shows for years. This is the big, consolidated show, 30 to 40 thousand people, too many people to count. Cameron Clayton is here. He is a GM of Watson Content and IoT Platform at IBM. Thanks for coming on. >> Thanks very much for having me. >> So quite a show, right? Standing room only! >> A large, large show. >> Standing room only and also great announcements. >> So tell us about your announcements. >> Yeah, so we got to couple of things we're really, really excited about. The team's been working really hard on for the last few months. One is a way to train Watson to make Watson even smarter than it already is out of the box. And so, we've been building data kits by vertical industry. So for financial services, for travel and transportation, for the hospitality industry, for health care and for government, on how do you give Watson a high machine IQ right out of the gate as opposed to having to train it in your area of industry. And so, once again, we're really focused on making Watson the AI system for Enterprise, and this is another step on that journey to make Watson really, really smart. >> It's really prioritizing it in a way that's much easier to consume. >> Much easier to consume, and if you think about it, there's a lot of jargon in each industry, right? To be an expert in industry, you got to know a lot of jargon, understand the context of that. An AI system doesn't know that unless it's taught that. And so we are teaching Watson that. And then how to apply it successfully in each of those industries. So it's a pretty material leap forward in how we're training Watson. >> So it hits the content component >> Cameron: Hits the content. >> And then industries you're knocking down? Where are you starting? >> Yeah, so we're starting with financial services. We're launching in travel and transportation and in hospitality. So we're basically, this is a pretty fun one, I love food. But basically Watson went out and scanned the entire internet and collected all the recipes that it could find on the internet and trained itself on food. And so, you can ask it now questions about food, what restaurants, about really specific things. If you're a vegan you can find out what's available near you. If you're gluten intolerant, you can find out things on the menu like that. But then there's other things, like in the travel and transportation industry. Virtual agents for travel agents, they can ask questions of Watson, and it can ask very specific, very deep things, very much like a human would. And so you can say a simple thing like, "Where should I stay in New York?" And a human would respond, "Well, are you a member of any hotel rewards program?" Normal AI chatbot wouldn't. It would just say, "These are the lists of the 4,000 hotels in New York." Watson will actually ask human-like questions to give you the best answer possible. But all that requires training, and that's what were built in with these Watson content data kits, and we're really excited about 'em. >> So I'll come back to that. But so if I take that example of Watson Chef, there's this discussion on AI for the enterprise versus AI for consumers. >> Right. Are you crossing over? That was kind of a consumer-y application. >> Cameron: Yeah. >> Is that just an example? >> It's just an example. No, it's very much about AI for the enterprise, right? And so the four priority industries that we're focused on, first is financial services, sort of the sweet spot for IBM. The second is supporting our government clients to make sure that Watson is trained in the language and nuisances the of government. The third is Watson health, so the health care industry, both the regulation and the language itself. So everything from pharmacology, et cetera. And then the fourth is travel and transportation. So it's very much about making Watson the smartest AI system for enterprise. That's absolutely its focus. >> What's the IoT angle in your title? >> Yeah, so-- >> What's going on there? >> I run the IoT platform for IBM, and so The Weather Company, which is how I joined IBM, which I also run, really is one of the largest IoT platforms in the world, which was actually a big part of the acquisition case for acquiring The Weather Company. We're now bringing the ability to ingest 35 to 40 billion data requests every day with The Weather Company platform to the IoT platform. We've combined those things together. So we can ingest data and content at a scale unlike pretty much anyone else in the world, sort of second only to Google in terms of the scale of data and content we can ingest. And we use that data to help train Watson on one hand, and on the other hand, to support our clients in multiple industries around the world. >> Yeah, I remember when IBM did that acquisition, Bob Picciano told me, "Well, you got to understand. "This is an IoT play as much as it is a data science play." So how has that evolved, come together, with IBM's core? >> Yeah, so I think in a couple of ways. One is, it's taken the way the company was mostly a domestic US business. IBM, in the last couple of years, has globalized that business in a very material way. A great example is in aviation, where we have the top 30 US operators. Now we have hundreds of operators all around the world helping them make decisions every day. At its core, this IoT platform that started with the way the company is now much larger than that, has grown into a decision platform, right? We make recommendations for people to make decisions. Mostly that's with Watson and AI, but sometimes it's just with machine learning and more traditional methods. >> So you got some other stuff going on. >> We were talking off camera >> We do. >> about this real-time closed captioning. I was showing you our video clipper tool. You said, "Hey-- >> Yeah! >> "We have something very similar." We're going to maybe talk and see if we can't-- >> Yeah, that'll be great. >> collaborate. I can't wait to try that out. So talk more about what you're doing with real-time closed captioning. It's a mandate, >> That's right. >> for broadcasters and other folks like YouTube. >> That's right. . How are you helping them? >> Yeah, so, as you mention, closed captioning is a regulated space for broadcasters, both local and national. It's a cost center for them, right? They have to do it, and it takes time, people, effort, and energy. We're automating that and we're doing it in a real-time way, so in true real time. So as we're speaking, Watson is listening. It's recording and it's annotating everything that goes on in the video clip. And then it's also breaking it up into essentially a highlight reel, right? And so you can ask questions. Hey, show me the highlights of the US Open or the Masters Golf Tournament. And it'll automatically select the very best clips that came from that tournament based on sentiment analysis, tone of voice, trending key words that were showing in social media, and surface those clips up, typically to a human editor who will then process them. It basically automates a system that today requires human intervention to deliver and makes it completely seamless by being in real-time. >> So Watson will analyze social data, Twitter data, take the fire hose and say, "OK, based on the Olympics," or whatever it was, "this is what was hot." >> Cameron: That's right. >> Curling was off the charts hot. >> (laughs) Curling is always hot in Olympics. >> Hashtag curling. >> Right. >> OK, cool. >> That's right. >> And this is a product that's out on the market today? >> It's a product that's launching here at Think and is being tested by multiple clients right now and is a really great accuracy, quality scores, 95% plus accuracy. But most importantly, it's no human intervention. So no person has to do anything, and it meets all of the regulatory requirements. For digital content creators, which are the fastest growing part of the video ecosystem, people like yourself and others, are also using it to automatically meta tag all their clips. So not only does it do sentiment analysis of the clips and the content itself using the closed captioning, but it's also going out and measuring social media key words and hashtags that are trending and looking for those key words in the closed captioning and clipping that out and surfacing it to make it easier. >> And I consume that as a monthly service kind of thing? >> Exactly, exactly, yep. >> How 'about GDPR? That's hot topic these days. Can you help me with my GDPR problem? 'Cause the clocks ticking on my defines, kicking in. >> Clocks ticking on GDPR. If you haven't started on GDPR yet, you're in some trouble. >> You're way late. >> You're way late, but you better call IBM pretty quickly, and we'll parachute in and try and help. >> How can you help? >> So I think we can help in multiple ways. So one is, obviously, our services group with GBS. We're doing thousands of engagements trying to help people with GDPR. I think, secondly, is we've got a big effort with our consumer weather business to be ready for GDPR. We have 250 million users of our weather app around the world, and they'll have to be compliant here pretty quickly. And so, we've got that all set up, ready to go. And then, these data kits also learn the regulations, right? So you can ask questions of Watson about GDPR and your specific use cases as a customer, and we'll show you how to apply the regulations of GDPR to your business. >> So earlier on, you talked about these data kits. I mean, in my head I was thinking SDK. >> Cameron: Right. So how does that all work? >> Yeah, so you can, you basically on a SAS basis, you essentially rent these data kits, everything from a general knowledge kit to a industry specific kit for financial services, to a sub-industry like wealth management within financial services. And you basically can rent each of those pieces. Within the government category, we have a GDPR capability, along with other regulatory capabilities within the data kits. >> OK, so how does that work? I sort of train my internal system? >> It's super easy. You, basically, go to Bluemix, and you can just use it as a subscription out of Bluemix is the fastest, easiest way to do it. Secondly, you can talk to any of your IBM associates about how you use data kits with Watson. It's always used in conjunction with Watson services themselves, is how you basically deploy our products. >> Let's say I got data all over the place in my organization, it's siloed out, and I'm freaking out because I've got personal data on an individual here and one over her and one over here. What do I do? I point my corpus of data at Watson, and it helps me extract from itities, dedupe, surface? >> The first step in all of our engagements is to listen and understand exactly where all the data is, and everyone's on a journey, right? From on prem to hybrid to some public cloud and everything in between. >> Dave: And they don't know where it all is. >> And they don't know where it all is. And so, step one is for us to go in and listen. We have a rule in our group, two ears and one mouth, use them proportionally. And so we go in and we try to listen, find out, map out sort of a architecture of where our client's data is. And then understand what problem they're really trying to solve because, often times, there's lots of good ideas, but there's only a couple of problems that really matter to that client to solve. Right now, GDPR is certainly one of those problems. But whether it's revenue or efficiency, we can help, but we really need to understand what the problem set is first. And so we have an engineering team that goes in and does sort of architectural work and listens upfront. And then we go into a sort of solutioning mode to solve problems. >> One of the question's we often ask on theCUBE is, how far can we take machine intelligence? How far should we take machine intelligence? What are the things that machines can do that humans can't? How is that changing? How will they complement each other? How will they compete? You must think about that a lot in your role. You're augmenting, sometimes replacing a lot of human tasks. But what are your thoughts on those big picture questions? >> Yes, I think we've, as a company, work really, really hard to make sure that we are always augmenting people wherever possible. We fundamentally believe that every job is going to be changed by AI, but we believe that humans are really good at creativity, at curiosity, and at risk management. We don't really think about us being good at risk management, but from when we're born, just learning to walk is a risk management exercise, right? Look at any toddler wobbling, learning to walk, you sort of realize it's a risk management exercise. AI systems have to learn all these things. And so surfacing and recommending decisions is what we believe Watson and AI is best equipped to do, and then have a person actually make the final call. >> Great. All right, Cameron, hey, thanks very much for coming on theCUBE. >> You're welcome. >> It was really a pleasure meeting you. >> Absolutely, likewise. >> And look forward to the follow up. >> Absolutely, we'll follow up. >> Excited to see that. All right, keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the show theCUBE live from IBM Think 2018. We'll be right back. (electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE covering IBM Think 2018 brought to you by IBM. >> We're back at IBM Think 2018. This is theCUBE the leader and live tech coverage. My name is Dave Vellante and I'm here with my cohost Peter Burst. Day two of our wall to wall coverage of IBM's inaugural Think conference. Rajesh Nambiar is here, he's the general manager of global business services for application services within IBM. Thanks for coming on theCUBE. >> Thank you for having me here. >> So how's this event going for you? You're in from Singapore. We were saying you must love the fact that IBM's consolidated a lot of it's major events in one place. You get a lot done in a week. >> Absolutely, I think this is four or five days that we're going to be here. Phenomenal amount of energy, I mean when you go around you can see that. I think as you said, combining some of the events it's made it even more interesting for us. I'll be meeting more people, more clients, more productive session for sure. >> So let's talk about what you do with application services and then we really want to get in to one of the themes that Jenny hit on today which is incumbent disruptors and competing from the core of your proprietary data. So let's get in to it. Start with your organization and what you guys do. >> Absolutely, so as you mentioned and I do the application services for GBS in IBM. Within application services I think we focus on application development and management which is a large area for us and as you know that IBM has been managing applications for many, many large clients over the period of time and it's a very large portfolio for IBM. What we see truly is enterprises as you saw and they are going to have enormous amount of issues with the new age companies if you may, all of the new companies which are sort of coming out. You can call them bond digital or bond in cloud or uberization of the organizations, whatever. So you'd find that the enterprises are going to have significant issues maintaining the company advantage over a period of time. And one of the ways they could sort of regain the leadership or company advantage would be by ensuring that they are digitally reinventing themselves. The problem is, I think as Forbes said recently in an article around saying that, about 80 percent of the digital transformation projects really fail with multiple reasons as to why they fail. I want to argue saying that one of the, and you heard from Jenny this morning, that you know you have the business architecture and the technology architecture. If we focus on the technology for second, you would find that many of these new incumbents that you mentioned will, they try to compete purely on the digital side of the equation. They will have a harder chance or they might not even get where they want to go. And we want to argue saying that if they kind of pay attention to the core they have, and I want to sort of define what this core and digital is going to be, so think of it this way, I mean core is what, if any company has been around for awhile, then they would of had a significant amount of core systems, systems of records if you want to call it where they have the business process embedded into that. They have the customer data embedded into that. Now what's happening on the other side, of course everybody wants to get there, the whole digital dimension, on the digital side of the equation you do have systems of engagement, where you truly understand and engage, I want to say customers but then again also have employees of your organization. So you're going to engage them in the last mile if you may. What touches the customer, touches your employees, that's what we call systems of engagement in the digital. Now, organizations tend to see these two as two different things and if you do not build your digital eco system, leveraging what you have in core, I believe that the chances of you failing in your digital transformation are very, very high. Why is that? Because I believe the intersection of these two worlds if you may, the core and the digital, is not that easy for people to leverage and I believe that we as a company, we help our clients sort of leverage that intersection if you may. >> Okay so, where do you start? Is it application modernization? Is it allowing them to develop applications that are more sort of more native as you say? When you talk to customers where do you see the starting point? >> Okay so, when you look at these two fundamentally there are synergies between these two worlds and they are discouraged. Synergies are natural why? Because as I mentioned before, in the core or in the systems of records you'll find business processes getting embedded, customers data getting embedded. And then on the other side of the digital system, you always have the user experience which is what we all want to try. I mean the user experience is all about everything. I was talking to a bank recently (foreign name). So they said, we built this phenomenally wonderful user friendly mobile app for our customers and what happened was the app was fantastic and it was great user experience and everything was fine, he just add for every transaction it took like a minute for the balance to show up on the mobile app. That's not what he wanted because why is that? Because your focusing on the digital only. The fact that it just go to your core systems, get the customer data and bring it back to the watch app or the mobile app or whatever, that wasn't the plan that I weighed and hence my point being that if you look at the synergies which is great, there are a lot of discordance because the way that all systems are being built is very different. Maybe you're using a waterfall. The new systems are getting built in a different way. If you leverage the synergies, manage the discordant in a nicer way. A great example would be, so do you have micro services coming out of your core systems to enable your digital systems. You have the right API's getting built from the core systems to enable your digital systems. If you're able to manage this intersection well, then I think you have a play and that's how I believe that we should. So again to your point, do you modernize? I believe you do the three things to get the synergy right. One would be you are to optimize your core systems for efficiency because more and more the systems get older and older. You're going to have challenges in maintaining them, more expensive to maintain them, so you optimize those systems for efficiency. Then you modernize them to build in or enable new capability. So second, as I said modernize, what you really do, you're making sure that it is easier for the digital systems to get to you, to understand what you're doing, to get the customer data, so that is a modernized space. The third is that you have to innovate sort of co create if you may and make sure that you're able to build those newer systems, digital systems using the core and enabling the core for growth. So if you had an organization, if you want growth, you're not going to get it if you don't do these three things in my opinion. >> So Rajesh, many years ago I did a research project for a client and we looked very closely at the consequences of increasing the functionality and automation in systems of engagement and how that drove work back in the core and we found that every success of generation of enhancement on the systems of engagement, drove the number of transactions back at the core sevenfold. Are you seeing relationships like that? Is there rules of thumbs that people should use now as their systems of engagement get even more powerful, more human friendly? What is the new kind of expectation these days? >> So the issue is definitely what you said. I mean for every about seven or eight times is what you want to drive the, for every single transaction which is rising out systems of engagement. However, one of the way to make it more efficient when the systems of records, which is the core systems if you may, is by using the modern, stuff we'll be talking about, if you have designed your core systems and enabled micro services in the right way, maybe instead of having seven or eight transactions, you could be able to do that in two or three. Similarly >> Peter: Unstage them. >> Unstage them, yeah in a certain way so that you're not getting into the performance issue which I talked about in this banking example as you know, you don't want to build a wonderful digital app but having that to go through a significant performance issue over the period of time. So that is one of the things. The other important element of what he just now said is also the talent piece of it. We underestimate, I think, as we said, one of the reasons why many of these engagements fail is also because people don't think talent is a big deal in a lot of this. Because when you really see, if you're a big company, been around for awhile, you have a very strong core, and your people in the IT organization are going to be wired somewhat to the processes which are going to be sort of the ordeal if you may. And how do you move to this new world of digital? So there is a fundamental difference from the talent point of view. Two things, as an organization you're moving from process centric to user centric. Now you want to build something for your customer, for your employee. When you do that the talent base, of course their minds have changed, but also a simple example, we always hire people for skills. Maybe still, some companies still do, for skills. But I believe that's a passe because you know what do you now need is a tenacity for learnability or tenacity for a life long learning for the people whom you're hiring. Not necessarily a skill that you value today because what happens in today's world, after six months that skill is no longer valuable for you. So what do you do with them? But if you have a tenacity for life long learning, the ability for you to pick up new skills and then transform yourself will be so high that you're not stuck with people who are all skills for a long period of time. >> I was talking to a senior, a guy who owns development and he said one of the biggest impacts of open source over the last few years was that it brought the notion of responsibility, recognition, reputation, and change the way that the evolvers talk about collaboration with each other, not just in the open source world but overall. I think collaboration and new collaboration agile also has to be part of the equation. What do you think? >> Well without a question. In fact, I was about to say, collaboration's very, very key because again, when you move from process interviews as intrigue, you also find, traditionally organizations are very role based, so everybody had a role. I'm a developer, I'm a tester, I'm a architect. But in the new world, this is going to be changing into maybe parts of people who are sort of working on a garage metal. Everybody does everything. You have a smaller group of people who are able to evaluate something very, very quick and in an agile fashion as opposed to the traditional way of saying, I'm sort of role based, I have an organization and that's how they operate. So I think there's significant difference and again, I would probably say to leverage the talent for the newer market. Again there are about two or three things that one could potentially do. One would clearly be this learnability. Skills are no longer what is valued, it's the learnability. The ability for you to sort of quickly move from one to another would be valued. Second would be diversity of skills. Today we hire more people with user experience, with psychology major. You would of never thought of this 10 years ago. We never hired anybody from art school but we do that today because of... >> I was really happy. My son's a music major. >> My son is a psychology major, I was just telling you in the University of Colorado. So they get hired probably as well as already the STEM students are going to be, so that's good. (laughing) And the last one is of course, I have this notion called digital label . I don't know if you've heard this before. And Jenny talked about it today. So you're going to have man and machine, when you do that, automation is a great influencer in all of this. I think there are going to be the digital label and the human label are going to co exist. So we're calling it hybrid label. So any task that you're going to do, we will have people which is sort of high capability now, leveraging watts, which is a digital label. So that's another important thing in the talent market. >> And the laborer increasingly requires sort of multi tool skills not only domain expertise but also digital skills. >> Rajesh: Absolutely >> At least being able to understand how the leverage, the machine intelligence. I want to ask you, and I know Peter you got to go soon. But this trend with IOT, Blockchain, we saw the IV and Maersk example today where they're attacking inefficiencies where there's a third party trust involved and it's creating a trustless system. Do you see a trend toward sort of putting token economics embedded inside of applications, things like Blockchain, increasingly going into core applications? Is that a trend you're seeing yet? >> Yes, yes, I think not as much as we would like to see but I think it's beginning to sort of level up in a period of time. I think Blockchain is still, as I said, there's a more in the experimentation phase, and there are a few companies who have leveraged fully. Great example is as you saw this morning with what we're doing with the APMM Maersk, the fact that we're able to do the distribution systems within shipping. And any radio finding that there's going to be a significant amount of paperwork or transactional arrangements that are being done outside of the normal systems. I think Blockchain would be a great way to solve this issue. >> I want to tease your session a little bit. You're going talk, you got a CIO panel, what is that? >> Well the talk is actually going to be I think unlocking the value of the core system. So there's going to be something similar to what we talked about. We've got great session with three CIO's who are going to be on the panel. We're going to have the Carhartt CIO John Hill is going to be on the panel, and they've done a lot of good work in terms of truly making sure that they understood that if they don't level the core they can't really get to the digital. >> Was that CarHartt? >> Carhartt, yes. >> The only brand I wear. >> Really? (laughing) >> They'll be interesting, then KLM with (mumbling) with their history of the core that they've had for several years and how they're really moving into the new digital era and then being sort of a customer friendly airline if you may, so he's going to talk about some of that. And then we also have the TPX which is the communications organization which they've done gone through about 12 acquisitions over the last 12 years, so one a year pretty much. How are they integrating all of those companies and how are they really putting them together into sort of one system. >> Peter: And when is that session? >> That session's on Thursday morning at 11:30, I hope you guys are there to watch that. I'm worried because it's the last day. >> It's a getaway day but listen, a good day to go down and check it out because that notion of what incumbents should be doing and competing from the core is very, very important idea. So Rajesh thanks for coming on theCUBE and explaining that. Best of luck to you tomorrow and great to see you. >> Thank you so much, thank you. >> Alright, keep it right there buddy. We'll be back with our next guest. This is CUBE, you're watching live from IBM Think 2018. We'll be right back. (upbeat music)

(upbeat music) >> Hi we're at the Palo Alto studio of SiliconANGLE Media and theCUBE. My name is George Gilbert, we have a special guest with us this week, Veeru Ramaswamy who is VP IBM Watson IoT platform and he's here to fill us in on the incredible amount of innovation and growth that's going on in that sector of the world and we're going to talk more broadly about IoT and digital twins as a broad new construct that we're seeing in how to build enterprise systems. So Veeru, good to have you. Why don't you introduce yourself and tell us a little bit about your background. >> Thanks George, thanks for having me. I've been in the technology space for a long time and if you look at what's happening in the IoT, in the digital space, it's pretty interesting the amount of growth, the amount of productivity and efficiency the companies are trying to achieve. It is just phenomenal and I think we're now turning off the hype cycle and getting into real actions in a lot of businesses. Prior to joining IBM, I was junior offiicer and senior VP of data science with Cable Vision where I led the data strategy for the entire company and prior to that I was the GE of one of the first two guys who actually built the Cyamon digital center. GE digital center, it's a center of excellence. Looking at different kinds of IoT related projects and products along with leading some of the UX and the analytics and the club ration or the social integration. So that's the background. >> So just to set context 'cause this is as we were talking before, there was another era when Steve Jobs was talking about the next work station and he talked about objectory imitation and then everything was sprinkled with fairy dust about objects. So help us distinguish between IoT and digital twins which GE was brilliant in marketing 'cause that concept everyone could grasp. Help us understand where they fit. >> The idea of digital twin is, how do you abstract the actual physical entity out there in the world, and create an object model out of it. So it's very similar in that sense, what happened in the 90s for Steve Jobs and if you look at that object abstraction, is what is now happening in the digital twin space from the IoT angle. The way we look at IoT is we look at every center which is out there which can actually produce a metric on every device which produces a metric we consider as a sense so it could be as simple as the pressure, temperature, humidity sensors or it could be as complicated as cardio sensors and your healthcare and so on and so forth. The concept of bringing these sensors into the to the digital world, the data from that physical world to the digital world is what is making it even more abstract from a programming perspective. >> Help us understand, so it sounds like we're going to have these fire hoses of data. How do we organize that into something that someone who's going to work on that data, someone is going to program to it. How do they make sense out of it the way a normal person looks at a physical object? >> That's a great question. We're looking at sensors as a device that we can measure out of and that we call it a device twin. Taking the data that's coming from the device, we call that as a device twin and then your physical asset, the physical thing itself, which could be elevators, jet engines anything, physical asset that we have what we call the asset twin and there's hierarchical model that we believe that will have to be existing for the digital twin to be actually constructed from an IoT perspective. The asset twins will basically encompass some of the device twins and then we actually take that and represent the digital twin on a physical world of that particular asset. >> So that would be sort of like as we were talking about earlier like an elevator might be the asset but the devices within it might be the bricks and the pulleys and the panels for operating it. >> Veeru: Exactly. >> And it's then the hierarchy of these or in manufacturing terms, the building materials that becomes a critical part of the twin. What are some other components of this digital twin? >> When we talk about digital twin, we don't just take the blueprint as schematics. We also think about the system, the process, the operation that goes along with that physical asset and when we capture that and be able to model that, in the digital world, then that gives you the ability to do a lot of things where you don't have to do it in the physical world. For instance, you don't have to train your people but on the physical world, if it is periodical systems and so on and so forth, you could actually train them in the digital world and then be able to allow them to operate on the physical world whenever it's needed. Or if you want to increase your productivity or efficiency doing predictive models and so forth, you can test all the models in your digital world and then you actually deploy it in your physical world. >> That's great for context setting. How would you think of, this digital twins is more than just a representation of the structure, but it's also got the behavior in there. So in a sense it's a sensor and an actuator in that you could program the real world. What would that look like? What things can you do with that sort of approach? >> So when you actually have the data coming this humongous amount of terabyte data that comes from the sensors, once you model it and you get the insights out of that, based on the insight, you can take an actionable outcome that could be turning off an actuator or turning on an actuator and simple thngs like in the elevator case, open the door, shut the door, move the elevator up, move the elevator down etc. etc All of these things can be done from a digital world. That's where it makes a humongous difference. >> Okay, so it's a structured way of interacting with the highly structured world around us. >> Veeru: That's right. >> Okay, so it's not the narrow definition that many of us have been used to like an airplane engine or the autonomous driving capability of a car. It's more general than that. >> Yeah, it is more general than that. >> Now let's talk about having sort of set context with the definition so everyone knows we're talking about a broader sense that's going on. What are some of the business impacts in terms of operational efficiency, maybe just the first-order impact. But what about the ability to change products into more customizable services that have SLAs or entirely new business models including engineered order instead of make to stock. Tell us something about that hierarchy of value. >> That's a great question. You're talking about things like operations optimization and predicament and all of that which you can actually do from the digital world it's all on digital twin. You also can look into various kinds of business models now instead of a product, you can actually have a service out of the product and then be able to have different business models like powered by the hour, pay per use and kinds of things. So these kinds of models, business models can be tried out. Think about what's happening in the world of Air BnB and Uber, nobody owns any asset but still be able to make revenue by pay per use or power by the hour. I think that's an interesting model. I don't think it's being tested out so much in the physical asset world but I think that could be interesting model that you could actually try. >> One thing that I picked up at the Genius of Things event in Munich in February was that we really have to rethink about software markets in the sense that IBM's customers become in the way your channel, sometimes because they sell to their customers. Almost like a supply chain master or something similar and also pricing changes from potentially we've already migrated or are migrating from perpetual licenses to service softwares or service but now we could do unit pricing or SLA-based pricing, in which case you as a vendor have to start getting very smart about, you owe your customers the risk in meeting an SLA so it's almost more like insurance, actuarial modeling. >> Correct so the way we want think about is, how can we make our customers more, what do you call, monetizable. Their products to be monetizable with their customers and then in that case, when we enter into a service level agreement with our customers, there's always that risk of what we deliver to make their products and services more successful? There's always a risk component which we will have to work with the customers to make sure that combined model of what our customers are going to deliver is going to be more beneficial, more contributing to both bottom line and top line. >> That implies that your modeling, someone's modeling and risk from you the supplier to your customer as vendor to their customer. >> Right. >> That sounds tricky. >> I'm pretty sure we have a lot of financial risk modeling entered into our SLAs when we actually go to our customers. >> So that's a new business model for IBM, for IBM's sort of supply chain master type customers if that's the right word. As this capability, this technology pervades more industries, customers become software vendors or if not software vendors, services vendors for software enhanced products or service enhanced products. >> Exactly, exactly. >> Another thing, I'd listened to a briefing by IBM Global Services where they thought, ultimately, this might end up where there's far more industries are engineered to order instead of make to stock. How would this enable that? >> I think the way we want think about it is that most of the IoT based services will actually start by co-designing and co-developing with your customers. And that's where you're going to start. That's how you're going to start. You're not going to say, here's my 100 data centers and you bring your billion devices and connect and it's going to happen. We are going to start that way and then our customers are going to say, hey by the way, I have these used cases that we want to start doing, so that's why platform becomes so imortant. Once you have the platform, now you can scale, into a scale, individual silos as a vertical use case for them. We provide the platform and the use cases start driving on top of the platform. So the scale becomes much easier for the customers. >> So this sounds like the traditional application. The traditional way an application vendor might turn into a platform vendor which is a difficult transition in itself but you take a few use cases and then generalize into a platform. >> We call that a zone application services. The zone application service is basically, is drawing on perfectly cold platform service which actually provides you the abilities. So for instance like an asset management. An asset management can be done in an oil and gas rig, you can look at asset management in power tub vine, you can can look at asset management in a jet engine. You can do asset management across any different vertical but that is a common horizontal application so most of the time you get 80% of your asset management API's if you will. Then you can be able to scale across multiple different vertical applications and solutions. >> Hold that thought 'cause we're going to come back to joint development and leveraging expertise from vendor and customer and sharing that. Let's talk just at a high level one of the things that I keep hearing is that in Europe industry 4.0 is sort of the hot topic and in the states, it's more digital twins. Help parse that out for us. >> So the way we believe how digital twin should be viewed is a component view. What we mean the component view is that we have your knowledge graph representation of the real assets in the digital world and then you bring in your IoT sensors and connections to the models then you have your functional, logical, physical models that you want to bring into your knowledge graph and then you also want to be able to give the ability of search visualize allies. Kind of an intelligent experience for the end consumer and then you want to bring your similation models when you do the actual similation models in digital to bring it in there and then your enterprise asset management, your ERP systems, all of that and then when you connect, when you're able to build a knowledge graph, that's when the digital twin really connects with your enterprise systems. Sort of bring the OT and the IT together. >> So this is sort of to try and summarize 'cause there are a lot of moving parts in there. You've got you've got the product hierarchy which, in product Kaiser call it building materials, sort of the explosion of parts in an assembly, sub-assembly and then that provides like a structure, a data model then the machine learning models in the different types of models that they could be represent behavior and then when you put a knowledge graph across that structure and behavior, is that what makes it simulation ready? >> Yes, so you're talking about entities and connecting these entities with the actual relationship between these entities. That's the graph that holds the relation between nodes and your links. >> And then integrating the enterprise systems that maybe the lower level operation systems. That's how you effect business processes. >> Correct. >> For efficiency or optimization, automation. >> Yes, take a look at what you can do with like a shop floor optimization. You have all the building materials, you need to know from your existing ERP systems and then you will actually have the actual real parts that's coming to your shop floors to manage them and now base supposing, depending on whether you want to repair, you want to replace, you want an overall, you want to modify whatever that is, you want to look at your existing building materials and see, okay do I first have it do we need more? Do we need to order more? So your auditing system naturally gets integrated into that and then you have to integrate the data that's coming from these models and the availability of the existing assets with you. You can integrate it and say how fast can you actually start moving these out of your shop, into the. >> Okay that's where you translate essentially what's more like intelligent about an object or a rich object into sort of operational implications. >> Veeru: Yes. >> Okay operational process. Let's talk about customer engagement so far. There's intense interest in this. I remember in the Munich event, they were like they had to shut off attendance because they couldn't find a big enough venue. >> Veeru: That's true. >> So what are the characteristics of some of the most successful engagements or the ones that are promising. Maybe it's a little early to say successful. >> So, I think the way you can definitely see success from customer engagement are two fold. One is show what's possible. Show what's possible with after all desire to connect, collection of data, all of that so that one part of it. The second part is understand the customer. The customer has certain requirements in their existing processes and operations. Understand that and then deliver based on what solutions they are expecting, what applications they want to build. How you bring them together is what is, so we're thinking about. That Munich center you talked about. We are actually bringing in chip manufacturers, sensor manufacturers, device manufacturers. We are binging in network providers. We are bringing in SIs, system integrators all of them into the fold and show what is possible and then your partners enable you to get to market faster. That's how we see the engagement with customer should happen in a much more foster manner and show them what's possible. >> It sounds like in the chip industry Moore's law for many years it wasn't deterministic that you we would do double things every 18 months or two years, it was actually an incredibly complex ecosystem web where everyone's sort of product release cycles were synchronized so as to enable that. And it sounds like you're synchronizing the ecosystem to keep up. >> Exactly The saxel of a particular organization IoT efforts is going to depend on how do you build this ecosystem and how do you establish that ecosystem to get to market faster. That's going to be extremely key for all your integration efforts with your customer. >> Let's start narrowly with you. IBM what are the key skills that you feel you need to own starting from sort of the base rocket scientists you know who not only work on machine learning models but they come up with new algorithms on top of say tons of flow work or something like that. And all the way up to the guys who are going to work in conjunction with the customer to apply that science to a particular industry. How does that hold together? >> So it all starts on the platform. On the platform side we have all the developers, the engineers who build these platform all the video connection and all of that to make the connections. So you need the highest software development engineers to build these on the platform and then you also need the solution builders so who is in front of the customer understanding what kind of solutions you want to build. Solutions could be anything. It could be predictive maintenance, it could be as simple as management, it could be remote monitoring and diagnostics. It could be any of these solutions that you want to build and then the solution builders and the platform builders work together to make sure that it's the holistic approach for the customer at the final deployment. >> And how much is the solution builder typically in the early stages IBM or is there some expertise that the customer has to contribute almost like agile development, but not two programmers but like 500 and 500 from different companies. >> 500 is a bit too much. (laughs) I would say this is the concept of co-designing and co-development. We definitely want the ultimate, the developer, the engineers form, the subject exports from our customers and we also need our analytics experts and software developers to come and sit together and understand what's the use case. How do we actually bring in those optimized solution for the customer. >> What level of expertise or what type of expertise are the developers who are contributing to this effort in terms of do they have to, if you're working with manufacturing let's say auto manufacturing. Do they have to have automotive software development expertise or are they more generically analytics and the automotive customer brings in the specific industry expertise. >> It depends. In some cases we have RGB for instance. We have dedicated servers, that particular vertical service provider. We understand some of this industry knowledge. In some cases we don't, in some cases it actually comes from the customer. But it has to be an aggregation of the subject matter experts with our platform developers and solution developers sitting together, finding what's the solution. Literally going through, think about how we actually bring in the UX. What does a typical day of a persona look like? We always by the way believe it's an augmented allegiance which means the human and the machine work together rather than a complete. It gives you the answer for everything you ask for. >> It's a debate that keeps coming up Doug Anglebad sort of had his own answer like 50 years ago which was he sort of set the path for modern computing by saying we're not going to replace people, we're going to augment them and this is just a continuation of that. >> It's a continuation of that. >> Like UX design sounds like someone on the IBM side might be talking to the domain expert and the customer to say how does this workflow work. >> Exactly. So have this design thinking, design sessions with our customers and then based on that we take that knowledge, take it back, we build our mark ups, we build our wire frames, visual designs and the analytics and software that goes behind it and then we provide on top of platform. So most of the platform work, the standard what do you call table state connections, collection of data. All of that as they are already existing then it's one level above as to what the particular solution a customer wants. That's when we actually. >> In terms of getting the customer organization aligned to make this project successful, what are some of the different configurations? Who needs to be a sponsor? Where does budget typically come from? How long are the pilots? That sort of stuff so to set expectations. >> We believe in all the agile thinking, agile development and we believe in all of that. It's almost given now. So depending on where the customer comes from so the customer could actually directly come and sign up to our platform on the existing cloud infrastructure and then they will say, okay we want to build applications then there are some customers really big customers, large enterprises who want to say, give me the platform, we have our solution folks. We will want to work on board with you but we also want somebody who understands building solutions. We integrate with our solution developers and then we build on top of that. They build on top of that actually. So you have that model as well and then you have a GBS which actually does this, has been doing this for years, decades. >> George: Almost like from the silicon. >> All the way up to the application level. >> When the customer is not outsourcing completely, The custom app that they need to build in other words when when they need to go to GBS Global Business Services, whereas if they want a semi-packaged app, can they go to the industry solutions group? >> Yes. >> I assume it's the IoT, Industry Solutions Group. >> Solutions group, yes. >> They then take a it's almost maybe a framework or an existing application that needs customization. >> Exactly so we have IoT-4. IoT for manufacturing, IoT for retail, IoT for insurance IoT for you name it. We have all these industry solutions so there would be some amount of template which is already existing in some fashion so when GBS gets a request to say here is customer X coming and asking for a particular solution. They would come back to IoT solutions group to say, they already have some template solutions from where we can start from rather than building it from scratch. You speed to market again is much faster and then based on that, if it's something that is to be customizable, both of them work together with the customer and then make that happen, and they leverage our platform underneath to do all the connection collection data analytics and so on and so forth that goes along with that. >> Tell me this from everything we hear. There's a huge talent shortage. Tell me in which roles is there the greatest shortage and then how do different members of the ecosystem platform vendors, solution vendors sort of a supply-chain master customers and their customers. How do they attract and retain and train? >> It's a fantastic question. One of the difficulties both in the valley and everywhere across is that three is a skill gap. You want advanced data scientists you want advances machinery experts, you want advanced AI specialists to actually come in. Luckily for us, we have about 1000 data scientists and AI specialists distributed across the globe. >> When you say 1000 data scientists and AI specialists, help us understand which layer are they-- >> It could be all the way from like a BI person all the way to people who can build advanced AI models. >> On top of an engine or a framework. >> We have our Watson APIs from which we build then we have our data signs experience which actually has some of the models then built on top of what's in the data platform so we take that as well. There are many different ways by which we can actually bring the AM model missionary models to build. >> Where do you find those people? Not just the sort of band strengths that's been with IBM for years but to grow that skill space and then where are they also attracted to? >> It's a great question. The valley definitely has a lot of talent, then we also go outside. We have multiple centers of excellence in Israel, in India, in China. So we have multiple centers of excellence we gather from them. It's difficult to get all the talent just from US or just from one country so it's naturally that talent has to be much more improvement and enhanced all the wat fom fresh graduates from colleges to more experienced folks in the in the actual profession. >> What about when you say enhancing the pool talent you have. Could it also include productivity improvements, qualitative productivity improvements in the tools that makes machine learning more accessible at any level? The old story of rising obstruction layers where deep learning might help design statistical models by doing future engineering and optimizing the search for the best model, that sort of stuff. >> Tools are very, very hopeful. There are so many. We have from our tools to python tools to psychic and all of that which can help the data scientist. The key part is the knowledge of the data scientist so data science, you need the algorithm, the statistical background, then you need your applications software development background and then you also need the domestics for engineering background. You have to bring all of them together. >> We don't have too many Michaelangelos who are these all around geniuses. There's the issue of, how do you to get them to work more effectively together and then assuming even each of those are in short supply, how do you make them more productive? >> So making them more productive is by giving them the right tools and resources to work with. I think that's the best way to do it, and in some cases in my organization, we just say, okay we know that a particular person is skilled is up skilled in certain technologies and certain skill sets and then give them all the tools and resources for them to go on build. There's a constant education training process that goes through that we in fact, we have our entire Watson ED platform that can be learned on Kosera today. >> George: Interesting. >> So people can go and learn how to build a platform from a Kosera. >> When we start talking with clients and with vendors, things we hear is that and we were kind of I think early that calling foul but in the open source infrastructure big data infrastructure this notion of mix-and-match and roll your own pipeline sounded so alluring, but in the end it was only the big Internet companies and maybe some big banks and telcos that had the people to operate that stuff and probably even fewer who could build stuff on it. Do we do we need to up level or simplify some of those roles because mainstream companies can't have enough or won't will have enough data scientists or other roles needed to make that whole team work >> I think it will be a combination of both one is we need to up school our existing students with the stem background, that's one thing and the other aspect is, how do you up scale your existing folks in your companies with the latest tools and how can you automate more things so that people who may not be schooled will still be able to use the tool to deliver other things but they don't have to go to a rigorous curriculum to actually be able to deal with it. >> So what does that look like? Give us an example. >> Think of tools like today. There are a lot of BI folks who can actually build. BI is usually your trends and graphs and charts that comes out of the data which are simple things. So they understand the distribution and so on and so forth but they may not know what is the random model. If you look at tools today, that actually gives you to build them, once you give the data to that model, it actually gives you the outputs so they don't really have to go dig deep I have to understand the decision tree model and so on and so forth. They have the data, they can give the data, tools like that. There are so many different tools which would actually give you the outputs and then they can actually start building app, the analytics application on top of that rather than being worried about how do I write 1000 line code or 2000 line code to actually build that model itself. >> The inbuilt machine learning models in and intend, integrated to like pentaho or what's another example. I'm trying to think, I lost my, I having a senior moment. These happen too often now. >> We do have it in our own data science tools. We already have those models supported. You can actually go and call those in your web portal and be able to call the data and then call the model and then you'll get all that. >> George: Splank has something like that. >> Splank does, yes. >> I don't know how functional it is but it seems to be oriented towards like someone who built a dashboard can sort of wire up a model, it gives you an example of what type of predictions or what type of data you need. >> True, in the Splank case, I think it is more of BI tool actually supporting a level of data science moral support on the back. I do not know, maybe I have to look at this but in our case we have a complete data science experience where you actually start from the minute the data gets ingested, you can actually start the storage, the transformation, the analytics and all of that can be done in less than 10 lines of coding. You can just actually do the whole thing. You just call those functions then it will the right there in front of you. So in twin you can do that. That I think is much more powerful and there are tools, there are many many tools today. >> So you're saying that data science experience is an enter in pipeline and therefore can integrate what were boundaries between separate products. >> The boundary is becoming narrower and narrower in some sense. You can go all the way from data ingestion to the analytics in just few clicks or few lines of course. That's what's happening today. Integrated experience if you will. >> That's different from the specialized skills where you might have a tri-factor, prexada or something similar as for the wrangling and then something else for sort of the the visualizations like Altracks or Tavlo and then into modeling. >> A year or so ago, most of data scientists try to spend a lot of time doing data wrangling because some of the models, they can actually call very directly but the wrangling is actually where they spend their time. How do you get the data crawl the data, cleanse the data, etc. That is all now part of our data platform. It is already integrated into the platform so you don't have to go through some of these things. >> Where are you finding the first success for that tool suite? >> Today it is almost integrated with, for instance, I had a case where we exchange the data we integrate that into what's in the Watson data platform and the Watson APIs is a layer above us in the platform where we actually use the analytics tools, more advanced AI tools but the simple machinery models and so on and so forth is already integrated into as part of the Watson data platform. It is going to become an integrated experience through and through. >> To connect data science experience into eWatson IoT platform and maybe a little higher at this quasi-solution layer. >> Correct, exactly. >> Okay, interesting. >> We are doing that today and given the fact that we have so much happening on the edge side of things which means mission critical systems today are expecting stream analysts to get to get insights right there and then be able to provide the outcomes at the edge rather than pushing all the data up to your cloud and then bringing it back down. >> Let's talk about edge versus cloud. Obviously, we can't for latency and band width reasons we can't forward all the data to the cloud, but there's different use cases. We were talking to Matasa Harry at Sparks Summit and one of the use cases he talked about was video. You can't send obviously all the video back and you typically on an edge device wouldn't have heavy-duty machine learning, but for video camera, you might want to learn what is anomalous or behavior call out for that camera. Help us understand some of the different use cases and how much data do you bring back and how frequently do retrain the models? >> In the case of video, it's so true that you want to do a lot of any object ignition and so on and so forth in the video itself. We have tools today, we have cameras outside where if a van goes it detect the particular object in the video live. Realtime streaming analytics so we can do that today. What I'm seeing today in the market is, in the transaction between the edge and the cloud. We believe edge is an extension of the cloud, closer to the asset or device and we believe that models are going to get pushed from the cloud, closer to the edge because the compute capacity and storage and the networking capacity are all improving. We are pushing more and more computing to their devices. >> When you talk about pushing more of the processing. you're talking more about predicts and inferencing then the training. >> Correct. >> Okay. >> I don't think I see so much of the training needs to be done at the edge. >> George: You don't see it. >> No, not yet at least. We see the training happening in the cloud and then once a train, the model has been trained, then you come to a steady, steady model and then that is the model you want to push. When you say model, it could be a bunch of coefficients. That could be pushed onto the edge and then when a new data comes in, you evaluate, make decisions on that, create insights and push it back as actions to the asset and then that data can be pushed back into the cloud once a day or once in a week, whatever that is. Whatever the capacity of the device you have and we believe that edge can go across multiple scales. We believe it could be as small with 128 MB it could be one or two which I see sitting in your local data center on the premise. >> I've had to hear examples of 32 megs in elevators. >> Exactly. >> There might be more like a sort of bandwidth and latency oriented platform at the edge and then throughput and an volume in the cloud for training. And then there's the issue of do you have a model at the edge that corresponds to that instance of a physical asset and then do you have an ensemble meaning, the model that maps to that instance, plus a master canonical model. Does that work for? >> In some cases, I think it'll be I think they have master canonical model and other subsidiary models based on what the asset, it could be a fleet so you in the fleet of assets which you have, you can have, does one asset in the fleet behave similar to another asset in the fleet then you could build similarity models in that. But then there will also be a model to look at now that I have to manage this fleet of assets which will be a different model compared to action similarity model, in terms of operations, in terms of optimization if I want to make certain operations of that asset work more efficiently, that model could be completely different with when compared to when you look at similarity of one model or one asset with another. >> That's interesting and then that model might fit into the information technology systems, the enterprise systems. Let's talk, I want to go get a little lower level now about the issue of intellectual property, joint development and sharing and ownership. IBM it's a nuanced subject. So we get different sort of answers, definitive answers from different execs, but at this high level, IBM says unlike Google and Facebook we will not take your customer data and make use of it but there's more to it than that. It's not as black-and-white. Help explain that for so us. >> The way you want to think is I would definitely paired back what our chairman always says customers' data is customers' data, customer insights is customer insights so they way we look at it is if you look at a black box engine, that could be your analytics engine, whatever it is. The data is your inputs and the insights are our outputs so the insights and outputs belong to them. we don't take their data and marry it with somebody else's data and so forth but we use the data to train the models and the model which is an abstract version of what that engine should be and then more we train the more better the model becomes. And then we can then use across many different customers and as we improve the models, we might go back to the same customers and hey we have an improved model you want to deploy this version rather than the previous version of the model we have. We can go to customer Y and say, here is a model which we believe it can take more of your data and fine tune that model again and then give it back to them. It is true that we don't actually take their data and share the data or the insights from one customer X to another customer Y but the models that make it better. How do you make that model more intelligent is what out job is and that's what we do. >> If we go with precise terminology, it sounds like when we talk about the black box having learned from the customer data and the insights also belonging to the customer. Let's say one of the examples we've heard was architecture engineering consulting for large capital projects has a model that's coming obviously across that vertical but also large capital projects like oil and gas exploration, something like that. There, the model sounds like it's going to get richer with each engagement. And let's pin down so what in the model is sort of not exposed to the next customer and what part of the model that has gotten richer does the next customer get the balance of? >> When we actually build a model, when we pass the data, in some cases, customer X data, the model is built out of customer X data may not sometimes work with the customer Y's data so in which case you actually build it from scratch again. Sometimes it doesn't. In some case it does help because of the similarity of the data in some instance because if the data from company X in oil gas is similar to company Y in oil gas, sometimes the data could be similar so in which case when you train that model, it becomes more efficient and the efficiency goes back to both customers. we will do that but there are places where it would really not work. What we are trying to do is. We are in fact trying to build some kind of knowledge bundles where we can actually what used to be a long process to train the model can ow shortened using that knowledge bundle of what we have actually gained. >> George: Tell me more about how it works. >> In retail for instance, when we actually provide analytics, from any kind of IoT sense, whatever sense of data this comes in we train the model, we get analytics used for ads, pushing coupons, whatever it is. That knowledge, what you have gained off that retail, it could be models of models, it could be metamodels, whatever you built. That can actually serve many different customers but the first customer who is trying to engage with us, you don't have any data to the model. It's almost starting from ground zero and so that would actually take a longer time when you are starting with a new industry and you don't have the data, it'll take you a longer time to understand what is that saturation point or optimization point where you think the model cannot go any further. In some cases, once you do that, you can take that saturated model or near saturated model and improve it based on more data that actually comes from different other segments. >> When you have a model that has gotten better with engagements and we've talked about the black box which produces the insights after taking in the customer data. Inside that black box there's like at the highest level we might call it the digital twin with the broad definition that we started with, then there's a data model which a data model which I guess could also be incorporated into the knowledge graft for the structure and then would it be fair to call the operational model the behavior? >> Yes, how does the system perform or behave with respect the data and the asset itself. >> And then underpinning that, the different models that correspond to the behaviors of different parts of this overall asset. So if we were to be really precise about this black box, what can move from one customer to the next and what what won't? >> The overall model, supposing I'm using a random data retrieval model, that remains but actual the coefficients are the feature rector, or whatever I use, that could be totally different for customers, depending on what kind of data they actually provide us. In data science or in analytics you have a whole platora of all the way from simple classification algorithms to very advanced predictive modeling algorithms. If you take the whole class when you start with a customer, you don't know which model is really going to work for a specific user case because the customer might come and can say, you might get some idea but you will not know exactly this is the model that will work. How you test it with one customer, that model could remain the same kind of use case for some of other customer, but that actual the coefficients the degree of the digital in some cases it might be two level decision trees, in others case it might be a six level decision tree. >> It is not like you take the model and the features and then just let different customers tweak the coefficients for the features. >> If you can do that, that will be great but I don't know whether you can really do it the data is going to change. The data is definitely going to change at some point of time but in certain cases it might be directly correlated where it can help, in certain cases it might not help. >> What I'm taking away is this is fundamentally different from traditional enterprise applications where you could standardize business processes and the transactional data that they were producing. Here it's going to be much more bespoke because I guess the processes, the analytic processes are not standardized. >> Correct, every business processes is unique for a business. >> The accentures of the world we're trying to tell people that when SAP shipped packaged processes, which were pretty much good enough, but that convince them to spend 10 times as much as the license fee on customization. But is there a qualitative difference between the processes here and the processes in the old ERP era? I think it's kind of different in the ERP era and the processes, we are more talking about just data management. Here we're talking about data science which means in the data management world, you're just moving data or transforming data and things like that, that's what you're doing. You're taking the data. transforming to some other form and then you're doing basic SQL queries to get some response, blah blah blah. That is a standard process that is not much of intelligence attached to it but now you are trying to see from the data what kind of intelligence can you derive by modeling the characteristics of the data. That becomes a much tougher problem so it now becomes one level higher of intelligence that you need to capture from the data itself that you want to serve a particular outcome from the insights you get from is model. >> This sounds like the differences are based on one different business objectives and perhaps data that's not as uniform that you would in enterprise applications, you would standardize the data here, if it's not standardized. >> I think because of the varied the disparity of the businesses and the kinds of verticals and things like that you're looking at, to get complete unified business model, is going to be extremely difficult. >> Last question, back-office systems the highest level they got to were maybe the CFO 'cause you had a sign off on a lot of the budget for the license and a much much bigger budget for the SI but he was getting something that was like close you quarter in three days or something instead of two weeks. It was a control function. Who do you sell to now for these different systems and what's the message, how much more strategic how do you sell the business impact differently? >> The platforms we directly interact with the CIO and CTOs or the head of engineering. And the actual solutions or the insights, we usually sell it to the COOs or the operational folks. So because the COO is responsible for showing you productivity, efficiency, how much of savings can you do on the bottom line top line. So the insights would actually go through the COOs or in some sense go through their CTOs to COOs but the actual platform itself will go to the enterprise IT folks in that order. >> This sounds like it's a platform and a solution sell which requires, is that different from the sales motions of other IBM technologies or is this a new approach? >> IBM is transforming on its way. The days where we believe that all the strategies and predictives that we are aligned towards, that actually needs to be the key goal because that's where the world is going. There are folks who, like Jeff Boaz talks about in the olden days you need 70 people to sell or 70% of the people to sell a 30% product. Today it's a 70% product and you need 30% to actually sell the product. The model is completely changing the way we interact with customers. So I think that's what's going to drive. We are transforming that in that area. We are becoming more conscious about all the strategy operations that we want to deliver to the market we want to be able to enable our customers with a much broader value proposition. >> With the industry solutions group and the Global Business Services teams work on these solutions. They've already been selling, line of business CXO type solutions. So is this more of the same, it's just better or is this really higher level than IBM's ever gotten in terms of strategic value? >> This is possibly in decades I would say a high level of value which come from a strategic perspective. >> Okay, on that note Veeru, we'll call it a day. This is great discussion and we look forward to writing it up and clipping all the videos and showering the internet with highlights. >> Thank you George. Appreciate it. >> Hopefully I will get you back soon. >> I was a pleasure, absolutely. >> With that, this George Gilbert. We're in our Palo Alto studio for wiki bond and theCUBE and we've been talking to Veeru Ramaswamy who's VP of Watson IoT platform and we look forward to coming back with Veeru sometime soon. (upbeat music)

>> Announcer: Live from Fisherman's Wharf in San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey Welcome back everybody. Jeff Frick with theCUBE, along with Peter Burris from Wikibon. We are in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. Coming to the end of a busy day, running out of steam. Blah, blah, blah. I need more water. But Joe's going to take us home. We're joined by Joe Selle. He is the global operations analytic solution lead for IBM. Joe, welcome. >> Thank you, thank you very much. It's great to be here. >> So you've been in sessions all day. I'm just curious to get kind of your general impressions of the event and any surprises or kind of validations that are coming out of these sessions. >> Well, general impression is that everybody is thrilled to be here and the participants, the speakers, the audience members all know that they're at the cusp of a moment in business history of great change. And that is as we graduate from regular analytics which are descriptive and dashboarding into the world of cognitive which is taking the capabilities to a whole other level. Many levels actually advanced from the basic things. >> And you're in a really interesting position because IBM has accepted the charter of basically consuming your own champagne, drinking your own champagne, whatever expression you want to use. >> I'm so glad you said that cause most people say eating your dog food. >> Well, if we were in Germany we'd talk about beer, but you know, we'll stick with the champagne analogy. But really, trying to build, not only to build and demonstrate the values that you're trying to sell to your customers within IBM but then actually documenting it and delivering it basically, it's called the blueprint, in October. We've already been told it's coming in October. So what a great opportunity. >> Part of that is the fact that Ginni Rometty, our CEO, had her start in IBM in the consulting part of IBM, GBS, Global Business Services. She was all about consulting to clients and creating big change in other organizations. Then she went through a series of job roles and now she's CEO and she's driving two things. One is the internal transformation of IBM, which is where I am, part of my role is, I should say. Reporting to the chief data officer and the chief analytics officer and their jobs are to accelerate the transformation of big blue into the cognitive era. And Ginni also talks about showcasing what we're doing internally for the rest of the world and the rest of the economy to see because parts of this other companies can do. They can emulate our road map, the blueprint rather, sorry, that Inderpal introduced, is going to be presented in the fall. That's our own blueprint for how we've been transforming ourselves so, some part of that blueprint is going to be valid and relevant for other companies. >> So you have a dual reporting relationship, you said. The chief data officer, which is this group, but also the chief analytics officer. What's the difference between the Chief data officer, the chief data analytics officer and how does that combination drive your mission? >> Well, the difference really is the chief data officer is in charge of making some very long-term investments, including short-term investments, but let me talk about the long-term investment. Anything around an enterprise data lake would be considered a long-term investment. This is where you're creating an environment where users can go in, these would be internal to IBM or whatever client company we're talking about, where they can use some themes around self-service, get out this information, create analysis, everything's available to them. They can grab external data. They can grab internal data. They can observe Twitter feeds. They can look at weather company information. In our case we get that because we're partnered with the weather company. That's the long-term vision of the chief data officer is to create a data lake environment that serves to democratize all of this for users within a company, within IBM. The chief analytics officer has the responsibility to deliver projects that are sort of the leading projects that prove out the value of analytics. So on that side of my dual relationship, we're forming projects that can deliver a result literally in a 10 or a 12 week time period. Or a half a year. Not a year and a half but short term and we're sprinting to the finish, we're delivering something. It's quite minimally scaled. The first project is always a minimally viable product or project. It's using as few data sources as we can and still getting a notable result. >> The chief analytics officer is at the vanguard of helping the business think about use cases, going after those use cases, asking problems the right way, finding data with effectiveness as well as efficiency and leading the charge. And then the Chief data officer is helping to accrete that experience and institutionalize it in the technology, the practices, the people, et cetera. So the business builds a capability over time. >> Yes, scalable. It's sort of an issue of it can scale. Once Inderpal and the Chief data officer come to the equation, we're going to scale this thing massively. So, high volume, high speed, that's all coming from a data lake and the early wins and the medium term wins maybe will be more in the realm of the chief analytics officer. So on your first summary a second ago, you're right in that the chief analytics officer is going around, and the team that I'm working with is doing this, to each functional group of IBM. HR, Legal, Supply Chain, Finance, you name it, and we're engaging in cognitive discovery sessions with them. You know, what is your roadmap? You're doing some dashboarding now, you're doing some first generation analytics or something but, what is your roadmap for getting cognitive? So we're helping to burst the boundaries of what their roadmap is, really build it out into something that was bigger then they had been conceiving of it. Adding the cognitive projects and then, program managing this giant portfolio so that we're making some progress and milestones that we can report to various stake holders like Ginni Rometty or Jim Kavanaugh who are driving this from a senior senior executive standpoint. We need to be able to tell them, in one case, every couple of weeks, what have you gotten done. Which is a terrible cadence, by the way, it's too fast. >> So in many Respects-- >> But we have to get there every couple of weeks we've got to deliver another few nuggets. >> So in many respects, analytics becomes the capability and data becomes the asset. >> Yes, that's true. Analytics has assets as well though. >> Paul: Sure, of course. >> Because we have models and we have techniques and we bake the models into a business process to make it real so people actually use it. It doesn't just sit over there as this really nifty science experiment. >> Right but kind of where are we on the journey? It's real still early days, right? Because, you know, we hear all the time about machine learning and deep learning and AI and VR and AI and all this stuff. >> We're patchy, every organization is patchy even IBM, but I'm learning from being here, so this is end of day one, I'm learning. I'm getting a little more perspective on the fact that we at IBM are actually, 'cause we've been investing in this heavily for a number of years. I came through the ranks and supply chain. We've been investing in these capabilities for six or seven years. We were some of the early adopters within IBM. But, I would say that maybe 10% of the people at this conference are sort of in the category of I'm running fast and I'm doing things. So that's 10%. Then there's maybe another 30% that are jogging or fast walking. And then there's the rest of them, so maybe 50%, if my math is right, it's been a long day. Are kind of looking and saying, yeah, I got to get that going at some point and I have two or three initiatives but I'm really looking forward to scaling it at some point. >> Right. >> I've just painted a picture to you of the fact that the industry in general is just starting this whole journey and the big potential is still in front of us. >> And then on the Champagne. So you've got the cognitive, you've got the brute and then you've got the Watson. And you know, there's a lot of, from the outside looking in at IBM, there's a lot of messaging about Watson and a lot of messaging about cognitive. How the two mesh and do they mesh within some of the projects that you're working on? Or how should people think of the two of them? >> Well, people should know that Watson is a brand and there are many specific technologies under the Watson brand. So, and then, think of it more as capabilities instead of technologies. Things like being able to absorb unstructured information. So you've heard, if you've been to any conferences, whether they're analytics or data, any company, any industry, 80% of your data is unstructured and invisible and you're probably working with 20% of your data on an active basis. So, do you want to go the 80%-- >> With 40% shrinking. >> As a percentage. >> That's true. >> As a percentage. >> Yeah because the volumes are growing. >> Tripling in size but shrinking as a percentage. >> Right, right. So, just, you know, think about that. >> Is Watson really then kind of the packaging of cognitive, more specific application? Because we're walking for health or. >> I'll tell you, Watson is a mechanism and a tool to achieve the outcome of cognitive business. That's a good way to think of it. And Watson capabilities that I was just about to get to are things like reading, if you will. In Watson Health, he reads oncology articles and they know, once one of them has been read, it's never forgotten. And by the way, you can read 200 a week and you can create the smartest doctor that there is on oncology. So, a Watson capability is absorbing information, reading. It's in an automated fashion, improving its abilities. So these are concepts around deep learning and machine learning. So the algorithms are either self correcting or people are providing feedback to correct them. So there's two forms of learning in there. >> Right, right. >> But these are kind of capabilities all around Watson. I mean, there are so many more. Optical, character recognition. >> Right. >> Retrieve and rank. >> Right. >> So giving me a strategy and telling me there's an 85% chance, Joe, that you're best move right now, given all these factors is to do x. And then I can say, well, x wouldn't work because of this other constraint which maybe the system didn't know about. >> Jeff: Right. >> Then the system will tell me, in that case, you should consider y and it's still an 81% chance of success verses the first which was at 85. >> Jeff: Right. >> So retrieving and ranking, these are capabilities that we call Watson. >> Jeff: Okay. >> And we try to work those in to all the job roles. >> Jeff: Okay. >> So again, whether you're in HR, legal, intellectual property management, environmental compliance. You know, regulations around the globe are changing all the time. Trade compliance. And if you violate some of these rules and regs, then you're prohibited from doing business in a certain geography. >> Jeff: Right. >> It's devastating. The stakes are really high. So these are the kind of tools we want. >> So I'm just curious, from your perspective, you've got a corporate edict behind you at the highest level, and your customers, your internal customers, have that same edict to go execute quickly. So given that you're not in that kind of slow moving or walking or observing half, what are the biggest challenges that you have to overcome even given the fact that you've got the highest level most senior edict both behind you as well as your internal customers. >> Yeah, well it, guess what, it comes down to data. Often, a lot of times, it comes to data. We can put together an example of a solution that is a minimally viable solution which might have only three or four or five different pieces of data and that's pretty neat and we can deliver a good result. But if we want to scale it and really move the needle so that it's something that Ginni Rometty sees and cares about, or a shareholder, then we have to scale. Then we need a lot of data, so then we come back to Inderpal, and the chief data officer role. So the constraint is on many of the programs and projects is if you want to get beyond the initial proof of concept, >> Jeff: Right. >> You need to access and be able to manipulate the big data and then you need to train these cognitive systems. This is the other area that's taking a lot of time. And I think we're going to have some technology and innovation here, but you have to train a cognitive system. You don't program it. You do some painstaking back and forth. You take a room full of your best experts in whatever the process is and they interact with the system. They provide input, yes, no. They rank the efficacy of the recommendations coming out of the system and the system improves. But it takes months. >> That's even the starting point. >> Joe: That's a problem. >> And then you trade it over often, an extended period of time. >> Joe: A lot of it gets better over time. >> Exactly. >> As long as you use this thing, like a corpus of information is built and then you can mine the corpus. >> But a lot of people seem to believe that you roll all this data, you run a bunch of algorithms and suddenly, boom, you've got this new way of doing things. And it is a very very deep set of relationships between people who are being given recommendations as you said, weighing them, voting them, voting on them, et cetera. This is a highly interactive process. >> Yeah, it is. If you're expecting lightning fast results, you're really talking about a more deterministic kind of solution. You know, if/then. If this is, then that's the answer. But we're talking about systems that understand and they reason and they tap you on the shoulder with a recommendation and tell you that there's an 85% chance that this is what you should do. And you can talk back to the system, like my story a minute ago, and you can say, well it makes sense, but, or great, thanks very much Watson, and then go ahead and do it. Those systems that are expert systems that have expertise just woven through them, you cannot just turn those on. But, as I was saying, one of the things we talked about on some of the panels today, was there's new techniques around training. There's new techniques around working with these corpuses of information. Actually, I'm not sure what the plural of corpus. Corpi? It's not Corpi. >> Jeff: I can look that up. >> Yeah, somebody look that up. >> It's not corpi. >> So anyway, I want to give you the last word, Jeff. So you've been doing this for a while, what advice would you give to someone kind of in your role at another company who's trying to be the catalyst to get these things moving. What kind of tips and tricks would you share, you know, having gone through it and working on this for a while? >> Sure. I would, the first thing I would do is, in your first move, keep the projects tightly defined and small with a minimum of input and keep, contain your risk and your risk of failure, and make sure that if you do three projects, at least one of them is going to be a hands down winner. And then once you have a winner, tout it through your organization. A lot of folks get so enamored with the technology that they start talking more about the technology than the business impact. And what you should be touting and bragging about is not the fact that I was able to simultaneously read 5,000 procurement contracts with this tool, you should be saying, it used to take us three weeks in a conference room with a team of one dozen lawyers and now we can do that whole thing in one week with six lawyers. That's what you should talk about, not the technology piece of it. >> Great, great. Well thank you very much for sharing and I'm glad to hear the conference is going so well. Thank you. >> And it's Corpa. >> Corpa? >> The answer to the question? Corpa. >> Peter: Not corpuses. >> With Joe, Peter, and Jeff, you're watching theCUBE. We'll be right back from the IBM chief data operator's strategy summit. Thanks for watching.

>> Announcer: Live from Las Vegas, it's theCUBE, covering Interconnect 2017. Brought to you by IBM. >> Hey, welcome back everyone. We're live here in Las Vegas at the Mandalay Bay for IBM InterConnect 2017 exclusive Cube coverage. I'm John Furrier, my co-host Dave Vellante, our next guest Dr. Angel Diaz who is the vice president of developer technology. Also you know him from the open source world. Great to see you again. >> Nice to see you. Thanks for spending time with us. >> Thank you. >> Boy, Blockchain, open source, booming, cloud-native, booming, hybrid cloud, brute force but rolling strong. Enterprise strong, if you will, as your CEO Ginni Rometty started talking about yesterday. Give us the update on what's going on with the technology and developers because this is something that you guys, you personally, have been spending a lot of time with. Developer traction, what's the update? >> Well you know if you look at history there's been this democratization of technology. Right, everything from object oriented programming to the internet where we realize if we created open communities you can build more skill, more value, create more innovation. And each one of these layers you create abstractions. You reduce the concept count of what developers need to know to get work done and it's all about getting work done faster. So, you know, we've been systematically around cloud, data, and AI, working really hard to make sure that you have open source communities to support those. Whether it's in things like compute, storage, and network, platform as a service like say Cloud Foundry, what we're doing around the open container initiatives and the Cloud Native Computing Foundation to all the things you see in the data space and everywhere else. So it's real exciting and it's real important for developers. >> So two hot trends that we're tracking obviously, one's pretty obvious. That's machine learning in cloud. Really hand and glove together. You see machine learning really powering the AI, hitting IOT all the way up to apps and wearables and what not, autonomous vehicles. Goes on and on. The other one is Kubernetes, and Kubernetes, the rise of Kubernetes has really brought the containers to a whole nother level around multi-cloud. People might not know it, but you are involved in the CNCF formation, which is Kubernetes movement, which was KubeCon, then it became part of the Linux Foundation. So, IBM has had their hand in these two trends pretty heavily. >> Angel: Oh yeah, absolutely. >> Give the perspective, because the Kubernetes one, in particular, we'll come back to the machine learning, but Kubernetes is powering a whole nother abstraction layer around helping containers go to the next level with microservices, where the develop equation has changed. It's not just the person writing code anymore, a person writing code throws off an application that has it's own life in relationship to other services in the community, which also has analytics tied to it. So, you're seeing a changing dynamic on this potential with Kubernetes. How important is Kubernetes, and what is the real impact? >> No, it is important. And what there actually is, there's a couple of, I think, application or architecture trends that are fundamentally changing how we build applications. So one of them I'll call, let's call it Code First. This is where you don't even think about the Kubernetes layer. All you do is you want to write your code and you want to deploy your code, and you want it to run. That's kind of the platform. Something like Cloud Foundry addresses the Code First approach. Then there's the whole event-drive architecture world. Serverless, right? Where it has a particular use case, event-driven, standing, stuff up and down, dealing with many types of inputs, running rules. Then you have, let's say the more transactional type applications. Microservices, right? These three thing, when combined allows you to kind of break the shackles of the monolith of old application architectures, and build things the way that best suit your application model, and then come together in much more coherent way. Specifically in Kubernetes, and that whole container stuff. You think think about it, initially, when, containers have been around a long time, as we all know, and Docker did a great job in making container accessible and easy, right? And we worked really closely with them to create some multisource activities around the base container definitions, the open container initiative in the Linux Foundation. But of course, that wasn't enough. We need to then start to build the management and the orchestration around that. So we teamed up with others and started to kind of build this Kubernetes-based community. You know, Docker just recently brought ContainerD into the CNCF, as well, as another layer. They are within the equation. But by building this, it's almost just Russian doll of capability, right, you know, you're able to go from one paradigm, whether it's a serverless paradigm running containers, or having your microservices become use in serverless or having Code First kick off something, you can have these things work well together. And I think that's the most exciting part of what we're doing at Kubernetes, what we're doing in serverless, and what we're doing, say, in this Code First world. >> So, development's always been kind of an art form. How is that art form evolving and changing as these trends that you're describing-- >> Oh, that's a great, I love that. 'Cause I always think of ourselves as computer science artists. You and I haven't spoken about that. That's awesome. Yeah, because, you know, it is an art form, right? Your screen is your canvas, right, and colors are the services that you can bring in to build, and the API calls, right? And what's great is that your canvas never ends, because you have, say, a cloud infrastructure, which is infinitely scalable or something, right? So, yeah. But the definition of the developer is changing because we're kind of in this next phase of lowering concept count. Remember I told you this lowering of concept count. You know, I love those O'Reilly books. The little cute animals. You know, as a developer today, you don't have to buy as many of those books, because a lot of it is done in the API calls that you've used. You don't write sorting algorithms anymore. Guess what, you don't need to do speech to text algorithms. You don't need to do some analysis algorithms. So the developer is becoming a cognitive developer and a data science developer, in addition to a application developer. And that is the future. And it's really important that folks skill up. Because the demand has increased dramatically in those areas, and the need has increased as well. So it's very exciting. >> So the thing about that, that point about cognitive developer, is that in the API calls, and the reason why we don't buy all those books is, the codes out there are already in open source and machine learning is a great example, if you look at what machine learning is doing. 'Cause now you have machine learning. It used to be an art and a science. You had to be a great computer scientist and understand algorithms, and almost have that artistic view. But now, as more and more machine learning comes out, you can still write custom machine learning, but still build on libraries that are already out there. >> Exactly. So what does that do? That reduces the time it takes to get something done. And it increases the quality of what you're building, right? Because, you know, this subroutine or this library has been used by thousands and thousands of other people, it's probably going to work pretty well for your use case, right? But I can stress the importance of this moment you brought up. The cognitive data application developer coming together. You know, when the Web happened, the development market blew up in orders of magnitude. Because everybody's is sort of learning HTML, CSS, Javascript, you know, J2E, whatever. All the things they needed to build, you know, Web Uize and transactional applications. Two phase commit apps in the back, right? Here we are again, and it's starting to explode with the microservices, et cetera, all the stuff you mentioned, but when you add cognitive and data to the equation, it's just going to be a bigger explosion than the Web days. >> So we were talking with some of the guys from IBM's GBS, the Global Business Services, and the GTS, Global Technology Services, and interesting things coming out. So if you take what you're saying forward, and you open innovation model, you got business model stacks and technology stacks. So process, stacks, you know, business process, and then technology, and they now have to go hand-in-hand. So if you take what you're saying about, you know, open source, open all of this innovation, and add say, Blockchain to it, you now have another developer type. So the cognitive piece is also contributing to what looks like to be a home run with Blockchain going open source, with the ledger. So now you have the process and the stacks coming together. So now, it's almost the Holy Grail. It used to be this, "Hey, those business processor guys, they did stuff, and then the guys coded it out, built stacks. Now they're interdependent a bit. >> Yeah. Well I mean, what's interesting to me about Blockchain, I always think of, at this point about business processes, you know, business processes have always been hard to change, right? You know, once you have partners in your ecosystem, it's hard to change. Things like APIs and all the technology allows it to be much quicker now. But with Blockchain, you don't need a human involved in the decision of who's in your partner network as long as they're trusted, right? I remember when Jerry Cuomo and Chris Ferris, in my team, he's the chairman of the Blockchain, of the hyperledger group, we're talking initially when we kind of brought it to the Linux Foundation. We were talking a lot about transactions, because you know, that was one of the initial use cases. But we always kind of new that there's a lot of other use cases for this, right, in addition to that. I mean, you know, the government of China is using Blockchain to deal with carbon emissions. And they have, essentially, an economy where folks can trade, essentially, carbon units to make sure that as an industry segment, they don't go over, as an example. So you can have people coming in and out of your business process in a much more fluid way. What fascinates me about Blockchain, and it's a great point, is it takes the whole ecosystem to another level because now that they've made Blockchain successful, ecosystem component's huge. That's a community model, that's just like open source. So now you've got the confluence of open source software, now with people in writing just software, and now microservices that interact with other microservices. Not agile within a company, agile within other developers. >> Angel: Right. >> So you have a data piece that ties that together, but you also have the process and potential business model disruption, a Blockchain. So those two things are interesting to me. But it's a community role. In your expert opinion on the community piece, how do you think the community will evolve to this new dynamic? Do you think it's going to take the same straight line growth of open source, do you think there's going to be a different twist to it? You mentioned this new persona is already developing with cognitive. How do you see that happening? >> Yes, I do. There's two, let's say three points. The first on circling the community, what we've been trying to do, architecturally, is build an open innovation platform. So all these elements that make up cloud, data, AI, are open so that people can innovate, skills can grow, anything, grow faster. So the communities are actually working together. So you see lots of intralocks and subcommittees and subgroups within teams, right? Just say this kind of nesting of technology. So I think that's one megatrend that will continue-- >> Integrated communities, basically. >> Integrated communities. They do their own thing. >> Yeah. >> But to your point earlier, they don't reinvent the wheel. If I'm in Cloud Foundry and I need a container model, why am I going to create my own? I'll just use the open compute initiative container model, you know what I'm saying? >> Dave: And the integration point is that collaboration-- >> Is that collaboration, right. And so we've started to see this a lot, and I think that's the next megatrend. The second is, we just look at developers. In all this conversation, we've been talking about the what? All the technology. But the most important thing, even more so than all of this stuff, is the how. How do I actually use the technology? What is the development methodology of how I add scale, build these applications? People call that DevOp, you know, that whole area. We at IBM announced about a year and a half ago, at Gene Kim's summit, he does DevOps, the garage method, and we open sourced it, which is a methodology of how you apply Agile and all the stuff we've learned in open source, to actually using this technology in a productive way at scale. Often times people talk about working in theses little squads and so forth, but once you hire, say you've got 10 people in San Francisco, and you're going to hire one in San Ramon, that person might as well be on Mars. Because if you're not on the team there, you're not in the decision process. Well, that's not reality. Open source is not that way, the world doesn't behave that way. So this is the methodology that we talked about. The how is really important. And then the third thing, is, if you can help developers, interlock communities, teach them about the how to do this effectively, then they want samples to fork and go. Technology journeys, physical code. So what you're start to see a lot of us in open source, and even IBM, is provide starters that show people how to use the technology, add the methodology, and then help them on their journey to get value. >> So at the base level, there's a whole new set of skills that are emerging. You mentioned the O'Reilly books before, it was sort of a sequential learning process, and it seems very nonlinear now, so what do you recommend for people, how do they go about capturing knowledge, where do they start? >> I think there's probably two or three places. The first one is directly in the open source communities. You go to any open source community and there's a plethora of information, but more so, if you hang out in the right places, you know, IRC channels or wherever, people are more than willing to help you. So you can get education for free if you participate and contribute and become a good member of a community. And, in fact, from a career perspective today, that's what developers want. They want that feeling of being part of something. They want the merit badge that you get for being a core committer, the pride that comes with that. And frankly, the marketability of yourself as a developer, so that's probably the first place. The second is, look, at IBM, we spend a huge amount of time trying to help developers be productive, especially in open source, as we started this conversation. So we have a place, developer.ibm.com. You go there and you can get links to all the relevant open source communities in this open innovation platform that I've talked about. You can see the methodologies that I spoke about that is open. And then you could also get these starter code journeys that I spoke about, to help you get started. So that's one place-- >> That's coming out in April, right? >> That's right. >> The journeys. >> Yeah, but you can go now and start looking at that, at developer.ibm.com, and not all of it is IBM content. This is not IBM propaganda here, right? It is-- >> John: Real world examples. >> Real world examples, it's real open source communities that either we've helped, we've shepherded along. And it is a great place, at least, to get your head around the space and then you can subset it, right? >> Yeah. So tell us about, at the last couple of minutes we have, what IBM's doing right now from a technology, and for developers, what are you guys doing to help developers today, give the message from what IBM's doing. What are you guys doing? What's your North Star? What's the vision and some of the things you're doing in the marketplace people can get involved in? You mentioned the garage as one. I'm sure there's others. >> Yeah, I mean look, we are m6anically focused on helping developers get value, get stuff done. That's what they want to do, that's what our clients want to do, and that's what turns us on. You build your art, you talk, you're going back to art, you build your drawing, you want to look at it. You want it to be beautiful. You want others to admire it, right? So if we could help you do that, you'll be better for it, and we will be better for it. >> As long as they don't eat their ear, then they're going to be fine. >> It's subjective, but give value of what they do. So how do they give value? They give value by open technologies and how we've built, essentially, cloud, data, AI, right? So art, arts technology adds value. We get value out of the methodology. We help them do this, it's around DevOps, tooling around it, and then these starters, these on-ramps, right, to getting started. >> I got to ask you my final question, a more personal one, and Dave and I talk about this all the time off camera, being an older guy, computer science guy, you're seeing stuff now that was once a major barrier, whether it's getting access to massive compute, machine learning, libraries, the composability of the building blocks that are out there, to create art, if you will, it's phenomenal. To me, it's just like the most amazing time to be be a computer scientist, or in tech, in general, building stuff. So I'm going to ask you, what are you jazzed up about? Looking back, in today's world, the young guns that are coming onto the scene not knowing that we walked barefoot in the snow to school, back in the old days. This is like, it's a pretty awesome environment right now. Give us personal color on your take on that, the change and the opportunity. >> Yeah, so first of all, when you mentioned older guys, you were referring to yourselves, right? Because this is my first year at IBM. I just graduated, there's nothing old here, guys. >> John: You could still go to, come on (laughs). >> What does that mean? Look you know, there's two things I'm going to say. Two sides of the equation. First of all, the fundamentals of computer science never go away. I still teach, undergrad seminars and so forth, and you have to know the fundamentals of computer science. That does not go away because you can write bad code. No matter what you're doing or how many abstractions you have, there are fundamental principles you need to understand. And that guides you in building better art, okay? Now putting that aside, there is less that you need to know all the time, to get your job done. And what excites me the most, so back when we worked on the Web in the early 90s, and the markup languages, right, and I see some in the audience there, Arno, hey, Arno, who helped author some of the original Web standards with me, and he was with the W3C. The use cases for math, for the Web, was to disseminate physics, that's why Tim did it, right? The use case for XML. I was co-chair of the mathematical markup language. That was a use case for XML. We had no idea that we would be using these same protocols, to power all the apps on your phone. I could not imagine that, okay? If I would have, trust me, I would have done something. We didn't know. So what excites me the most is not being able to imagine what people will be able to create. Because we are so much more advanced than we were there, in terms of levels of abstraction. That's what's, that's the exciting part. >> All right. Dr. Angel Diaz, great to have you on theCUBE. Great inspiration. Great time to be a developer. Great time to be building stuff. IOT, we didn't even get to IOT, I mean, the prospects of what's happening in industrialization, I mean, just pretty amazing. Augmented intelligence, artificial intelligence, machine learning, really the perfect storm for innovation. Obviously, all in the open. >> Angel: Yes. Awesome stuff. Thanks for coming on the theCUBE. Really appreciate it. >> Thank you guys, appreciate it. >> IBM, making it happen with developers. Always have been. Big open source proponents. And now they got the tools, they got the garages for building. I'm John Furrier, stay with us, there's some great interviews. Be right back with more after this short break. (tech music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering Interconnect 2017. Brought to you by IBM. >> Okay, welcome back everyone, we're live in Las Vegas for IBM Interconnect 2017, this is theCUBE's three-day coverage, we're in day two, wall-to-wall coverage with theCUBE, I'm John Furrier, with my co-host, Dave Vellante. Our next guest is Jason Kelley, Vice President, he's a partner at IBM's Global Business Solutions, GBS Solutions and Design, part of the group that brings it all together in the digital transformation for IBM. Welcome to theCUBE. >> Grand to be here, thanks for having me. >> So, we were just talking about South by Southwest, before we kicked on the cameras, and you guys had a huge presence there. But you're an interesting part of IBM, and I want you just to make a minute to explain what you do, because everyone talks about, "Oh UX design, you're going to develop the future," it's a lot more complicated than just saying UX design. >> That's true, very true. >> There's some work involved, so take us through what this design experience concept's about, and how does it work, and why everyone's so buzzed-up about it, 'cause it's gettin' a lot of traction. >> Great question to start with, and I always get to spin that then back to you. So as you said UX, first thing that came out, you said design and UX, so tell me, when you hear design, what do you think of? Do you think of cool ties, jackets, what do you think? >> I don't know, a nice cube setup with good user-- >> A couple good lookin' guys. >> Interface on the website. >> I was thinking devices. >> Dave's tie. >> I think of cool visuals, right? I think of movies, actually. >> Okay, okay. So, they are things that give you some type of experience. >> Dave: Yeah, they create a feeling inside, an emotion, it's a motive. >> All right, okay. So, now we're headed in that direction. So take that emotion piece, set that to the side, and think about what also came out, you said device, so it's something that you use. And often when you say design now, they think of the wonderful things like-- >> John: The iPhone. >> You got it, iPhone. They say, "Oh, what wonderful design." That design evokes emotion. And so, when we think of emotion, take that and put that into business, and think about creating an elegant solution for the outcomes of the end user in a business. So, you have a business that has a problem, they need to solve it, and you want to create a solution that evokes emotion. So that as they experience, like you can't set down that phone, we don't want them to set down their IBM solutions, that's the type of design that I'm talking about. >> Jason, this is interesting, Dave and I always talk about this in theCUBE when we get into this kind of like, get into The Cloud and look down at the world, the computer industry has always been centered on how many users do you have? I mean user, are you a drug user? What kind of user are you? It's the consumer, right? So, now you're really getting at the heart of design transcending computer, a user on a terminal. They're all consumers. So this is kind of the new normal. >> That's right, the new norm is, the consumer, meaning the focus. We'll go back to your phone, you think about this consumable capabilities and that consumption. You think back when we thought were cool and you would say, "This is my home office, "and I've got my fax machine here and I've got my-- >> John: A pager! >> "I've got my pager, I've got my telephone, "I've got all these things." >> My stereo. >> You had all those, and now... Here it is. And who did this? This is the consumer. And so, having consumable solutions that a consumer would be excited about, but taking that to the enterprise, at scale. At scale, did I send someone a great text there? >> No, I was just plugging in. (Jason and John laughing) >> So that you have to-- >> It's got a cognitive energy in it, so it's designed well. (all laughing) >> Honey, bring me more milk and bread. What we do from a consumability perspective is just that: how do you make sure that you have consumer grade solutions that the enterprise can enjoy? Right? So that is key, and this is what you pivot around. >> One of the things that we also were watching last week, we were at the Big Data event that we had in Silicon Valley, you can judge 'em as Strata Hadoop is, the collision course between the big data world which tends to be analytics: Watson's got cognitive, and then The Cloud, you've got brute force blocking and tackling, Cloud under the hood, hard IT problems, in-production workloads; and then you have the cool, sexy, sizzley web app, and mobile apps, creativity, kind of comin' together. So, on one hand you got creativity, you have energy, you have emotions, all this kind of outcome-based consumer thinking, and then you got the hard scaffolding, the iron under the hood, like workloads, hard stuff. So, how do you balance that when you get into the Design Center? It's not what people might think, "Oh, they got the crazy ideas, and I'm going to do this, "change the world," but at the end of the day you got to go implement it, so take me through that process. >> So you think about implementation, and we have, here over the last four years, established 26-plus IBM Design Studios globally. And our clients love to come to those studios because they get to talk about what you're asking me here, "Look we have all these things, these piece parts, "some things new, some things legacy. "How do I take this, and how do I tie it all together?" They usually come with these business challenges and say, "Look, I have a front office, and a back office, "and I'm tyin' to get all this," we go "Wait a second. "What you've just described is really one office, "and in that one office, "at the center of all those challenges are data, typically." And you're tryin' to figure out, "How can I make this data work?" And then, as soon as you solve that problem you say, "Wait a minute, then there's business process, "that's working between the front office, "and the back office, and this middle office." And then "Oh wait, there's also then some regulation "that I have to worry about." So now, you have this crashing of these different capabilities, you have this challenge of saying, "How do I make the business architecture, "work with the technical architecture, "work with my human architecture?" And that's where design comes in, that's where you begin to weave those things together by understanding how each one of those diverse pieces of the business work in harmony. >> So Jason, what are some of your favorite examples of an outcome that drove business value? >> I'll use a great example, and it was one with a client I was just havin' a wonderful dinner with last night, the Bank of the Philippine Islands. Banking has each one of these things that I've talked about: trying be more nimble on the front end, as well as having a very complicated, and often regulated back end. This wonderful, wonderful client of IBM said, "Listen, could you come in "and help me solve my data problem? "Because we have a big data challenge." I said, "Sure, well let's understand that, "let's get under the covers of this data problem," in a design workshop with them, walking them through their end users, their end users being all the way through their enterprise, our process realized, wait a minute, it's not our data problem that we have, it's a start-up problem. We're always going to have a data problem, but we can't run like a start-up, we can't move fast, we're not as agile as we think we are. We think we do DevOps, but our DevOps hit separate from agile, and by the way, this design-thinking thinking is great, how do you weave all of that together? What they found then in their start-up was now that we know what our problem is, you've wowed us, we're wowed. But then, how do we execute? We use this term, if I can wow you, you will definitely then how me, right? So how do we do this? And this is where the design came in where we said, "Look, now let's understand how you move like a start-up," which then did get under the covers with: well we need a Cloud capability; we need to have some tooling, like Bluemix, where we can go ahead and quickly assemble those things together; and we need to understand how we can apply some of our analytics, and maybe even cognitive, towards our clients. So, that's something that started one way, here's the problem, and it's data, that really ended up another way. And as they will tell you if you were to ask Bank of Philippine Islands, they'd say, "Listen, the design doesn't stop." And what they've learned from us is that design never stops, everything's a prototype in a sense, and design only stops when the problem is solved. And I can ask you, is the problem ever solved? >> No, it's a moving train every day. >> Jason: You're never done. >> The Design Center, really Studio is a great idea, I think it's phenomenal. The question I want to kind of probe into is how much of it is therapy for the customer to kind of, "Doctor, am I okay? "I think what's goin' on with me, can you look around me?" 'Cause they're lookin' from kind of that 360 blind spot, and how to be innovative. And so, you kind of rub their shoulders, "You been doin' okay, you're going to survive," and then you got to wow them. So before you wow them, you have to kind of whip 'em into shape and get their perspective, so how much of the percentage of time is herding the cats in a therapeutic way? Or is it not a factor to then, when you get that momentum going? Take us through the psychology of the buyer, your customer, because I can almost imagine the opportunities is somewhat intoxicating these days. So you go, "Hey, I got pressure to go Cloud native, "but I know it's going to be a disaster if I do." >> You're on a great point, and I like the thought of the therapy because look, it is somewhat of a Dr. Phil moment that they have. Where you sit back and what we find client after client is that sure, we could tell them, "Here are your pain points. "We're IBM, we deal with thousands of clients every week," but that doesn't cause change. I mean, you really have to change in the way that you're acting, so you can't really, we like to use this phrase-- >> Hit the playbook, run the offense. >> That's right. >> You got to have the culture. >> And you will have some people say that you have to have a culture, so you can't think your way into a new way of acting, you have to act your way into a new way of thinking. And so that's the process, is where you bring this discovery by way of using the basics of empathy, and this is design thinking, in the core of its essence. >> Empathy, great word. Business empathy is really the challenge because, I hate to use the example of will the parachute open? You know I always say to my kids, "Pack your own parachute, learn how to pack a parachute." Not that I tease that dangerous, but it can be, I mean, security breaches are one of those things where the blind trust that's out there, and some opportunities, to Jenny's point on stage today, trust economy. >> That's very true. >> This could be a dangerous world, so you don't want to just trust the parachute's going to open. >> No, no, I will tell ya in a prior life I used a parachute, I jumped Airborne Ranger, jumped out of planes, and I always joked saying, "Hey, no one is going to get shot out, "or have to jump out of an airplane today," so it'll be fine. Well, I can laugh and joke, but you're right because you sit there and to any of our clients, it's not a joke. That trust economy that we're in is reality, and it has to be underlayed with the confidence that we can bring that to-- >> Well Cloud, I have said The Cloud which underpins all this is going to move at the speed of trust, if you don't trust The Cloud, you're not going to use it. >> Jason: Very true. >> That example you gave, I want to go back to it, 'cause we talked about the emotion. So, the emotion comes from what, the consumer experience? You know the bank, that you gave that example. So, take us through sort of what that outcome was, I mean, it was the entire experience that was reimagined? Is that right? >> Well that's exactly, the experience was when the diverse team across the bank was in one room, and going through some of the exercises we take them through to use this empathy for the enterprise. Not just for the individual, or design for a product, this is design for an entire business. As they sit there and they look across that, what they got out of that was this thought that, "Wait a second, this is very complicated "for my part of the business. "Oh but wait, your part of the business "is having similar challenges, and oh, yours as well." And then you have the aha moment you're like, "Wait, we're all having similar challenges." And this becomes the emotion, the emotion goes, "Wait a second, you've just helped me see something "that was right in front of me, it was right there." Thank you, this is the Dr. Phil moment, because then you say, "Oh well, "then we're doing this together." And you go, "Yes, now let us walk you through, "walk you through walking us through "what we might do together collaboratively," and that's where you get this new step change of action. >> So, you're a business therapist, but also can implement. >> Right, because ultimately you have to make, and we have these steps where we look at how we walk through our cycle. If you think of an infinity sign, we go through: you must understand, reflect and make. And we have those as stages of this infinity sign, that you never stop going through those loops, as we call it, the loop of understanding, reflecting and making. >> Jason, I want to talk about the, you mentioned a Dr. Phil moment, this empathy, really a legitimate thing that goes on but-- >> Yeah, you're going to think I'm Dr. Phil, right? >> But also, a lot of customers I can imagine are grounded in disappointment. I mean, the way I felt when Duke lost in the March Madness, I'm like, and then like, "Oh my God, how could they be out?" I had them goin' all the way, it kind of screws up the brackets. So, like that's IT. IT's a lot like, you know, you make a bet, and sometimes it doesn't pan out, you got to be agile. So coming into the disappointment, clients come into the Design Center, probably with either an itch they're scratching, I want to innovate, and then problems that they're trying to solve, which might be some baggage, some sort of issue. Is there a pattern that you see when you have prospects come through, and clients come through the Design Center that are consistent? Like is there a trend, a trending chart, like top three, stack-ranked, issues fall into categorically, Cloud transformation, Watson analytics, is there a trend line? And by the way, did you have Duke to go all the way? >> I thought they would. In the trend that we see, there's some common things that come to mind where a client will say, "I want to move faster." And none of these are going to be surprises: I need to move faster, okay; I need to be agile; I would love to be more innovative; I would like to take my innovation and put it in action; how do I do all of there things? And you'll find if you work with them you go, "So why?" "Why?" We play the game of 5-Whys, and eventually you get to what the true, the true need is, and that true need is to get to get an outcome very quickly, they all have something right in front of them, and it's to be agile, innovative, and out in front of the market. All of those things require what you've already called-out with the technologies, and they are just technologies, the challenge is putting them in action. >> So with the Whys, you get to the outcome, that's the real pain point, and then you settle in to a variety of solution architectural choices. >> Yes, because that architecture battle, as we hear from Jenny, it's going to be the architecture battles on cognitive, on AI and data. And finding those three areas, that's where it has to be knit together. >> Enterprise strong, data first, and cognitive to the core. >> Well said. >> See, I was listening Jenny, I've listened to all your words in your speech, and I don't need Watson for that, but I'll forget tonight after I have a few cocktails. Jason, thank you so much for comin' on theCUBE, appreciate the insight. >> I appreciate the time. >> Be safe jumping out of the airplanes. >> All right, take care guys. >> Thanks so much. More live coverage here from theCUBE after the show, stay with us, some more interviews still on day two to come. Great content here, great guests, more after the short break.

live from Orlando Florida it's the kue covering sapphire now headline sponsored by ASAP Hana cloud the leader in platform-as-a-service with support from console Inc the cloud internet company now here's your host John furrier hey welcome back and we are here live in sapphire now in orlando florida this is the cube silicon angles flagship program we go out to the events and extract the signal noise want to thank our sponsors SI p HANA cloud platform and console inc at consoled cloud our next guest is an eel cebu vp of business development at fred balboni who is the GM of IBM here on the cube together SI p time you book them back of the cube good to see you guys like when is down so microsoft's up on stage ibm's here with SI p this is the old sav no real change of the game in terms of you guys have been multi-vendor very partnering very eco system driven but yet the game is changing very rapidly in this ecosystem of multi partnering with joint solutions i mean even apple your announcement earlier so is this kind of like a bunch of Barney deals as we used to say in the old days or what is the new relationship dynamic because data is the new currency it's the new oil it's the digital capital data is capital data is a digital asset partnerships are critical talk about this dynamic partnerships are critical and I think what we're doing is we are going deeper than we've ever gone with these partnerships with IBM we announced last month we announced the joint ASAP IBM partnership for digital transformation what does this do so what we've been doing traditionally with IBM we've had siloed partnerships with different IBM brands right we had a partnership with a power brand we had a partnership with the cloud team we are a partnership with GBS what we've done now with the digital transformation is bringing it all together so we have a CEO level discussion that's driven this partnership and I think that's really the differentiation so we have moved away from the so-called Barney deals because our customers expect bill talked about it in the keynote today he says when it's a multi partner situation customers expect that you're going to have one voice you're going to be a line you're going to provide value to those customers that's what we're trying to do and that's what this partnership is all right I want to get your thoughts on this I mean I'm Barnum's reference to the character you know I love you you love me kind of like a statement of mission but really not walking the talk so to speak but but I want to get your thoughts because you have a look at the analytics background at IBM when you built that business up there's a conflict in a way but it's also a great thing in the market apps are changing in very workload specific at the edge with its IOT or a mobile or whatever digital app they have to be unique they have to have data they got to be they have to be somewhat siloed but yet the trend is to break down the silos for the customer so how do you guys is it the data that does that because you guys doing a lot of work in this year you want to build great apps and be highly differentiated yet no silos how do you make that ok so it is its first of all it's very exciting and a confronting but also exciting for not only our companies but also for our customers it's all enabled really simply because of a couple of major technology shifts that have happened number one technology shift is the cloud the cloud without question is driving driving all of this in addition to your notion about data readily available data and the algorithms and software that can you know make cognitive sense of that is both driving of this whole change last but not least and I think Hana really enables this you know embodies this is the architectural change so you put those three things together availability of data cloud which means the capital investment required to build the infrastructure is inexpensive and then finally Hana which is the technology platform that rapidly allows you to take using you know a generic term api's and wire them to different sources allow you to dynamically reconfigure businesses now there's one last thing I think is really important here that we don't want to underplay and this is the social phenomena of the consumerization of IT and this has been going on for many many years but we've really seen it accelerate in the last 3 to 4 100 ala dated yeah absolutely and when you see a device like this becomes the system of engagement and oh by the way if you don't like if you don't like dark skies weather app well then go to the weather channel's weather app and if you don't like their weather I've go to one of 40 other weather apps so therefore this consumerization of IT is bombarding our CIOs what's exciting is that cloud cognitive insight a flexible core with great social engagement allows a CIO to really rapidly reconfigure so that's why these partnerships are rising that's very important you just said to about this relationship now about consumerization of IT is a complete game changer on the enterprise software business because now the relationship to the suppliers I'm the CXO or CIO I had a traditional siloed as you use that word earlier relationship with my my vendors one pane of glass like that IT Service Management down here I got the operations I up changed my appt every six months or six years the cadence of interaction was very inside the firewall absolutely so the relationship has changed with the suppliers expand on that because that really hits a whole nother thread I'm the buyer i don't want complexity you don't and what you do want is time to value so combining that with the beautiful user experience that you know thanks to devices like the one that Fred showed you know are an absolute necessity they it's it's understood now it's an expectation that customers have and customers of customers also have so i think that is impacted us in multiple ways what you heard and build scheme out you heard that with our supplier Network you heard our president for ASAP Arriba Alex talk about it he is that the change within that organization itself with our different vendors with the fact that we have to provide choice to our customers i think that is that has changed the way we do business and it's interesting to just I mean this is right now a moment in history as a flashpoint not that's a big of event but it's been seeing this trend happening over the hundreds of cube events that we've been to over the past few years is that now in just today highlights it the Giants of tech are here ASAP IBM or I mean Microsoft Office state's atty Nutella the apple announcement you guys have a similar deal with Apple these are the Giants okay working together now iBM has bluemix you have HANA cloud platform you have on a cloud everyone's got cloud so this kind of highlights that it's not a one cloud world absolutely and so this really kind of changes the game so I got to ask you given all that how do you guys talk to the ecosystem because they're our total transistors going on at capgemini Accenture pwc CSC it's an outside-in dynamic now how is that change for you guys as you guys go to market together in a variety of things in a coop efficient some faces how does that dynamic change with it for the partners that have to implement this stuff so co-op edition is is a reality i think we've asap we've learnt this probably from a partner that does the best which is IBM they probably they practically invented cooperation in the enterprise software space so i think here's how here's the way we look at it right so so we are looking at with with hana with HANA cloud platform we're really morphing into a platform and applications company and and we have the strategy of essentially later thousand apps blue so what are we doing on HANA cloud platform in such a short time so we have two about 2600 plus customers we have I think the more important part is that our ecosystem around HANA cloud platform is 400 + partners so that's an advantage visa V say Oracle for instance which is waves to have an ecosystem they lot of people there too I think I think the DNA of SI p isn't being an open company we've had that for ages so we work closely with Barton's and by the way I used to be at Oracle I was there for seven years and I know the difference its it's stuck Oracle's got a different strategy we've got a very very different very open strategy so I think what we're doing is we coalescing around these key assets right our digital Korres for Hana Hana cloud platform as the key platform for our customers okay so a nice watching out there and looking out over the next year so what execution successes do you put out there that's a to prove that you guys are are open and you guys are doing good deals what success kpi's key indicators would you say look for the following things to happen so number one available availability of AP is I think if you look at the different api's they access to the variety of SI p systems what you did see is that there's a digital core there's all of the different assets we've got in the cloud easy access to those I think customers can look for that right how can they rapidly develop an essay p successfactors extension or how can they extend ASAP arriba very quickly integrating that with the s100 digital core I think that's number one number two is the HCP App Center so we have probably about a thousand plus apps out there and by the way I do need to give a shout out here because we've got three apps that three iOS apps that IBM pour it onto HANA cloud platform in the last six weeks was it Fred six weeks we're talking about you know an incredibly short amount of time that are now highlighted on HANA cloud platform app center Fred talk about IBM right now because this isn't a game finished shift I've noticed more aggressively the three years ago I saw the wave coming at IBM and now remote past two years it's just been constant battering on the beachhead iBM has been donating a ton of IP with open sores everyone's behind blue bluemix has gone from you know a fork of cloud foundry to a now really fast they're moving very very quickly yes sir writing apps you're partnering is this part of the strategy just to kind of keep humbling the Markowitz assets like this is that's open the more open IBM and how is open mean to for you guys today well because I think at the end of the day we got to realize that I mean us to question a couple couple questions ago and I Neal answered it quite well which is customers are going to make the choice customers want to be flexible in their choice so understand I want to first of all shout outs IV to Apple excuse me to sav a shadow tennis AP here which is s ap has always been about partnering an ecosystem and so that's a court that's a core belief of theirs so when you look at what they've technically done here with the HANA cloud platform you know one of the many strategists can put this on a board enjoys well this is what this is what they should be doing but the reality of it is is the reason companies stay with existing service providers the reason companies say with existing technologies is because they've already got it it's what they know how to do and so and what they want to do is very hard so the Hana architecture in the hunting club platform was probably drawn on a board ten years ago the fact that it's real and here now now mace clients the ability to actually make these kind of ships IBM's move to the cloud moving assets to the cloud because we recognize clients are actually going to want to pick and choose and build these things in a dynamic fashion and we want our workloads to be on the IBM cloud every single show I go to down basically feels like a cloud in a data show even amplify which is kind of a commerce show sure it's all about data and the cloud so I we got to get we got to get wrapped up I want to get one final thread in with you guys and that is unpardonable Apple just spent the billion dollars with the uber clone and China so you see their partner strategy they did partner with you guys and now SI p this is a really interesting strategy for Apple to go into the enterprise they don't have to get over their skis and over-rotate on this market that can come in pre existing players and extend out versus trying to just have a strategy of rolling products out so it seems that Apple is partnering creating alliances as their way into the enterprise similar to what they're doing in in China with who were just a random example but which is impressed this week is that the Apple strategy I mean you guys both talk to Apple I mean you guys have both of deals share some color on Apple's partnering and alliances their joint venture not your invention for joint development seems to be very cool so I it's not I I I want you know when I look at what we're doing with that you know we have a goal and our goal is we believe that we can transform the enterprise you know we I BM we IBM and SI p we IBM and our partners including Apple we want to transform enterprise Apple signed on to that because Apple realized that they were changing consumers lives and and then they woke up and they said well actually but many people spend a large part of their waking day at work so if I can change a consumers life I can also change an enterprise employees life and that is the work that we are setting about doing and so therefore the partnership IBM understands enterprise really well SI p was Bill statistic today seventy-three percent of the world's transactions run through an essay peak or so yeah Apple's very obviously very delivered in picking their partners we're thrilled with the mobile first for iOS worked in Swiss great programming language has great legs is so elegant and sweet it's like see but more elegant absolutely I think again when you look at what Apple's mission has been and you look at sa peace mission right we talked about helping companies run better and transforming lives so i think i think the missions actually do intersect here and and I think SI p is a very different company than we were you know 20 years ago so for us now that user experience and product while agent by the way absence proc solid quality absolutely so I think I i think you know we converge on those areas so I would say that it's a it's a very natural farming from Apple's a brilliant strategy because it's interbred and it prizes hard you guys to live that every day it's not easy and we see venture-backed startups try to get into the enterprise and the barriers just go up every day with dev ops and you know integration now is mrs. Ann we could talk about another segment with a break but we haven't gone to the whole what does it mean to integrate that's a whole nother complex world that requires orchestration really really interesting and you just write that over the weekend and a hackathon absolutely and I think now with the tools that we're making available on our cloud platform as part of a platform as a service I think again that's the way where we can get the user interface the experience that apple provides combined with the enterprise solid stuff that we do that's awesome I'll give you guys both the final word on the segment and a bumper sticker what is this show about this year what is s AP sapphire 2016 about what's the the bumper sticker what's the theme I you know what I love builds words today I think it's about empathy it's about making it real for customers I think you'll see you know our demos are joined demos as well both in an essay p IBM Joint Center here as well as in the IBM boat you see real life solutions that are real that customers can touch that they can use so I'd like to go with that predicate real hey listen to me it's a really simple to two simple words digital reinvention every single company in the world is trying to become a digital company I think about my Hilton app when I checked into my hotel yesterday and I opened my door with my iPhone my hotel my room door you know it is every company is endeavoring to become a digital company and what what sapphire is about this year is everyone realizes at the core of every company is that platform that s AP gahanna or ECC platform and every major enterprise that's waking up to that suddenly realizes we've got to do something an essay p nibm our partner here to help thanks guys so much for sharing your insight digital reinvention going on for real here at sapphire this is the cube you're watching the cube live at sapphire now we'll be right back thank you

okay welcome back live in Las Vegas is the cube ibm's information on demand conferences q exclusive coverage SiliconANGLE will keep on here live I'm John furry the founder of silicon Hank I'm Joe mykos Dave vellante co-founder Wikibon org our next guest is a Fred Balboni global leader business analytics optimization IBM GBS global business services you know obviously big data is powering the world I mean just can demand for information and solutions is off the charts afraid welcome to the cube anything there's a services angle here where you know services matters because one in the channel partner is this good gross profit for helping customers implement solutions that they have demand for so you've a combination of a market that's exploding with demand people know it's a game changer with big data analytics cloud is obviously right there in the horizon in terms of on prem of Prem then you've got now see mobile devices bring your own device to work which is thrown off more data okay and then people want to be in all the different channels the social business so you know CIO to CEO says hey this new wave is here if we don't think about it now and get a position and understand it the consequences of not doing anything might be higher than they are so we've heard that how do you look at that and what are you guys doing what's the strategy give us a quick update and from from GBS i think that the to make this successful first of all it services is important it's the last mile you know that means the point you may it's the last mile and without without that you cannot ever deliver the value the the really interesting challenge that every executive faces is you need to be able to we can easily get our head around big data technology and I shouldn't trivialize that but you can go and understand the technology what's possible in big data you can also get your head around analytics and the analytics algorithms and the kind of insights that can be drawn from that the real challenge is how do you articulate what's kind of possible to a client because many of the use cases are very niche and so clients often say yet that's right but it's big it's possibly bigger than that yeah that's right it's possibly bigger than that the other issue or the other challenge to get we've got a hurdle we've got a jump on me articulate this to the businesses clients businesses think in terms of process you don't think in terms of data you know you don't go talk to a CIO CEO and say you know tell us what's the key attributes of your customer and they don't think that way they can talk to you about servicing a customer or selling to a customer or managing customer complaints so that the processes but the data it's a tough thing so the first part the services is so crucial in this is being able to articulate the value of analytics and big data to a client in the businesses terms so it becomes a boardroom conversation kind of so that's that gets the program started and then quickly being able to fill in with use cases because clients don't want this to be they don't want to start from a blank sheet of paper and they don't like going to give me some quick wins here so it's kind of those timetable what kind of timetables mmmm back in the 80s 90s when client-server rolled out it was months and months yeah project management meetings roll out the Oracle systems roll out the big iron now I mean I'll see maybe shorter spurts little different hurdles what's the timetable only some of these horizons for these quick wins okay so project implementation I come on now let's let's know it's it's I think that that we're measuring project implementations in weeks I think cloud-based technology allows us to provision environments on the order of a couple of weeks and that used to be on the order of five to six months so I think that's going to that accelerates everything and that also allows you to do a lot of a lot more speed to value get applications or analytics use cases up there much more rapidly one two as you start to build these portfolio of use cases and if they're built on acceleration tools I mean acceleration so you've got those code sets that are already there that you can add you can jump on top of I mean you can get these use cases up there in 6-8 weeks we have one we have an example a really large major company i'd rather not i'd rather not because it's not externally referenceable but a really a significant client that had on the order of more than more than 5 million discreet customers and doing detailed customer analytics on their customer base against their products and we were able to get that baby up and running in three and a half months now that two to three years ago traditional logic would have told you that was a nine to twelve month project and by the way you know ten years ago that would have been a 18 to 24 month project yeah so I think that yeah we're moving much more rats the expectation now too I mean the customers realize that too right the absolute not but but there's one thing I want to talk about this it's still this is the one thing that if you'd asked me what's most important this speed thing allows you to go rapidly to places but you you better have a navigation roadmap on where you're going because if you're going to do all kinds of little code drops that's great but you want to make sure you're getting leverage so you're going somewhere so therefore there's a scale but this is where roadmapping becomes really really important for every the technology side of the business you have to have a technology roadmap the other thing that's really important out of this is if you don't let's use the client-server example you used because this kind of has a you know we've all been here right here we've all lived seen this movie before yeah if you if you don't in the build this roadmap another thing that happens do you remember when CIOs finally said okay I'm taking control this client servicing sure what do they end up with they ended up with all these departments of computing in the costs work going astronomical so if you've got a road map you can also address the issues of managed services because you don't the least thing you want to be is having all these data Mart's that are scattered everywhere because you get no economies you get no economies of it but a cloud would bring you you get Noah kind you get no economies and being able to do that and you end up having to have all these maintenance teams you know that maintenance and by the way analytics by its nature has constant maintenance little adjustments and changes you're getting new economies of that because they're all managed is discrete units so therefore there's a lot to be as you build this roadmap you've got to think about the managed services environment as well so Fred you talked about earlier clients don't think in terms of data they think in terms of their business process is that a blind spot for clients because there are some companies Google for example that does think in terms of data in your view should clients increasingly be thinking in data terms or does our industry have to evolve to make the data map to business process I actually I kind of just take it as a thick I don't I don't I don't choose to question why I just accept it um i but i would say i which i would say customer's always right I just I just think the industry i thought that definitely but i think just the industries at a stage where you know we've always you know back in the old days of you know i'm going to show my age here but you know the procedure division in the data division oh my god looked at all and and and we you know the procedure division is where you actually did all the really and i think if the reason is we got understand the paradigm under which modern computing was created I don't to be like we go into history lesson but the paradigm under which modern computing was created was that we use computers to automate tasks so we've always taken this procedural approach which went then we went to process reengineering and that became a boardroom conversation so just I think we've conditioned over the last 40 years businesses to think about using technology to gain business efficiency they've always thought in terms of process so that's why this data element yeah companies like Google founded on analytics clearly have got a whole different headset in a different way to approach these which gives them a built-in bias when they address the problems they've got in their businesses sure but you don't come a decline saying hey you got to rethink the way in which you look at data you come in and say let's figure out how we can exploit data in your biz erect what we do it two ways we do it two ways first of all let me not dress let me not dress monton up as lamb at the end of the day it's its data its data okay now the question is how you articulate that and it's twofold we tend to I like to use a metaphor to describe the data so if its customer that the metaphor we've been using recently is DNA DNA strands to be able so you use a metaphor that there's a language that the business can relate to and you can create a common language very easy one in that way you can have an account because you're never going to drag a CEO into your fourth normal form data model so so therefore you've got to you've got to talk a language one number two you talk about as a collection of use cases so you use use cases as a vehicle to have the process conversation and because with the use case you also can talk business outcomes benefits and you can tell kind of a story you don't have to drag them through the details of the process but you can tell them a story whether it's you know I if you can understand called detailed called detailed data records and the affinities you can understand the social networks and therefore you can reduce churn within your telco customer base as an example quick but if you follow I do so you talked about its little use cases and they begin to understand wow what's possible and then you talk about their data as a DNA chain and they get I got it I actually need to get the DNA chain if I'm going to actually think about think about my customer base or my product base or whatever the lingua franca the business is still the businesses language it doesn't result of data but data can enrich the conversation in a way that can lead to new outcomes the data in rich's the conversation when you talk about the business outcomes that are created as the part of the use case well it's like a three third order differential equation but i go back i watch this yeah i just go say your tweet your epic soundbite machine just can't type fast enough on the crowd chat it's good for good for Twitter viewing yeah I've just opened a Twitter account please look me up I'm looking for friends I promise to start posting you got people watching all right all right so so in terms of customers right give us a little bit peak of some of the customer responses when you when you open the kimono show them the road map you know the messaging around on IBM right now is pretty tight here at IOD last year was good this year is better you look really unified face to the customer when you show them the road map what's the feeling they get it they feel like okay I got some trust IBM's got some track record history do they is the is the emotion more of okay where do I jump in how do I jump in there doing it and this little shadow IT going on all over the place we know with Amazon out the area so so when you're in there you've got to have these are conversations what do they like and what's that what's the level of response you get from CIOs and then also the folks in the trenches so there's always a question which there's a couple of questions first of all is how can I get how can I get value from this and that in that and that's you know a I'm tightly coupled to my existing transaction processing which is kind of like if you will call that turbocharged bi and and which is which is where so many people have come from is this turbocharged bi environment and listen that's an important part of your reporting business you need to do that to keep the wheels on the question is as you move to this notion of analytics giving you great insight then then you've got to say okay I need to go from turbocharged bi to really augmented components so clients I'd say there's a large there's a large group of people that are right now moving from turbocharged bi to the notion advanced use cases so there's this some disco a large discussion right now how do I show me do use cases by which i can I can rapidly that would be advanced how to linux up the calling advance limit well no we have well 60 60 use cases industry-based use cases that we as a services business put together on top of that we have about seven or eight key code fragments that we uses accelerators I mean we call them wink we call them assets and we just them up as accelerators but their code fragments that we bring to a client as the basis that we put on top of the the blue stack of technology to actually get them a speed to value because we really want to be able to get clients up and running within this notion of non idealities it's like literally being best practices in the form of technology to the customers well you're on an IBM thing I mean dare I called an application no I wouldn't dare call it an application we're not in that business but the point is is that it is it's starting to feel like an application because it's really moving down these unreal integrated solution is really where we going it's an accelerant this code correct so it's leverage the economies of scale is every success breeds that's exactly it more and then on top of that we would have that just don't throw a few other things that we do to accelerate these things we actually have five what we call signature solutions which is services software together with a piece of services code coming together to solve a problem we've got that round risk and fraud around customers I mean some specific very narrow things if somebody wants to you know because often IT departments they want to buy something they want to buy something they don't want to go down the parts they want to buy something and so fine here's a package solution let's go buy something um and then last but not least one thing we haven't talked much about but I always like to throw this out there because I think this is one of the things they and we didn't talk about it much in the main 10 or any better sessions but let's not forget about IBM research I'm really proud to report to you now since we started this category we've done 61st of a kinds with IBM Research so this is about client says I've got this problem i think it's unachievable i cannot solve this problem you know help me map in my oil exploration like things that are considered big problems big problems let's let's apply this group that does patent factory you know that IBM is but 15 years in a row let's apply those people to my our problems and we have 60 we have 16 so we do about 15 to 20 a year so it's not like we like we're not cranking these out like I'm hundreds of thousands of licenses but it's where basically our services business our software business and IBM Research go work on solving a client specific problem you heard Tim Buckman this morning when he was asked to know why IBM that was said IBM Research was the first answer that's right he gave we talked to him about that on the cube you know in his is insane me as a customer and we you know we always love to hear from customers I mean you know the splunk conference just had was just last week as an emerging startup because probably well aware of those guys they have customers that just say just glowing reports you get to the same same set of customers you know he is someone of high-caliber at the command and control in his healthcare mission and he's automating himself he it's and essentially creating this new data model that allows it to be pushed down to be listen you've got to do this and I'll tell you why you remember the the governance discussion is it was well I'm most excited about is the governance discussion five to eight years ago was an arcane discussion available of data modelers and like what do we do the governance discussion is quickly moving into the language of our business people and the reason is because they're beginning to do you remember the days of accounting systems when they say we want our accounting department to focus on analyzing the numbers and not collecting and forming the numbers well we're here again and if you've got good data governance you can focus on creating the insights and determining what actions you want from the insights as opposed to questioning the numbers and questioning the validity and the heritage of the number the validity and the heritage of the numbers and in this place everywhere yep financial services companies are the most stressed about it because the validity and heritage is required when you want to prove a compliance to a federal statute yes but it means everywhere if you're a consumer packaged goods company and you don't believe that sales are down in a certain market or a certain chain store first thing they do is they start challenging the numbers if you have good governance you can now start that you can now start to trust these systems of record but let's talk about data quality data quality but it's also the governess in the death of mindset is much broader iteration right how we said the first you know that folks from the nonprofit said you want to go on the record but he's basically saying I'll say basically when you put stuff out when you package and then bring it out it still might have some flaws in the data quality but it's the iteration is transformational but once that's in market saying that's changing he things prepare pre-packaging data and then bringing it in is not the better approach but I want to ask you about the your what you just said about this governance conversation that is date the core of this debate around the data economy what is the data economy in your mind given what you do the history that you've lived through we've seen those movies now the cutting edge new wave that will create new well for new ways change from transform business all that stuff's great but what is the data conn what does that mean to business executives that they're focusing on outcomes is is it changing data governance is it changing the value chains is it changing what's your thoughts on that the data economy is about discovering those points of leverage that that the data tells you that your instincts don't the data tells you that your instincts don't one of my favorite stories three years ago four years ago we were called in and clients said this is my problem the going and problem was I got to take 200 million dollars out of my advertising spend budget two hundred million dollars out of my advertising spend was he's a retailer end and the problem is is out of my 600 million dollar advertising budget the problem I have is also have all kinds of interesting theories and models that my agencies have told me I'm not quite sure do I just take 200 off the board across the board do I take 200 off to minimize my risk just spread it around how do i how do I manage the process and what we actually did was we built a super super set of sophisticated analytics which tied to their transaction systems but also tied to their social media system so we also understood and what we did was we were able to understand which customer cohorts responded to which media types then we added one more parts of the model which is we understood the trending in the cost of free-to-air cable radio internet all the different media types and as we looked at the cost models of them and we understood which customer cohorts responded to which media types we suddenly realized that they were super saturated in certain media types they could like doubled their spin and they wouldn't got want any lift in the advertised in their in their sales what we did was we got 200 million out of their budget and increase they got 300 million incremental sales that Christmas season because we help them get really smart about the play let me tell you I tell us privately i maked media buyers look at me like like I'm like a pariah yeah but but it is actually really you know really started to rethink now there's just a really great example because I think we've all can relate to that but that's the data economy where you find these veins of gold in these simple correlations and from that simple correlation you can instantly go and your business you can get the lift listen I can get five percent I IBM get five percent ten percent lift in some small segment business I've got the volume that's going to make a significant difference to my share one small piece of data could open up a window kind of had with Jodie Foster we would contact words like one piece of data opens up a ton of new data I mean that totally is leverage and it changes the game for that customer and and that to me is that is the guts of the data economy identifying those correlations and and what we're finding is our most recent study we just released it here the thing the IB the IBM Institute for business value big data and analytics study w IBM com it's the Institute for bit I bv study on big data just released and said 75 percent of all companies that are outperforming their peers have said big data analytics is one of the key reasons and the human component not to put are all on machines it's really about it's an ardent science its a mix of both the math and the human piece well you know there's this notion of not only do you create the insight but you've got to take action on the insight you know it's not enough to know if I could predict for you who's going to win tonight's basketball game you still got to place the bet you still have to take action on the inside and so therefore this notion of action to insight is all about trust trust in the insight trust in the data and trust in the technology that the business trust the technology and it's until you take that leap of faith remember when the Indiana Jones movie when he liked the leap of faith and you've got to like to step out and take that leap of faith once you take that leap of faith in you suddenly have trust in the data so that's that trust to mention and that's a human thing that's not a that's that's not a that's an organizational thing that is not a lot of technology in that one okay Fred we gotta wrap up i'll give you the final word for the folks out there quickly put a bumper sticker on iod this year's and put on my car when I Drive home what's that bumper sticker say for this year it's not all about the technology but it starts with the technology ok we're here live in Las Vegas we're going to take about that bet that was going to win the games and I will be the sports book later this is the cube live in Las Vegas for information on demand hashtag IBM iod this tequila right back with our next guest if the short break exclusive coverage from information on demand ibm's premier conference we write back the q