>> 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)

>> Presenter: From around the globe, it's theCUBE with digital coverage of IBM think2021 brought to you by IBM. >> Well, good to have you here on theCUBE. We continue our conversations here as part of the IBM Think initiative. I'm John Walls, your host here on theCUBE joined today by Mirko Novakovic, who is the co-founder and CEO of Instana which is an IBM company. Is specialized in enterprise observability for cloud native applications. And Mirko joins us all the way from Germany, near Cologne, Germany. Mirko, good to see it today. How are you doing? >> I'm good. Hi, John. Nice to meet you. >> You bet yeah. Thank you for taking the time today. First off, let's just give some definitions here. Enterprise observability. What is that? What are we talking about here? >> Yes observability is basically the next generation of monitoring, which means it provides data from a system, from an application to the outside, so that people from the outset can basically judge what's happening inside of an application. So think about you're a big e-commerce provider and you have your shop application and it doesn't work. Observability gives you the ability to really deep dive and see all the relevant metrics, logs and application flows to understand why something is not working as you would expect. >> So if I'm, or just listening to this, I think, okay, I'm monitoring my applications already right. I've got to APM and enforce and and I kind of know what things are going on. What's happening, where the hiccups are, all that. How, what is the enhancement here then in terms of observability taking, it sounds like you're kind of taking APM to a much higher level. >> Absolutely. I mean that's essentially how you can think about it. And we see three things that really make us Instana and enterprise observability different. And number one is automation. So the way we gather this information is fully automated. So you don't have to configure anything. We get inside of your code. We analyze the flow up the clarification we get the arrows, the logs and the metrics fully automatic. And the second is getting context. One of the problems with monitoring is if you have all these monitoring data silos so you have metrics on the one side locks into different tool. What we built is a real context. So we tie those data automatically together so that you get real information out of all the data. And the third is that we provide actions. So basically we use AI to figure out what the problem is and then automate things. Is it a problem resolution, restarting container or resizing your cloud? That's what we suggest automatically out of all the context and data that we gathered. >> So you're talking about automation, context, intelligence you'd combine all of that into one big bundle here then basically, that's a big bundle, right? I'm not a giant vacuum. If you will, you're ingesting all this information. You're looking for, you know, performance metrics. So you're trying to find problems. What's the complexity of tying all that together instead of keeping those functions separate you know, what are what's the benefit to having all that kind of under one roof then? >> Yeah. So from the complexity point of view for the end customer it's really easy because we do it automated. For us as a vendor building this it's super complex but we wanted to make it very easy for the user and I would say the benefit is that you get, we call it the meantime to repair like the time from a problem to resolve the problem gets significantly reduced because normally you have to do that correlation of data manually. And now with that context you get this automated by a machine and we even suggest you these intelligent actions to fix the problem. >> So, I'm sorry, go ahead. >> Yeah. And by the way, one of the things why IBM acquired us and why we are so excited working together with IBM is the combination of that functionality with something like Watson AIOps, because as I said we are suggesting an action and the next step is really fully automating this action with something like Watson AIOps and the automation functionality that IBM has. So that the end user not only gets the information what to do the machine even does and fix the problem automatically. >> Well, and I'm wondering too, just about the kind of the volume that we're dealing with these days in terms of software capabilities and data. You've got obviously a lot more inputs, right? A lot more interaction going on a lot more capabilities. You've got apps they're kind of broken down into microservices now. So, I mean, you've got you've got a lot more action, basically, right? You got a lot more going on and what's the challenge to not only keeping up with that but also building for the future for building for different kinds of capabilities and different kinds of interactions that maybe we can't even predict right now. >> Absolutely. Yeah. So I'm 20 years in that space. When I started, as you said it was a very simple system, right? You had an application server like WebSphere maybe a DB2 database so that was your application. It's like today applications are broken down into hundreds of little services that communicate with each other. And you can imagine if something breaks down in a system where you have two or three components it's maybe not easy, but it's handled by a human to figure out what the problem is. If you have a thousand pieces that are somehow interconnected and something is broken it is really hard to figure that out. And that's essentially the problem that we had to solve with the contacts, with the automation, with AI to figure out how all these things are tied together and then analyze automatically for the user where issues are happening. And by the way, that's also when you look into the future I think things will get more and more complicated. You can see now that people break down from microservice into functions, we get more server less. We get more into a hybrid cloud environment where you operate on premise and in multiple clouds. So things get more complex not less complex from an architectural perspective. >> You bring up clouds too. Is this agnostic, I mean, or do you work with an exclusive cloud provider or are you open for business basically? >> We are open for business but we have to support the different cloud technologies. So we support all the big public cloud vendors from IBM to Amazon, Google, Microsoft. But on the other hand, we see with enterprises maybe there's 10, 20% of the workload in the public cloud but the rest is still on premises. And there's also a lot of legacy. So you have to bring all this together in one view and in one context, and that's one of the things we do. We not only support the modern cloud native applications we also support the legacy on premise world so that we can bring that together. And that helps customer to migrate, right? Because if they understand the workload in the on-premise world it's easier to transform that into a cloud native world but it also gives an end to end view from the end user to we always say from mobile to mainframe, right? From a mobile app down to the mainframe application we can give you an end to end view. >> Yeah, you talk about legacy. In this case, you may be cloud services that people use but they're, but that, you know a lot of these legacy applications, right, too that are running, that are they're still very useful and still highly functional but at some point they're not going to be so would it be easier for you or what do you do in terms of talking with your clients in terms of what do they leave behind? What are they bringing with them? How, what kind of transition timeframe should they be thinking about? Because I don't think you want to be supporting forever, right? I mean, you want to be evolving into newer more efficient services and solutions. And so you've got to bring them along too, I would think. Right? >> Yeah. But to be really honest I think there are two ways of thinking. One is as a vendor you would love to support only the new technologies and don't have to support all the legacy technologies. But on the other hand, the reality is especially in bigger enterprises you will find everything in every word. And so if you want to give a holistic D view into the application stacks you have to support also the older legacy parts because they are part of the business critical systems of the customer. And yes, we suggest to upgrade and go into a cloud native world, but being realistic I think for the next decade we will have to live with a world where you have legacy and new things working together. I think that's just the reality. And in 10 years, what is new today is legacy then, right? >> John: Right exactly. >> So we will always live in a kind of hybrid world between legacy and new things. >> Yeah, you've got this technological continuum going on right? That you know, what's new and shiny today's is going to be, you know old hat in five years. But that's the beauty of it all obviously >> Yes. >> Now talk about AIOps. I mean, go into that relationship a little bit if you would , I mean eventually what is observability set you up to do in terms of your artificial intelligence operations and what are the capabilities now that you're providing in terms of the observability solutions that AIOps can benefit from? >> Yeah, so the way I think about these two categories is that observability is the system of record. That's where all the data is collected and put into context. So that's what we do as Instana is we take all the data metrics, logs, traces, profiles and put it into our system of record by the way in very high granularity, it's very important. So we do not sample, we have second granularity metrics. So very high quality data in that system of record where AIOps is the system of action. This is the system where it takes the data that we have, applies machine learning, statistical analytics et cetera, on it, to figure out, for example root cause of problems or even predict problems in the future, and then suggests actions, right? What the next thing that AI does is it suggests or automates an action that you need to do to to for example, scale up the system, scale down the system scaling down because you want to save costs for example these are all things that are happening in the system of action, which is the AIOps space. >> When I think about what you're talking about in terms of observability, I think, well, who needs it? Everybody is probably the answer to that. Can you give us maybe just a couple of examples of some clients that you've worked with in terms of particular needs that they had, and then how you applied your observability platform to provide them with these kinds of solutions? >> Yeah. I remember a big e-commerce vendor in the US approaching us last October. They were approaching the black Friday, right? Where they sell a lot of goods and they had performance issues but they only had issues with certain types of customers and with their existing APM solution, they couldn't figure out where the problem is because existing solutions sample which means if you have a thousand customers you only see one of them as an example because the other 999 are not in your sample. And so they used us because we don't sample. With us, if you have, they have more than a billion requests today you see every of the 1 billion requests and after a few days they had all the problems figured out. And that's what, that was one of the things that we really do differently is providing all the needed data, not sampling and then giving the context around the problem so that you can solve issues like performance issues on your e-commerce system easily. So they switched and you can imagine switching assistant before black Friday, you only do that if it's really needed. So they were really under pressure and so they switched their APM tool to Instana to be able to fulfill the big demand they have on these black Friday days. >> All right, before I let you go you were just saying they had a high degree of confidence. How were you sweating that one out? Because that was not a small thing at all I would assume. >> Yes. It's not a small thing and to be honest, also it's very hard to predict the traffic on black Fridays. Right? And in this case, I remember our SRE team. They had almost 20 times the traffic of a normal day during that black Friday. And because we don't sample, we need to make sure that we can handle and process all these traces but we did we did pretty well. So I have a high confidence in our platform that we can really handle a big amounts of data. We have one of the biggest companies in the world. The biggest companies in these worlds they use our tool to monitor billions of requests. So I think we have proven that it works. >> Yeah, I would say you're smiling too about it. So I think it, obviously it did work. >> It did work, but yeah, I'm sweating still. Yeah. (laughs) >> Never let them see you, sweat Mirko. I think you're very good at that. And obviously very good at enterprise observability. It's an interesting concept. Certainly putting it well under practice. And thanks for the time today to talk about it here as part of IBM thing to share your company's success story. Thank you Mirko. >> Thanks for having me John. >> All Right. We've been talking about enterprise observability here. IBM Think, The initiative continues here on theCUBE. I'm John Walls and thank you for joining us. (soft music)

>> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's maximo mobile. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the--example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about to slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

(soft music) >> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's Maximo Mbo. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the C QVS example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about the slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft 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)