>> Narrator: From around the globe It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back everyone to theCUBE's coverage of IBM Think 2021 virtual, I'm John Furrier, your host. I've got a great guest here Robin Hernandez, vice president Hybrid Cloud Management and Watson AIOps. Robin, great to see you. Thanks for coming on theCUBE. >> Thanks so much for having me, John. >> You know, Hybrid Cloud, the CEO of IBM Arvind loves Cloud. We know that we've talked to him all the time about it. And Cloud is now part of the entire DNA of the company. Hybrid Cloud is validated multi clouds around the corner. This is the underlying pinnings of the new operating system of business. And with that, that's massive change that we've seen IT move to large scale. You're seeing transformation, driving innovation, driving scale, and AI is the center of it. So AIOps is a huge topic. I want to jump right into it. Can you just tell me about your day to day IT operations teams what you guys are doing? How are you guys organized? How you guys bring in value to the customers? What are your teams responsible for? >> Yeah, so for a few years we've been working with our IT customers, our enterprise customers in this transformation that they're going through. As they move more workloads to cloud, and they still have some of their workloads on premise, or they have a strategy of using multiple public clouds, each of those cloud vendors have different tools. And so they're forced with, how do I keep up with the changing rate and pace of this technology? How do I build skills on a particular public cloud vendor when, you know, maybe six months from now we'll have another cloud vendor that will be introduced or another technology that will be introduced. And it's almost impossible for an it team to keep up with the rate and pace of the change. So we've really been working with IT operations in transforming their processes and their skills within their teams and that looking at what tools do they use to move to this cloud operations model. And then as part of that, how do they leverage the benefits of AI and make that practical and purposeful in this new mode of cloud operations >> And the trend that's been booming is this idea of a site reliability engineer. It's really an IT operations role. It's become kind of a new mix between engineering and IT and development. I mean, classic DevOps, we've seen, you know dev and ops, right? You got to operate the developers and the software modern apps are coming in that's infrastructure as course has been around for a while. But now as the materialization of things like Kubernetes and microservices, people are programming the infrastructure. And so the scale is there, and that's been around for a while. Now it's going to go to a whole enterprise level with containers and other things. How is the site reliability engineering persona if you will, or ITOps changed specifically because that's where the action is. And that's where you hear things like observability and I need more data, break down the silos. What's this all about? What's your view? >> Yeah, so site reliability engineering or SRE practices as we call it has really not changed the processes per se that IT has to do, but it's more accelerated at an enormous rate and pace. Those processes and the tools as you mentioned, the cloud native tools like Kubernetes have accelerated how those processes are executed. Everything from releasing new code and how they work with development to actually code the infrastructure and the policies in that development process to maintaining and observing over the life cycle of an application, the performance, the availability, the response time, and the customer experience. All of those processes that used to happen in silos with separate teams and sort of a waterfall approach, with SRE practices now, they're happening instantaneously. They're being scaled out. They're being... Failback is happening much more quickly so that applications didn't do not have outages. And the rate and pace of this has just accelerated so quickly. This is the transformation of what we call cloud operations. And we believe that as IT teams work more closely with developers and they moved towards this SRE model, that they cannot just do this with their personnel and changing skills and changing tools. They have to do this with modernized tools like AI. And this is where we are recommending applying AI to those processes so that you can then get automation out of the back end that you would not think about in a traditional IT operations, or even in an SRE practice. You have to leverage capabilities and new technologies like AI to even accelerate further. >> Let's unpack the AI operations piece because I think that's where I think I'm in hearing. I'd love you to clarify this because it becomes I think the key important point but also kind of confusing to some folks because IT operations people see that changing. You just pointed out why, honestly, the tools and the culture is changing, but AI becomes a scale point because of the automation piece you mentioned. How does that thread together? How does AIOps specifically change the customer's approach in terms of how they work with their teams and how that automation is being applied? 'Cause I think that's the key thread, right? 'Cause everyone kind of gets the cultural shifts and the tools, if they're not living it and putting it in place, but now they want to scale it. That's where automation comes in. Is that right? Is that the right way to think about it? What's your view on this? This is important. >> It's absolutely right. And I always like to talk about AI in other industries before we apply it to IT to help IT understand. Because a lot of times, IT looks at AI as a buzzword and they say, "Oh, you know, yes, sure. "This is going to help me." But if you think about... We've been doing AI for a long time at many different companies not just at IBM, but if you think about the other industries where we've applied it, healthcare in particular is so tangible for most people, right? It didn't replace a doctor but it helps a doctor see the things that would take them weeks and months of studying and analyzing different patients to say, "Hey, John, I think this may be a symptom "that we overlooked or didn't think about "or a diagnosis that we didn't think about," without manually looking at all this research. AI can accelerate that so rapidly for a doctor, the same notion for IT. If we apply AI properly to IT, we can accelerate things like remediating incidents or finding a performance problem that may take your eye months or weeks or even hours to find, AI applied properly find those issues and diagnose just like they could in healthcare it diagnoses issues correctly much more rapidly. >> Now again, I want to get your thoughts on something while you're here 'cause you've been in the business for many, many decades 20 years experience, you know, cloud cold, you know the new modern area you're managing it now. Clients are having a scenario where they, "Okay, I'm changing over the culture." I'm "Okay, I got some cloud, I got some public "and I got some hybrid and man, "we did some agile things. "We're provisioned, it's all done. "It's out there." And all of a sudden someone adds something new and it crashes (chuckles) And now I've got to get in, "Where's the risks? where's the security holes?" They're seeing this kind of day two operations as some people call, another buzz word but it's becoming more of, "Okay, we got it up and running "but we still now going to still push some code "and things are starting to break. "and that's net new thing." So it's kind of like they're out of their comfort zone. This is where I kind of see the AIOps evolving quickly because there's kind of a DevSecOps piece. There's also data involved, observability. How do you talk to that scenario? Where, okay, you sold me on cloud, I've been doing it. I did some projects. We're not been running. We got a production system and we added something new. Something maybe trivial and it breaks stuff? >> Yes. Yeah, so with the new cloud operations and SRE, the IT teams are much more responsible for business outcomes. And not just as you say, the application being deployed and the application being available, but the life cycle of that application and the results that it's bringing to the end users and the business. And what this means is that it needs to partner much more closely with development. And it is hard for them to keep up with the tools that are being used and the new code and the architectures of microservices that developers are using. So we like to apply AI on what we call the change risk management process. And so everyone's familiar with change management that means a new piece of code is being released. You have to maintain where that code is being released to was part of the application architecture and make sure that it's scaled out and rolled out properly within your enterprise policies. When we apply AI, we then apply what we call a risk factor to that change because we know so often, application outages occur not something new within the environment. So by applying AI, we can then give you a risk rating that says, "There's an 80% probability "that this change that you're about to roll out, "a code change is going to cause a problem "in this application." So it allows you to then go back and work with the development team and say, "Hey, how do we reduce this risk?" Or decide to take that calculated risk and put into the visibility of where those risks may occur. So this is a great example, change risk management of how applying AI can make you more intelligent in your decisions much more tied to the business and tied to the application release team. >> That's awesome. Well, I got you here on this point of change management. The term "Shift Left" has come up a lot in the industry. I'd love to get your quick definition of what that is in your mind. What does Shift Left mean for Ops teams with AIOps? >> Yeah, so in the early days of IT there was a hard line definitely between your development and IT team. It was kind of we always said throwing it over the fence, right? The developers would throw the code over the fence and say, good luck IT, you know, figure out how to deploy it where it needs to be deployed and cross your fingers that nothing bad happens. Well, Shift Left is really about a breaking down that fence. And if you think of your developers on your left-hand side you'd being the IT team, it's really shifting more towards that development team and getting involved in that code release process, getting involved in their CI/CD pipeline to make sure that all of your enterprise policies and what that code needs to run effectively in your enterprise application and architecture, those pieces are coded ahead of time with the developer. So it's really about partnering between it and development, shifting left to have a more collaboration versus throwing things over the fence and playing the blame game, which is what happens a lot in the early days IT. >> Yeah, and you get a smarter team out of it, great point. That's great insight. Thanks for sharing that. I think it's super relevant. That's the hot trend right now making dealers more productive, building security from the beginning. While they're doing it code it right in, make it a security proof if you will. I got to ask you one of the organizational questions as IBM leader. What are some of the roadblocks that you see in organizations that when they embrace AIOps, are trying to embrace AI ops are trying to scale it and how they can overcome those blockers. What are some of the things you're seeing that you could share with other folks that are maybe watching and trying to solve this problem? >> Yeah, so you know, AI in any industry or discipline is only as good as the data you feed it. AI is about learning from past trends and creating a normal baseline for what is normal in your environment. What is most optimal in your environment this being your enterprise application running in steady state. And so if you think back to the healthcare example, if we only have five or six pieces of patient data that we feed the AI, then the AI recommendation to the doctor is going to be pretty limited. We need a broad set of use cases across a wide demographic of people in the healthcare example, it's the same with IT, applying AI to IT. You need a broad set of data. So one of the roadblocks that we hear from many customers is, well I using an analytics tool already and I'm not really getting a lot of good recommendations or automation out of that analytics tool. And we often find it's because they're pulling data from one source, likely they're pulling data from performance metrics, performance of what's happening with the infrastructure, CPU utilization or memory utilization, storage utilization. And those are all good metrics, but without the context of everything else in your environment, without pulling in data from what's happening in your logs, pulling in data from unstructured data, from things like collaboration tools, what are your team saying? What are the customers saying about the experience with your application? You have to pull in many different data sets across IT and the business in order to make that AI recommendation the most useful. And so we recommend a more holistic true AI platform versus a very segregated data approach to applying and eating the analytics or AI engine. >> That's awesome, it's like a masterclass right there. Robin, great stuff. Great insight. We'll quickly wrap. I would love to you to take a quick minute to explain and share what are some of the use cases to get started and really get into AIOps system successes for people that want to explore more, dig in, and get into this fast, what are some use case, what's some low hanging fruit? What would you share? >> Yeah, we know that IT teams like to see results and they hate black boxes. They like to see into everything that's happening and understand deeply. And so this is one of our major focus areas as we do. We say, we're making AI purposeful for IT teams but some of the low hanging fruits, we have visions. And lots of our enterprise customers have visions of applying AI to everything from a customer experience of the application, costs management of the application and infrastructure in many different aspects. But some of the low hanging fruit is really expanding the availability and the service level agreements of your applications. So many people will say, you know I have a 93% uptime availability or an agreement with my business that this application will be up 93% of the time. Applying AI, we can increase those numbers to 99.9% of the time because it learns from past problems and it creates that baseline of what's normal in your environment. And then we'll tell you before an application outage occurs. So avoiding application outages, and then improving performance, recommendations and scalability. What's the number of users coming in versus your normal scale rate and automating that scalability. So, performance improvements and scalability is another low-hanging fruit area where many IT teams are starting. >> Yeah, I mean, why wouldn't you want to have the AIOps? They're totally cool, very relevant. You know, you're seeing hybrid cloud, standardized all across business. You've got to have that data and you got to have that incident management work there. Robin, great insight. Thank you for sharing. Robin Hernandez, vice president of Hybrid Cloud Management in Watson AIOps. Thanks for coming on theCUBE. >> Thank you so much for having me John. >> Okay, this theCUBE's coverage of IBM Think 2021. I'm John Furrier your host. Thanks for watching. (bright upbeat music)

(bright upbeat music) >> Narrator: From around the globe. It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back everyone to theCUBE's coverage of IBM Think 2021 virtual, I'm John Furrier, your host. I've got a great guest here Robin Hernandez, vice president Hybrid Cloud Management and Watson AIOps. Robin, great to see you. Thanks for coming on theCUBE. >> Thanks so much for having me, John. >> You know, Hybrid Cloud, the CEO of IBM Arvind loves Cloud. We know that we've talked to him all the time about it. And Cloud is now part of the entire DNA of the company. Hybrid Cloud is validated multi clouds around the corner. This is the underlying pinnings of the new operating system of business. And with that, that's massive change that we've seen IT move to large scale. You're seeing transformation, driving innovation, driving scale, and AI is the center of it. So AIOps is a huge topic. I want to jump right into it. Can you just tell me about your day to day IT operations teams what you guys are doing? How are you guys organized? How you guys bring in value to the customers? What are your teams responsible for? >> Yeah, so for a few years we've been working with our IT customers, our enterprise customers in this transformation that they're going through. As they move more workloads to cloud, and they still have some of their workloads on premise, or they have a strategy of using multiple public clouds, each of those cloud vendors have different tools. And so they're forced with, how do I keep up with the changing rate and pace of this technology? How do I build skills on a particular public cloud vendor when, you know, maybe six months from now we'll have another cloud vendor that will be introduced or another technology that will be introduced. And it's almost impossible for an it team to keep up with the rate and pace of the change. So we've really been working with IT operations in transforming their processes and their skills within their teams and that looking at what tools do they use to move to this cloud operations model. And then as part of that, how do they leverage the benefits of AI and make that practical and purposeful in this new mode of cloud operations >> And the trend that's been booming is this idea of a site reliability engineer. It's really an IT operations role. It's become kind of a new mix between engineering and IT and development. I mean, classic DevOps, we've seen, you know dev and ops, right? You got to operate the developers and the software modern apps are coming in that's infrastructure as course has been around for a while. But now as the materialization of things like Kubernetes and microservices, people are programming the infrastructure. And so the scale is there, and that's been around for a while. Now it's going to go to a whole enterprise level with containers and other things. How is the site reliability engineering persona if you will, or ITOps changed specifically because that's where the action is. And that's where you hear things like observability and I need more data, break down the silos. What's this all about? What's your view? >> Yeah, so site reliability engineering or SRE practices as we call it has really not changed the processes to say that it has to do, but it's more accelerated at an enormous rate and pace. Those processes and the tools as you mentioned, the cloud native tools like Kubernetes have accelerated how those processes are executed. Everything from releasing new code and how they work with development to actually code the infrastructure and the policies in that development process to maintaining and observing over the life cycle of an application, the performance, the availability, the response time, and the customer experience. All of those processes that used to happen in silos with separate teams and sort of a waterfall approach, with SRA practices now, they're happening instantaneously. They're being scaled out. They're being... Failback is happening much more quickly so that applications didn't do not have outages. And the rate and pace of this has just accelerated so quickly. This is the transformation of what we call cloud operations. And we believe that as IT teams work more closely with developers and they moved towards this SRE model, that they cannot just do this with their personnel and changing skills and changing tools. They have to do this with modernized tools like AI. And this is where we are recommending applying AI to those processes so that you can then get automation out of the back end that you would not think about in a traditional IT operations, or even in an SRE practice. You have to leverage capabilities and new technologies like AI to even accelerate further. >> Let's unpack the AI operations piece because I think that's where I think I'm in hearing. I'd love you to clarify this because it becomes I think the key important point but also kind of confusing to some folks because IT operations people see that changing. You just pointed out why, honestly, the tools and the culture is changing, but AI becomes a scale point because of the automation piece you mentioned. How does that thread together? How does AIOps specifically change the customer's approach in terms of how they work with their teams and how that automation is being applied? 'Cause I think that's the key thread, right? 'Cause everyone kind of gets the cultural shifts and the tools, if they're not living it and putting it in place, but now they want to scale it. That's where automation comes in. Is that right? Is that the right way to think about it? What's your view on this? This is important. >> It's absolutely right. And I always like to talk about AI in other industries before we apply it to IT to help IT understand. Because a lot of times, IT looks at AI as a buzzword and they say, "Oh, you know, yes, sure. "This is going to help me." But if you think about... We've been doing AI for a long time at many different companies not just at IBM, but if you think about the other industries where we've applied it, healthcare in particular is so tangible for most people, right? It didn't replace a doctor but it helps a doctor see the things that would take them weeks and months of studying and analyzing different patients to say, "Hey, John, I think this may be a symptom "that we overlooked or didn't think about "or a diagnosis that we didn't think about," without manually looking at all this research. AI can accelerate that so rapidly for a doctor, the same notion for IT. If we apply AI properly to IT, we can accelerate things like remediating incidents or finding a performance problem that may take your eye months or weeks or even hours to find, AI applied properly find those issues and diagnose just like they could in healthcare it diagnoses issues correctly much more rapidly. >> Now again, I want to get your thoughts on something while you're here 'cause you've been in the business for many, many decades 20 years experience, you know, cloud cold, you know the new modern area you're managing it now. Clients are having a scenario where they, "Okay, I'm changing over the culture." I'm "Okay, I got some cloud, I got some public "and I got some hybrid and man, "we did some agile things. "We're provisioned, it's all done. "It's out there." And all of a sudden someone adds something new and it crashes (chuckles) And now I've got to get in, "Where's the risks? where's the security holes?" They're seeing this kind of day two operations as some people call, another buzz word but it's becoming more of, "Okay, we got it up and running "but we still now going to still push some code "and things are starting to break. "and that's net new thing." So it's kind of like they're out of their comfort zone. This is where I kind of see the AIOps evolving quickly because there's kind of a DevSecOps piece. There's also data involved, observability. How do you talk to that scenario? Where, okay, you sold me on cloud, I've been doing it. I did some projects. We're not been running. We got a production system and we added something new. Something maybe trivial and it breaks stuff? >> Yes. Yeah, so with the new cloud operations and SRE, the IT teams are much more responsible for business outcomes. And not just as you say, the application being deployed and the application being available, but the life cycle of that application and the results that it's bringing to the end users and the business. And what this means is that it needs to partner much more closely with development. And it is hard for them to keep up with the tools that are being used and the new code and the architectures of microservices that developers are using. So we like to apply AI on what we call the change risk management process. And so everyone's familiar with change management that means a new piece of code is being released. You have to maintain where that code is being released to was part of the application architecture and make sure that it's scaled out and rolled out properly within your enterprise policies. When we apply AI, we then apply what we call a risk factor to that change because we know so often, application outages occur not something new within the environment. So by applying AI, we can then give you a risk rating that says, "There's an 80% probability "that this change that you're about to roll out, "a code change is going to cause a problem "in this application." So it allows you to then go back and work with the development team and say, "Hey, how do we reduce this risk?" Or decide to take that calculated risk and put into the visibility of where those risks may occur. So this is a great example, change risk management of how applying AI can make you more intelligent in your decisions much more tied to the business and tied to the application release team. >> That's awesome. Well, I got you here on this point of change management. The term "Shift Left" has come up a lot in the industry. I'd love to get your quick definition of what that is in your mind. What does Shift Left mean for Ops teams with AIOps? >> Yeah, so in the early days of IT there was a hard line definitely between your development and IT team. It was kind of we always said throwing it over the fence, right? The developers would throw the code over the fence and say, good luck IT, you know, figure out how to deploy it where it needs to be deployed and cross your fingers that nothing bad happens. Well, Shift Left is really about a breaking down that fence. And if you think of your developers on your left-hand side you'd being the IT team, it's really shifting more towards that development team and getting involved in that code release process, getting involved in their CI/CD pipeline to make sure that all of your enterprise policies and what that code needs to run effectively in your enterprise application and architecture, those pieces are coded ahead of time with the developer. So it's really about partnering between it and development, shifting left to have a more collaboration versus throwing things over the fence and playing the blame game, which is what happens a lot in the early days IT. >> Yeah, and you get a smarter team out of it, great point. That's great insight. Thanks for sharing that. I think it's super relevant. That's the hot trend right now making dealers more productive, building security from the beginning. While they're doing it code it right in, make it a security proof if you will. I got to ask you one of the organizational questions as IBM leader. What are some of the roadblocks that you see in organizations that when they embrace AIOps, are trying to embrace AI ops are trying to scale it and how they can overcome those blockers. What are some of the things you're seeing that you could share with other folks that are maybe watching and trying to solve this problem? >> Yeah, so you know, AI in any industry or discipline is only as good as the data you feed it. AI is about learning from past trends and creating a normal baseline for what is normal in your environment. What is most optimal in your environment this being your enterprise application running in steady state. And so if you think back to the healthcare example, if we only have five or six pieces of patient data that we feed the AI, then the AI recommendation to the doctor is going to be pretty limited. We need a broad set of use cases across a wide demographic of people in the healthcare example, it's the same with IT, applying AI to IT. You need a broad set of data. So one of the roadblocks that we hear from many customers is, well I using an analytics tool already and I'm not really getting a lot of good recommendations or automation out of that analytics tool. And we often find it's because they're pulling data from one source, likely they're pulling data from performance metrics, performance of what's happening with the infrastructure, CPU utilization or memory utilization, storage utilization. And those are all good metrics, but without the context of everything else in your environment, without pulling in data from what's happening in your logs, pulling in data from unstructured data, from things like collaboration tools, what are your team saying? What are the customers saying about the experience with your application? You have to pull in many different data sets across IT and the business in order to make that AI recommendation the most useful. And so we recommend a more holistic true AI platform versus a very segregated data approach to applying and eating the analytics or AI engine. >> That's awesome, it's like a masterclass right there. Robin, great stuff. Great insight. We'll quickly wrap. I would love to you to take a quick minute to explain and share what are some of the use cases to get started and really get into AIOps system successes for people that want to explore more, dig in, and get into this fast, what are some use case, what's some low hanging fruit? What would you share? >> Yeah, we know that IT teams like to see results and they hate black boxes. They like to see into everything that's happening and understand deeply. And so this is one of our major focus areas as we do. We say, we're making AI purposeful for IT teams but some of the low hanging fruits, we have visions. And lots of our enterprise customers have visions of applying AI to everything from a customer experience of the application, costs management of the application and infrastructure in many different aspects. But some of the low hanging fruit is really expanding the availability and the service level agreements of your applications. So many people will say, you know I have a 93% uptime availability or an agreement with my business that this application will be up 93% of the time. Applying AI, we can increase those numbers to 99.9% of the time because it learns from past problems and it creates that baseline of what's normal in your environment. And then we'll tell you before an application outage occurs. So avoiding application outages, and then improving performance, recommendations and scalability. What's the number of users coming in versus your normal scale rate and automating that scalability. So, performance improvements and scalability is another low-hanging fruit area where many IT teams are starting. >> Yeah, I mean, why wouldn't you want to have the AIOps? They're totally cool, very relevant. You know, you're seeing hybrid cloud, standardized all across business. You've got to have that data and you got to have that incident management work there. Robin, great insight. Thank you for sharing. Robin Hernandez, vice president of Hybrid Cloud Management in Watson AIOps. Thanks for coming on theCUBE. >> Thank you so much for having me John. >> Okay, this theCUBE's coverage of IBM Think 2021. I'm John Furrier your host. Thanks for watching. (bright upbeat music)

>> Narrator: Live from Las Vegas, its theCUBE covering IBM Think 2018. Brought to you by IBM >> Welcome back to IBM Think 2018, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host Peter Burris, this is day three of our coverage. Mohammad Farooq is here, he's the general manager of Brokerage Services GTS at IBM. Mohammad, great to see you again, thanks for coming back on theCUBE. >> Thank you very much, appreciate for having me here. >> You're very welcome. So, big show. All the clients come together in one big tent. >> Yes. >> What do you think? >> It's very exciting. I think we're doing some interesting things with our technology. We have learned a lot from our clients the last two years. We are working very closely with our partners because we believe not one company can do everything in this massive transformation that's underway. So, working with our partners, with our clients on new technologies to specifically accelerate enterprized option of the cloud model and that's exciting for us. >> Partnering, it seems to have new, energized momentum at IBM. I sense a change, is it palpable? I mean, how can you comment on that? >> I think partnering is critical for everybody's success because the industry itself is transforming, and one company cannot achieve all the requirements that clients are asking for, and we have our core competencies. Service Now, our VMWare, our Amazon, our Azure They have their core competencies. But IBM, as a company, is a company that enterprisers trust to move them to cloud and operate them in the cloud. So what we are doing is, to keep that goal in mind, we are saying okay, we are going to take a client from point A, which is non-cloud, to point B, which is cloud native, and in that journey, we will take everybody our partners helped to get there. So that's why, based on client request, we are leveraging our partners, and it has a special meaning for us because it makes our clients successful. >> OK, so, describe exactly what brokerage services does. Is it your job to get people to the cloud? But talk more about that; add some color please. >> I think the brokerage has evolved since I last talked to you a year ago. At this conference, right? A lot of people think brokerage is arbitrage. >> Peter: Is what? >> Arbitrage of services from one provider to the other, that's the limited definition of brokerage. So what we're really calling it is Hybrid Cloud Management System, not brokerage. Brokerage is one part of it. So the Hybrid Cloud Management System is the go-forward strategy of IBM in 2019, 2018 and beyond. Which includes three, four components. One is: how do you bring the entire cloud ecosystem into a federated management maodel? Which includes: business management of IT and cloud, our hybrid. Consumption: a standard consumption model through one point of access to all clouds, internal or external. Third: delivery, how do we deliver services, either automated or workflow? Bi-model, as Gartner calls it, in one model. And four: operations management across public, private, hybrid, internal or external. >> Let me make sure I got this, so, business services in the sense of running IT more like a business, >> Mohammad: Right! >> A consumption model in terms of presenting this in a way that's simple and easy for a business-person to use, a delivery model, in the sense that it's very simple and straightforward and fast to deliver, and then an operations model which makes sure that everything above it works well. >> Yes, and the consumers, in this case, are developers, IT operations people, and DevApp teams, and from a delivery perspective, it is automated or people-work-flow, so you support both, so bringing this federated model together is a very complex undertaking, and IBM services is the strategic partner clients are asking to take them on this journey. Hey, bring this together for us. It's very complex at all layers. It's not a simple thing, and in that bringing it together, partners have a big role to play. Azure, Amazon, Google, Service Now, VMWare, Cisco, they all have critical pieces of it that make this model work, and clients have made choices. Clients already have VMWare. Clients already have Service Now Clients already have Amazon, Azure. But there is no system that brings it together and manages it on an ongoing basis, and the important thing is, the clouds keep changing very fast, and keeping up with the clouds, leveraging the power of the clouds to the right teams within the enterprise to deliver new digital apps, to delivery revenue, is what IBM is enabling our clients to do. >> So wikibot has actually done a fair amount of research around what we call the cloud operating model, we call the Digital Business Platform. AWS has an example of that, as you mention. They all have their approaches to handle those four things that you mentioned. >> Mohammad: Yes! >> But when you get to a customer, who also has to marry across these clouds, sustain some on-premises assets, perhaps some near-premises assets within the cost-service provider, it's what you're trying to do is ensure that they have their operating model that is the appropriate mix of all these different capabilities for their business, we got that right? >> Exactly, you got it. So what we said was every cloud provider, internal or external, or even hosting cloud providers, IBM is a hosting cloud provider. >> Right! >> With the adjustment of business. They had their own model across those four things: Business, consumption, delivery, operations. Now, we cannot operate four silos. Every enterprise is using Amazon, every enterprise has Google, every enterprise has VMWare, every enterprise has IBM. We cannot have four models. >> Dave: Right! >> So what we have done is we have created one standard consistent target operating model. We have integrated all these offerings within that so clients don't have to do it. We offer services to create extensions to it based on variations clients might have, and then operate it as a service for them, so that their path to cloud gets accelerated, and they start leveraging the power of what's good today inside the data centers, and what's available outside in public clouds, in a very secure way. So that is the business IBM is in moving forward, which we are calling it, we are transforming our offerings portfolio, we are calling it: Hybrid Cloud Services Business >> OK so you've got this hybrid operating model, IT operating model that you're envisioning, you're letting the cloud partners do what they do best, >> Mohammad: Right. >> Including your IBM cloud partners, >> Mohammad: Including our operating partners, >> And then you guys are bringing it all together in a framework, in an operating model, That actually can drive business value. >> Exactly, that's what we're doing. We are giving them ease of access from one place, choice of delivery platform, choice of delivery models from one place. Single visibility into how they're running, performing, help, and diagnostics from one place, and then, one billing and payment model, not four. So when I pay monthly bills, I pay based on usage, qualification of that usage across everybody, and then reconciling with my ERP systems, and making the payments. So the CFO has a standard way to manage payments. So that's what IBM is bringing to the table. >> How far could you take this? Could you take this into my SAS portfolio as well, Or is that sort of next step? >> What right now we are doing InfoSecure as a service and platform as a service. Our goal is in '18 and '19 to move to software as a service, because software as a service is much easier because we don't own the infrastructure or the service, we just consume it as electricity, utility. So that we discover the usage of SAS and meter it for usage against our billing model that we have to as B2B contract between a SAS provider and an enterprise and then make sure we've done the license management right. So there's companies like Flexera and others who do that For SAS management there's companies like Skyhigh Networks that recently got acquired, we're bringing those companies in to give us that component. >> But doing that level of brokering amongst the different services, while very useful, valuable, especially if you can provide greater visibility in the cost, because this becomes an increasing feature of COGs in a digital business, right? You still got to do a lot about the people stuff. A lot of folks are focused on ITIL, ITSM, automation at that level. Describe how you'll work with an IT organization and a business to evolve its underlying principals for how the operating model is going to work. >> I think that's probably a more difficult challenge than the technology itself, and if you look at our business, it was a people, it is a people business with GTS. We're more than 90,000 to 100,000 employees babysitting infrastructure for major fortune 500 corporations, and InfoSecure is more into software-defined, that means that we are moving from configuration skills to programming skills, where your programming API is in Amazon to provision infrastructure and deploy, so the skillsets have to definitely move. They have to move to infrastructure teams now have to become programming teams, which they have not been used to. They used to go to VMWare, vSphere, vCenter and configure VMs and deploy VMs. Now they have the right programs to drive and provision infrastructure, so that's one part of it. Second, the process was you do development and then your throw it over to operations, and they'll go configure and deploy production. Now, when you're programming infrastructure. Second, you're doing it in collaboration with developers, because developers are defining their own infrastructure in the cloud. So the process is different. The skills are different, and the process you are to operate in is not the same, it's different. Third, the technologies are different that you work with. So there is change at all levels and what GTS has done is we have put a massive goal in place to re-scale our workforce to take our people and re-scale them in the new process, the new technology and the new roles and that's a very big challenge I think the industry is facing: we don't have enough people who know this. A lot of these people are in Netflix, Facebook, Google, in Silicon Valley, and now, it takes time, it has happened before. The training and the transfer of knowledge, all of that is going on right now. So right now we have a crunch, And the second thing that is becoming more difficult is there's a lot of data coming out of these systems. The volume of data is unbelievable. Like if you look at Splunk and other tools and platforms, they collect a lot of log data. So all these cloud platforms spit out a lot of machine data. Humans cannot comprehend that. It's incomprehensible. So we need machine learning skills and data science skills to understand how these systems are performing. >> Peter: And tools. >> And tools. So we need the AI skills, the data science skills, in addition to the infrastructure design architecture and programming skills. So we really have a challenge on our hands as an industry to kind of effectively build the next-gen management systems. >> Right and we've got, so we've got all these clouds, the ascendancy of clouds has brought cloud creep, >> Mohammad: Right. >> All these bespoke tools along with them, all these different operating models. You're clearly solving a problem there. What's the go-to-market model with all these partners that you've mentioned? You've got cloud, you got PRAM, eventually SAS, >> Yeah, so our cloud go-to-market is three ways We see clients adopting cloud in three ways. One is digital initiatives: They want to go build new IOD apps or mobile apps and they want to put it in production that drive revenue, okay? So we are creating offerings around the DevApps model. We'll say like look, the biggest challenge that our folks have is how to put a app that you build in production. I built a new feature, how can I get it to my client as soon as possible, in a secure way, that can scale and perform, that is the biggest problem with app developers. I can develop anywhere, it's all open-source. I'm not living in, and I can spin up a VM or a container in Amazon and develop a service in two days. But to put it in production, it takes a long time. How can we make offerings that accelerate that? Through our DevApp CICD automation process I was talking about, that's our revenue play. So our go-to-market is driven by how we can generate revenue for our clients through agile offerings for DevApps, that's one go-to-market. Second go-to-market is CIOs are saying like look, I'm spending a lot of money managing my current infrasatructure and my current app portfolio, and I can take money out of the system through cost reductions, so what is my migration and modernization path for my existing portfolio? >> Well, slightly differently, I used to get I used to get my eight to nine percent that I gave back to the business every year simply by following hardware price performance. >> Mohammad: That's exactly right. >> That's not available in the same way. I have to do it through process and automation. >> All automation right? So then we have to look at everything. What part of the portfolio can move to Amazon or cannot? What other refactoring I have to do to microservices and containers to build portability to move to the cloud? So we have created a migration, a global migration practice at IBM in a factory in India and in the US where we have created offerings to work with the CIO right from planning, cost planning, portfolio planning, application design planning and design review, to lift-and-shift, to deploy in cloud and operate it. So we have a series of offerings that track the life-cycle of migration. So that's our second go-to-market path. Our third go-to-market path is: Hey, my business per units are shadow IT; they're already in the cloud, now my CEO is telling me: Hey mister CIO, you make sure they all work and they're secure, and there's no loss in data. And this infrastructure is now in cloud and on-prem. So how do I provide, manage service, to manage your infrastructure and workloads in the cloud? IBM has offerings that will directly provide you multi-cloud management as managed service. So we are taking three client journeys and we are building go-to-market offerings around those three, and we have built, we have re-designed IBM portfolio to operate on those lines. >> Do the digital initiatives, chief digital officer, obviously, target their CIO for the portfolio rationalization optimization and line of business through the shadow IT? >> Right! >> And you bring those together with a constant consistent operating model? >> Exactly, so all three journeys lead to one operating model. >> Dave: Yeah! >> But going back to what Dave said, and we have time for just a little bit more, is, is, no offense, there's no way you can do it all by yourself. >> Mohammad: You cannot. >> So what are some of the core, what are some of the most important partnerships that users need to be looking to? >> I think we have defined what's goal to us. Not always go back to, if you are clearly going to market, what is the core competency of IBM? Okay, with (mumbles) we're going to service this company for a long time, right? We made sure we are, we bring the complexity and control and we manage the complexity; that's our core business. We had mainframe business, we had software business, and a very profitable software business. So we've done all three, hardware, software, and services. As we go forward, cloud services, cloud managed services, our IBM services, is a core competency for us, which is planning, design, managed services, and services integration, to bring these tool sets together from different partners, and operationalizing it, and babysitting it and offering it as a service. So services business is our core offering. Now in the software space, which is the management software, which is service now, (mumbles) Cisco, there there is many layers to it, as I talked about the four things: consumption, operations management, business management, >> And service delivery >> And service delivery. And in service delivery we have three choices: we have VMWare, we have Microsoft and we have IBM. We have stitched it together in a federated framework. The stitching together is our core competence. Okay, Operations management. We have created a federated data lake because data will drive everything going forward. So we own the data lake as our core competency and Watson driving intelligence. But some of the monitoring tools like AppDynamics, New Relic, Splunk, that collect the data, those are our partners. We're integrating that into our Watson framework. So we're looking at core versus non-core in all four layers, and wherever there's a overlap, we're creating unique vertical go-to-market strategies. Here, for this segment, we overlap with you, we agree to compete, to your clients you can lead with that, for our clients we'll lead with ours, so we agree to disagree, but we are going to stick to the target operating model, so that our clients are successful. So there's no confusion we are creating in their minds. So its a very complex dance at this point. >> But you laid it out and it's coherent. >> Right. >> It's got to start there. >> The most important thing is we need to tell our clients what is our core, and what is the core we're going to stand behind? And that core delivers them bottom-line value to move from point A to point B and be successful in the cloud. >> Well Mohammad, I think you've defined those swim lanes, you obviously trust and you've got the trust of your partners, trust of your customers. Like you say, you agree to compete where it makes sense, and you bring core competency and value to differentiate from your competition, so, >> Right. >> Dave: Congratulations on laying that out. We really appreciate you coming on theCUBE. >> Thank you very much. Appreciate it. >> You're welcome. All right, keep it right there everybody, we'll be back with our next guest. You're watching theCUBE live from Think 2018, we'll be right back. >> Mohammad: Thank you very much. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back everyone, we are live here at the Mandalay Bay for exclusive CUBE three-day coverage of IBM InterConnect 2017, I'm John Furrier with my co-host, Dave Vellante for all three days, we're on day three, winding down, great show, our next guest is Justin Youngblood, VP of Hybrid Cloud Management with IBM, welcome to theCUBE. >> Thank you for having me. >> Great to have you on, because a lot of the talk, obviously, cloud, we could blockchain, but a lot of under-the-hood production workload stuff still needs to manage with all this stuff. You guys had an announcement on day one on the cloud automation management. Big part of the keynote, so it was kind of a primetime spot. Can you share us, well, why'd you get that great slot, how did you get the great slot, and what's the impact? >> Well, it really all starts with what's happening in the market, and the team's been working hard inside of IBM, we announced IBM Cloud Automation Manager, it was elevated to a tier-one offering, very strategic space for IBM in multi-cloud management. What we know is, every enterprise is now moving towards multi-cloud environments, cloud adoption is well into its maturity, and really, it's 71% of enterprises have three or more clouds, and they need to manage those clouds with a common management platform, and that's what cloud-- >> And it's big paying point too, it's one of those non-sexy items like blockchain, it's like, AI and blockchain took the headlines, but a lot of the blocking and tackling is going on in hybrid right now, so you see that orchestration piece between multi-cloud, little things like latency, security, workload migration, this is what you guys are doing, bringing the IT operations to a modern level, is that kind of the main thing? >> That's exactly right, and there're really two entry points to this, because on the one hand, it is that IT team, when you think of the modern enterprise, every modern enterprise is trying to move faster, trying to get applications out faster, trying to better engage with their customers, essentially trying to digitally transform, be the disruptor instead of the disrupted, and often, they'll look at their IT team and say, "You're not keeping up, you're too slow," so this is an automation and orchestration tool that allows the IT teams to rapidly deploy applications and infrastructure to the line of business and to their devops teams. >> Well, that's the thing, you got developers, not just IT, you got developers and the line of business who have a financial stakeholder, the top line revenue, to make it happen, and you got the movement to true private cloud happening. What's different now for you guys with automation? What's the key unique thing in this announcement that makes it go to the next level? >> Several things there, but no solution is complete from IBM these days without cognitive, and so bringing in those cognitive services and insights to analyze and help optimize the performance of workloads on any cloud environment, and also really to provide an advisor role, prescriptive guidance and recommendations on where to place workloads to optimize performance, cost, compliance right within company policy and security and regulatory environments. >> So we had Mohammed Farooq on earlier, and he was talking about cloud brokerage services, and I wonder, as you enter this market, if you're starting to see different KPIs emerge, the traditional IT operations KPIs, okay, the light on the server's on, it's uptime, planned downtime, unplanned downtime, percentage of my backups that fail, whatever it is, are there new KPIs emerging as people become cloud brokers? >> Yeah, absolutely, and Mohammed's a good friend, we're both Austenites, right, in the same building. >> Dave: Another Austenite! Austin's dominating theCUBE this week! >> We talk regularly, and really, we see a nice synergy because the cloud brokerage tool, which is brokering across the application readiness assessments of putting workloads onto the cloud and then planning and cost analysis and so on, and then the orchestration of actually deploying those workloads, so there's a nice synergy, and then, really, the third leg of the stool in my mind then plays into service management, and having the integration across all those pieces is really important, so being both cloud agnostic for multi-cloud environments, but then also having an open API, an ecosystem that you can enable and plug in with existing tools. >> Now, there was a period of time where IT was almost afraid of automation, but then this cloud thing sweeps over them, are we past that now? >> We are past that, and it's a great point, because sometimes, IT can be afraid of automation, 'cause they can think, "That's threatening my job." But we've got client success stories where we're running our cloud orchestration and hybrid cloud management solutions at massive scale, literally saving dozens of full-time equivalent hours, and what we're finding is these enterprisers saying, "Finally! "Now I can get to the innovation "and the transformative projects "that are on the strategic agenda "rather than working within manual IT processes," so it's really been a win-win. >> And when you talk about that average stat, the average enterprise has, you said three clouds? >> Three or more clouds, 71% of enterprises have three or more clouds. >> Are you excluding SaaS in that number? 'Cause-- >> Excluding SaaS, because you think about-- >> Dave: Alright, so that's infrastructure clouds, right? >> Absolutely, private clouds, public clouds, and a lot of departmental clouds or shadow IT where different cloud services are being consumed even if the IT team may not be managing it. >> So that brings the question, then, where does SaaS play, if I'm a cloud broker, and I've got these corporate edicts, and I've got these KPIs around running the business and transforming the business. How do I apply those edicts to SaaS, and can you help me do that? Is that futures, or is that just sort of a separate island? >> Yeah, it's a little bit futures right now, many times with the cloud management platforms in particular, these tools are used to automate the deployment of the infrastructure, and what's unique in our solution is the full stack application and even the day two operations, but the SaaS applications are tending to come in through a slightly different channel now, over time, I think what we're going to see is all applications, whether they're delivered by the IT team, or from the cloud, need to come into a common-- >> And should CIOs be worried about that? Because each SaaS provider has different infrastructure, some of the different availability profiles, different definitions, different SLAs, that's a whole 'nother problem area to be attacked, I guess. >> No, it is a concern, just the application sprawl, infrastructure sprawls, cloud sprawl, and this is why I think any time we're entering into a new industry, we're going to see that expanse and then back to a convergence, and honestly, I presented with Dave Bartoletti from Forrester this week, and a lot of his insights and things that he writes about and what I spoke about, and what my team did in our sessions was the need for a common management platform because of that sprawl, it's reining in the chaos. >> What are some of your favorite examples, customer case, the early wins? >> Yeah, so a great case study is that Swiss Re, large global insurance company, 60 global offices, this is a company that uses our cloud orchestrator solution with business process manager, their environment includes WebSphere, but also Microsoft Active Directory, ServiceNow, Puppet, et cetera. When they came and used our solution to, really, to automate the deployment of applications to put applications and IT as a service into a self-service catalog for their line of business and development users, at the end of the day, they have automated 45,000 processes executed each month, and literally dozens of offerings into the service catalog now. >> So the IT service management business has been evolving very rapidly, cloud has impacted that, the on-premise ratios are going to probably shift a little bit, but not radically, but then again, the use cases for public cloud are going to be dependent upon the workload, so that's kind of well-defined and discussed. The question I have for you is, from a customer standpoint, the number one competition we're having, and we're seeing, digitally at least, on Twitter and theCUBE is, what does enterprise readiness mean? So I'm an enterprise, and I want to go to the cloud. I have to then evaluate which cloud is best for which workload, but then I also have to put it through the prism of readiness, their table stakes, do they have the table stakes? >> Yeah, absolutely. >> Google's got some great machine learning, but the SLAs might not match up, or Amazon's got some great Kinesis for analytics, but I can't run my other thing on that. That's comes up a readiness problem. >> It is a readiness, and I would say, there is no single cloud that is purpose-built for all workloads, and a lot of the messages you heard here at InterConnect this week, even from Ginni Rometty herself, where IBM has the enterprise-ready cloud, and a lot of data points to back that up, including every enterprise that's going to be cognitive, and the way we think about that is cloud and cognitive are two sides of the same coin, a famous quote also from Ginni. But now we're getting into trusted networks and Hyperledger and blockchain, I don't want to get too far offtrack, but it's some-- >> But they'll all bent the change on the disruption side, on the innovation side, and honestly impact some of the blocking and tackling table stakes. >> The blocking and tackling, so that gets down to some of the regulatory concerns and other pieces, which is why we've invested now to have 51 data centers around the world, because of data locality and security concerns that companies have, so there's a lot-- >> Well, I love her line, she's the best, I got to say, very memorable, enterprise strong, made me think of the whole Boston strong thing instilled in my head, 'cause, being from that area. Enterprise strong, data first, cognitive to the core, those are the three pillars, you can unpack that in every different way, so you guys have to bring that into your offering, so I get the enterprise strong. Data first, how are you guys using the cognitive piece, specifically, in this? Data first, is that an architectural thing? And then where's the cognitive piece fit in? >> Yeah, perfect, so as we architected this solution, it was really important to us to put cognitive at the core. And really, two use cases, primarily, the first is around, as companies go deploying their applications and workloads on the cloud, every application is going to have its downtimes, its slowdowns, its outages, and that impacts end user experience, that's why it matters, it can impact revenue or NPS scores for the company. So the first is a cognitive operations capability, and you can think of that analytics moving from log analytics to quickly pinpoint the root cause of issues, up through predictive analytics to prevent an outage or an issue before it impacts your end users, ultimately into the cognitive domain, which is a true machine learning, and the capabilities that we're working on on our labs now, and that we demonstrated this week at InterConnect, we actually have a chat ops interface for the IT operator to come and interact with a cognitive system that's part of Cloud Automation Manager, and get prescriptive guidance and confidence levels-- >> Going to be a voice-activated Watson, basically, in the future. "Hey, move to cloud nine!" >> So that's the differentiation, right, if I were to push you on that, it's trust, everybody's going to say they have cloud, but like you said, it's a multi-cloud world, and it's the cognitive piece, is that right? It's really the trust and the cognitive piece. >> The cognitive piece is absolutely the number one piece of differentiation that no one has. >> Because a lot of big enterprise hardware and software companies are going to say, "You trust us," people do trust us, that's how they got to be multi billion dollar companies, but talk a little bit more about the differentiation with respect to cognitive. >> Yeah, so that's one aspect of it, and that's just cognitive operations management, and even that is that one level of value. Where I think there's additional value is getting into really letting Watson, and cognitive services, become an advisor to your business, so imagine your smartest IT operator in the business, if Watson can learn from that person, Sally or Jeff, whoever it is, learn from that, and help every IT operator in your business always make the best decision as smart as the smartest subject matter expert is in IT operations, and so this is the learning aspect of cognitive, and in that advisor role now, all of a sudden, a cognitive chat ops interface can begin to provide prescriptive guidance when there's an outage. Or imagine an application or workload going down, and Watson taking automatic action to redeploy the workload on a different cloud that has not been impacted, no interruption of service to the end user, and then come back and say, now let's pinpoint the root cause of the problem and fix that, but I've already address the main point, so-- >> And what's key about that is it's a learning machine model, so you have the domain expertise of the specific use cases, it's not trying to use some sort of vocabulary and map that on through an infrastructure environment or software environment. >> Very plain language, natural language understanding, and it's really, really powerful capability. >> Alright, so the question is, how do customers get access to this, Bluemix storage, is there IBM.com, what's the vehicles for getting this in the hands of customers? >> The easiest way is at IBM.biz/tryibmCAM, so if you go there, it'll take you right into our Bluemix service, and customers can get started right away, we have a free addition that allows customers to get started with the-- >> I know this is a tough personal question, but I'll ask anyway, no one likes to pick who their favorite child is, but what's most exciting about the product from your standpoint, looking at the success of the announcement, obviously, primetime on the keynote, congratulations, but what's the one thing that you get most excited about the product? >> Yeah, the most exciting thing is, it's all about the application. It's all about the application and digital transformation, so, certainly, the cognitive piece, and we've talked about that, but I want to highlight one other thing, which is, we in IBM are providing pre-built automation content from the infrastructure up through full-stack applications and getting into the day two operations, the monitoring, the backup, et cetera, we can orchestrate that end-to-end, unlike anyone in the industry. >> End-to-end is the key word. This is now big part of the architecture. End-to-end cross vendor. >> Exactly. >> And opensource. >> Yep. >> That's kind of the big-- >> Dave: That's what you call automation packs? >> These are the cloud automation packs, exactly, in the past, we called them patterns, we're moving to an open-pattern technology base, and we call 'em cloud automation packs. And I'll just say more about that, we're going to make them available in a marketplace, in the IBM cloud marketplace so clients can come, learn about, discover, try, and buy these automation-- >> Alright, so here's the hard question for you. Well, might be easy for you, hard for me, but as you go end-to-end, which is totally the right way, I believe, that's what everyone wants, end-to-end, but you're crossing horizontal and vertical specialty across multiple vendors, and new things coming, so now 5G comes enables autonomous vehicles, now you got smart cities, now you got Watson trying to learn new environments that I've never seen before in IT. How do you guys prepare for that, what are you guys doing to get out in front of that next wave? >> Yeah, so in the past, I think a lot of applications, and even management tools have been built as monolithic applications. With the Cloud Automation Manager, we built it from the ground up, it's cloud-native, microservices-based, just like a lot of applications out there in the enterprise are bring run, that allows us to be much more composable and flexible than we've ever been in the past, and we augment that with a set of open APIs to integrate with clients' existing tools, you heard me mention the example of integrating with ServiceNow, of course, we can integrate with UrbanCode or other devops tools, APM and monitoring tools, et cetera. >> That's the key, integration is the new table stake. >> That is the new table stake. >> Justin Youngblood, thanks for coming on theCUBE, great, congratulations on the success of your launch, and good luck with the adoption, and we'll see ya out in the marketplace, thanks for coming on theCUBE, Justin, the VP of Cloud Management inside theCUBE, more cloud action, more data action, more predictive content here on theCUBE, more great interviews coming, stay with us, I'm John Furrier with Dave Vellante, we'll be right back.