(upbeat music) >> Hello everyone and welcome to theCUBE. In this special power panel we're going to dig into and take a peek at the future of cloud. You know a lot has transpired in the last decade. The cloud itself, we've seen a data explosion. The AI winter turned into machine intelligence going mainstream. We've seen the emergence of As-a-Service models. And as we look forward to the next 10 years we see the whole idea of cloud expanding, new definitions occurring. Yes, the world is hybrid but the situation is more nuanced than that. You've got remote locations, smaller data centers, clandestine facilities, oil rigs, autonomous vehicles, windmills, you name it. Technology is connecting our world, data is flowing through the pipes like water, and AI is helping us make sense of the noise. All of this, and more is driving a new digital economy. And with me to talk about these topics are three great guests from IBM. Maia Sisk is the Director of SaaS Offering Management, at IBM Data and AI. And she's within the IBM Cloud and Cognitive Software Group. Stephanie Walter is the Program Director for data and AI Offering Management, same group IBM Cloud and Cognitive Software. And Daniel Berg is a Distinguished Engineer. He's focused on IBM Cloud Kubernetes Service. He's in the Cloud Organization. And he's going to talk today a lot about IBM's cloud Satellite and of course Containers. Wow, two girls, two boys on a panel, we did it. Folks welcome to theCUBE. (chuckles) >> Thank you. >> Thank you. >> Glad to be here. >> So Maia, I want to start with you and have some other folks chime in here. And really want to dig into the problem statement and what you're seeing with customers and you know, what are some of the challenges that you're hearing from customers? >> Yeah, I think a big challenge that we face is, (indistinct) talked about it earlier just data is everywhere. And when we look at opportunities to apply the cloud and apply an As-a-Service model, one of the challenges that we typically face is that the data isn't all nice cleanly package where you can bring it all together, and you know, one AI models on it, run analytics on it, get it in an easy and clean way. It's messy. And what we're finding is that customers are challenged with the problem of having to bring all of the data together on a single cloud in order to leverage it. So we're now looking at IBM and how we flip that paradigm around. And instead of bringing the data to the cloud bring the cloud to the data , in order to help clients manage that challenge and really harness the value of the data, regardless of where you live. >> I love that because data is distributed by its very nature it's silo, Daniel, anything you'd add? >> Yeah, I mean, I would definitely echo that, what Maia was saying, because we're seeing this with a number of customers that they have certain amount of data that while they're strategically looking that moving to the cloud, there's data that for various reasons they can not move itself into the cloud. And in order to reduce latency and get the fastest amount of processing time, they going to move the processing closer to that data. And that's something that we're looking at providing for our customers as well. The other services within IBM Cloud, through our notion of IBM Cloud Satellite. How to help teams and organizations get processing power manage them to service, but closer to where their data may reside. >> And just to play off of that with one other comment. Then the other thing I think we see a lot today is heightened concerned about risks, about data security, about data privacy. And you're trying to figure out how to manage that challenge of especially when you start sending data over the wire, wanting to make sure that it is still safe, it is still secure and it is still resident in the appropriate places. And that kind of need to manage the governance of the data kind of adds an additional layer of complexity. >> Right, if it's not secure, it's a, non-starter, Stephanie let's bring you into the conversation and talk about, you know, some of the waves that you're seeing. Maybe some of the trends, we've certainly seen digital accelerate as a result of the pandemic. It's no longer I'll get to that someday. It's really, it become a mandate you're out of business, if you don't have a digital business. What are some of the markets shifts that you're seeing? >> Well, I mean, really at the end of the day our clients want to infuse AI into their organizations. And so, you know, really the goal is to achieve ambient AI, AI that's just running in the background unchoosibly helping our clients make these really important business decisions. They're also really focused on trust. That's a big issue here. They're really focused on, you know, being able to explain how their AI is making these decisions and also being able to feel confident that they're not introducing harmful biases into their decision-making. So I say that because when you think about, you know digital organization going digital, that's what our customers want to focus on. They don't want to focus on managing IT. They don't want to focus on managing software. They don't want to to have to focus on, you know, patching and upgrading. And so we're seeing more of a move to manage services As-a-Service technologies, where the clients can really focus on their business problems and using The technologies like AI, to help improve their businesses. And not have to worry so much about building them from the ground up. >> So let's stay on that for a minute. And maybe Maia, Daniel, you can comment. So you, Stephanie, you said that customers want to infuse AI and kind of gave some reasons why, but I want to stay on that for a minute. That, what is that really that main outcome that they're looking for? Maybe there are several, they're trying to get to insight. You mentioned that trynna be more efficient it sounds like they're trynna automate governance and compliance, Maia, Daniel can you sort of add anything to this conversation? >> Yeah, well, I would, I would definitely say that, you know at the end of the day, customers are looking to use the data that they have to make smarter decisions. And in order to make smarter decisions it's not enough to just have the insight. The insight has to, you know, meet the business person that needs it, you know in the context, you know, in the application, in the customer interaction. So I think that that's really important. And then everything else becomes like the the superstructure that helps power, that decision and the decision being embedded in the business process. So we at IBM talk a lot about a concept we call the Ladder to AI. And the the short tagline is there is no AI without IA. You know, there is no Artificial Intelligence without Information Architecture. It is so critical, you know, Maia's version this is the garbage in garbage out. You have to have high quality data. You have to have that data be well-organized and well-managed so that you're using it appropriately. And all of that is just, you know then becomes the fuel that powers your AI. But if you have the AI without having that super structure, you know, you're going to end up making, get bad decisions. And ultimately, you know our customers making their customers experience less than it could and should be. And in a digital world, that's, you know, at the end of the day, it's all about digitizing that interaction with whoever the end customer whoever the end consumer is and making that experience the best it can be, because that's what fuels innovation and growth. >> Okay. So we've heard Arvind Krishna talk about, he actually made this statement IBM has to win the architectural battle for cloud. And I'm wondering maybe Daniel you can comment, on what that architectural framework looks like. I mean Maia just talked about the Information Architecture. You can't have AI without that foundation but we know what does Arvind mean by that? How is IBM thinking about that? >> Yeah, I mean, this is where we're really striving to allow our customers really focusing on their business and focusing on the goals that they're trying to achieve without forcing them to worry as much about the IT and the infrastructure and the platform for which they're going to run. Typically, if you're anchored by your data and the data is not able to move into the cloud, generally we would say that you don't have access to cloud services. You must go and install and run and operate your own software to perform the duties or the processing that you require. And that's a huge burden to push onto a customer because they couldn't move their data to your cloud. Now you're pushing a lot of responsibilities back onto them. So what we're really striving for here is, how can we give them that cloud experience where they can process their data? They can run their run book. They can have all of that managed As-a-Service so that they could focus on their business but get that closer to where the data actually resides. And that's what we're really striving for as far as the architecture is concerned. So with IBM Cloud Satellite, we're pushing the core platform and the platform services that we support in IBM Cloud outside of our data centers and into locations where it's closer to your data. And all of that is underpinned by Containerizations, Containers, Kubernetes and OpenShift. Is fundamentally the platform for which we're building upon. >> Okay. So that, so really it's still it's always a data problem, right? Data is you don't want to move it if you don't have to. Right. So it's, so Stephanie, should we think about this as a new emergent data architecture I guess that's what IA is all about. How do you see that evolving? >> Well I mean, I see it evolving as, I mean, first of all our clients, you know, we know that data is the lifeblood of AI. We know the vast majority of our clients are using more than one cloud. And we know that the client's data may be located in different clouds, and that could be due to costs, that could be due to location. So we have to ask the question, how are our clients supposed to deal with this? This is incredibly complex environments they're are incredibly complex reasons sometimes for the data to be where it is. It can include anything from costs to laws, that our clients have to abide by. So what we need to do, is we need to adapt to these different environments and provide clients with the consistent experience and lower complexity to be able to handle data and be able to use AI in these complex environments. And so, you know, we know data, we also know data science talent is scarce. And if each one of these environments have their own tools that need to be used, depending on where the data is located, that's a huge time sink, for these data scientist and our clients don't want to waste their talents time on problems like this. So what we're seeing is, we're seeing more of a acceptance and realization that this is what our clients are dealing with. We have to make it easier. We have to do Innovative things like figure out how to bring the AI to the data, how to bring the AI to where the clients need it and make it much easier and accessible for them to take advantage of. >> And I think there's an additional point to make on this one, which is it's not just easy and accessible but it's also unified. I mean, one of the challenges that customers face in this multicloud environment and many customers are multicloud, you know, not necessarily by intent but just because of how, you know, businesses have adopted as a service. But to then have all of that experience be fragmented and have different tools not just of data, but different pools of, again catalog, different pools of data science it's extremely complex to manage. So I think one of the powerful things that we're doing here, is we're kind of bringing those multiple clouds together, into more of an integrated or a unified, you know window into the client's data in AI state. So not only does the end-user not have to worry about you know, the technologies of dealing with multiple individual clouds, but also, you know it all comes together in one place. So it can be give managed in a more unified way so that assets can be shared across, and it becomes more of a unified approach. The way I like to think of it is, you know, it's true hybrid multicloud, in that it is all connected as opposed to multi-cloud, but it's pools of multiple clouds, one cloud at a time. >> So it can we stay on that for a second because it's, you're saying it's unified but the data stays where it is. The data is distributed by nature. So it's unified logically, but it's decentralized. Is that, am I getting that right? Correct. Okay. Correct. All right. I'm really interested in how you do this. And maybe we can talk about maybe the approach that you take for some of your offerings and maybe get specific on that. So maybe Stephanie, why don't you start, you know, Yes so, what do you have in your basket? Like Cloud Pak So what we have in our basket I mean lets talk about that. >> We have, so Cloud Pak for Data as a Service. This is our premier data and AI platform. It's offered as a service, its fully managed, and there's roughly, there's 30 services integrated services in our services catalog and growing. So we have services to help you through the entire AI life cycle from preparing your data, which is Maia was saying it's very, very, very important. It's critical to any successful AI project. From building your models, from running the models and then monitoring them to make sure that as I was saying before, you can trust them. You don't have to make sure that, you need to make sure that there's not biased. You need to be able to manage these models and then the life cycle them retrain them if needed. So our platform handles all of that. It's hosted on IBM Cloud. And what we're doing now, which is really exciting, is we're going to use, and you mentioned before IBM Cloud Satellite, as a way for us to send our AI to data that perhaps is located on another cloud or another environment. So how this would work is that the services that are integrated with Cloud Pak for Data as a Service they'll be able to use satellite locations to send their AI workloads, to run next to the data. And this means that the data doesn't need to be moved. You don't have to worry about high egress charges. You can see, you can reduce latency and see much stronger performance by running these AI workloads where it counts. We're really excited to to add this capability to our platform. Because, you know, we spent a lot of time talking about earlier all of these challenges that our clients have and this is going to make a big difference in helping them overcome them. Okay. So Daniel, how to Containers fit in? I mean, obviously the Red Hat acquisition was so strategic. We're seeing the real, the ascendancy of OpenShift in particular. Talk about Containers and where it fits into the IBM Cloud Satellite strategy. >> Yeah. So a lot of this builds on top of how we run our cloud business today. Today the vast majority of the services that are available in IBM cloud catalog, actually runs as Containers, runs in a Kubernetes based environment and runs on top of the services that we provide to our customers. So the Container Platform that we provide to our customers is the same one that we're using to run our own cloud services. And those are underpinned with Containers, Kubernetes, and OpenShift. And IBM cloud satellite, based on the way that the designed our Container Platform using Kubernetes and Containers and OpenShift, allows us to take that same design and the same principles and extended outside of our data centers with user provided infrastructure. And this, this goes back to what Stephanie was saying is a satellite location. So using that technology, that same technology and the fact that we've already containerized many of our services and run them on our own platform, we are now distributing our platform outside of IBM Cloud Data Centers using satellite locations and making those available for our cloud service teams, to make their services available in those locations. >> I see and Maia, this, it is as a service. It's a OPEX. Is that right? Absolutely Okay. Absolutely >> Yeah, it's with the two different options on how we can run. One is we can leverage IBM Cloud Satellite and reach into a customer's operating environment. They provide the infrastructure, but we've provide the As-a-Service experience for the Container on up. The other option that we have is for some of our capabilities like our data science capability, where, you know customer might need something a little bit more turnkey because it's, you know, more of a business person or somebody in the CTO's office consuming the As-a-Service. We'll also offer select workloads in an IBM own satellite and environment. I, you know, so that it kind of soup to nuts managed by us. But that is the key is that other than, you know providing the operating environment and then connecting what we do to, you know, their data sources, really the rest is up to us. We're responsible for, you know everything that you would expect in an As-a-Service environment. That things are running, that they're updated, that they're secure, that they're compliant, that's all part of our responsibility. >> Yeah. So a lot of options for customers and it's kind of the way they want to consume. Let's talk about the business impact. You know, you guys, IBM, very consultative selling, you know, tight relationships with customers. What's the business case look like when you go into a client? What's the conversation like? What's possible? What can you share? Stephanie, can you maybe start things off there? Any examples, use-cases, business case, help us understand the metrics. >> Yeah. I mean, so let's talk about a couple of use cases here. So let's say I'm an investment firm, and I'm using data points from all kinds of data sources right? To use AI, to create models to inform my investment decisions. So I'm going to be using, I may be using data sources you know, like regulatory filings, newspaper articles that are pretty standard. I may also be using things like satellite data that monitors parking lots or maybe even weather data, weather forecast data. And all of this data is coming together and being, it needs to be used for models to predict, you know when to buy, sell, trade, however, due to costs, due to just availability of the data they may be located on completely different clouds. You know, and we know that especially capital markets things are fast, fast, fast. So I need to bring my AI to my data, and need to do it quickly so that I can build these models where the data resides, and then be able to make my investment decisions, very fast. And these models get updated often because conditions change, markets change. And this is one way to provide a unified set of AI tools that my data scientists can use. We don't have to be trained on I'm told depending on what cloud the data is stored on. And they can actually build these models much faster and even cheaper. If you would take into egress charges into consideration, you know, moving all the all this data around. Another use case that we're seeing is you know, something like let's say, a multinational telecommunications company that has locations in multiple countries and maybe they want to reduce their customer churn. So they have say customer data that it's stored in different countries and different countries may have different regulations, or the company may have policies that, that data can't be moved out to those country. So what can we do? Again, what we can do is we can send our AI to this data. We can make a customer churn prediction model, that when my customer service representative is on the phone with a customer, and put their information, and see how likely they are to stop using my service and tailor my phone interaction and the offers that I would offer them as this customer service representative to them. If there's a high likelihood that they're going to churn I will probably sweeten the deal. And I can do all that while I'm being fast, right. Because we know that these interactions need to happen quickly. But also while complying with whatever policies or even regulations that are in place for my multinational company. So you know, if you think back to the use cases that I was just talking about you know, latency, performance, reducing costs and also being able to comply with any policy or regulations that our customers might have are really, are really the key pieces of the use cases that we've been seeing. >> Yeah. So Maia there's a theme here. I bring five megabytes of code to a petabyte of data kind of thing. And so Stephanie was talking about speed. There's a an inherent compliance and governance piece. It's it sounds like it's not a bolt on, it's not an afterthought, it's fundamental. So maybe you could add to the conversation, just specifically interested in, you know, what should a client expect? I mean, you're putting data in the hands of you know domain experts in the line of business. There's a self-serve component here, presumably. So there's cross selling is what I heard in some of what Stephanie was just talking about. So it was revenue, there's cost cutting, there's risk reduction, that I'm seeing the business case form. What can you add? >> Yeah, absolutely. I think that the only other thing I would add, is going back to the conversation that we had about, Oh you know, a lot of this is being driven by, you know the digitization of business and you know even moreso this year. You know, at the end of the day there's a lot of costs benefits to leveraging and As-a-Service model, you know, to leveraging that experience in economies of scale from a service provider, as well as, you know leveraging satellite kind of takes that to the next level of, you know, reducing some other costs. But I always go back to, you know at the end of the day, this is about customer experience. It's about revenue creation, and it's about, you know, creating, you know enhanced customer satisfaction and loyalty. So there's a top-line benefits here, you know, of having the best possible AI, you know plugging that into the customer experience, the application where that application resides. So it's not just about where the data resides. You can also put it on the other side and say, you know, we're bringing the AI, we're bringing the machine learning model to the application so that the experiences at excellent the application is responsive there's less latency and that can help clients then leverage AI to create those revenue benefits, you know, of having the the satisfied customer and of having the, you know the right decision at the right time in order to, you know propel them to, to spend and spend more. >> So Daniel bring us home. I mean, there's a lot of engineering going on here. There's the technology, the people in the process if I'm a client, I'm going to say, okay, I'm going to rely on IBM R&D to cut my labor costs, to drive automation, to help me, you know, automate governance and reduce my risks, you know, take care of the technology. You know, I'll focus my efforts on my process, my people but it's a journey. So how do you see that shaping out in the next, you know several years or, or the coming decade, bring us home. >> Yeah. I mean what we're seeing here is that there's a realization that customers have highly skilled individuals. And we're not saying that these highly skilled individuals couldn't run and operate these platforms and the software themselves, they absolutely could. In some cases, maybe they can't but in many cases they could. But we're also talking about these are they're highly skilled individuals that are focusing on platform and platform services and not their business. And the realization here is that companies want their best and brightest focused on their business, not the platform. If they can get that platform from another vendor that they rely on and can provide the necessary compute services, in a timely and available fashion. The other aspect of this is, people have grown to appreciate those cloud services. They like that on demand experience. And they want that in almost every aspect of what they're working on. And the problem is, sometimes you have to have that experience in localities that are remote. They're very difficult. There's no cloud in some of these remote parts of the world. You might think that clouds everywhere, but it's not. It's actually in very specific locations across the world, but there are many remote locations that they want and need these services from the cloud that they can get. Something like IBM Cloud Satellite. That is what we're pursuing here, is being able to bring that cloud experience into these remote locations where you can't get it today. And that's where you can run your AI workloads. You don't have to run it yourself, we will run it and you can put it in those remote locations. And remote locations don't actually have to be like in the middle of a jungle, they could be in your, on your plant floor or within a port that you have across the world, right? It could be in a warehouse. I mean, there's lots of areas where there's data that needs to be processed quickly, and you want to have that cloud experience, that usage pay model for that processing. And that's exactly what we're trying to achieve with IBM Cloud Satellite and what we're trying to achieve with the IBM Cloud Pak for Data as a Service as well. Running on satellite is to give you those cloud experiences. Those services managed as a service in those remote locations that you absolutely need them and want them. >> Well, you guys are making a lot of progress in the next decade is not going to look like the last decade. I can pretty confident in that prediction. Guys thanks so much for coming on the cube and sharing your insights, really great conversation. >> Absolutely. Thank you, Dave. >> Thank you. >> You're welcome, and thank you for watching everybody. This is Dave Vellante from the cube. We'll see you next time. (upbeat music)

>> live from Miami Beach, Florida It's the que covering demon 2019. Brought to you, by the way. >> Welcome back to Miami. Everybody watching the Cube, The leader in live tech coverage. This is Day two of the mon 2019 3 cubes. Third year at V mon, We did New Orleans. We did Chicago last year. Course here at the Fountain Blue in Miami. Great venue for an event like this. I'm Dave a lot. It was my co host, Peter Burroughs. Abba Dabbas. Eye is Adi is here. He's the head of a Charles Russell speech. Liza London based law firm. How about great. Great to see you. Thanks for coming on. Thankyou. So you tell us about this judge. Interesting name. Charles Russell. Speech lease. It was a merger of two firms, Right. Tell us how it all came about. >> Back in 2,014 Charles, loss of species performed for a merger between two different companies. Charles docile and speaks Lee Burcham from a 90 perspective. That was very interesting for the two departments coming together s So we have a limited time period where we had to merge these two companies Two different systems different data centers, different data sets. So it was formed by emerging back in 2,014 for five years on way here today >> that we see this a lot, you know, Emanate goes down. The acquiring company of this sounds like it was a merger. You know, they sort of battle. Okay, who's going toe? Really? Which framework is going to win? Because I'm sure had that conversation. But so to take us through that merger, what it entailed what? What the scenario looked like and how you plan for it. Sure. >> So I was part of the Charles. Also legacy Charles Russell team on, then obviously speaks about. Some had their own team as well. So initially, when we first found out about the merger, it was essential for the two teams to get together to work out. Okay, What systems? You have free mail. What systems you have for document management system playing trump cards. Which is who's got the best system and which way do we wantto move forward? A little. >> Ah, >> so but being a law firm, most law firms around the world and in the UK especially used the same types of software so essentially that from that perspective it was It was it was quite simple. But then way had to work out. How do we How do we go forward with this? Because two different headquarters in the London area. Which office do we move into? Sort of logistics around that. Can we fit in pre merger? It was six. Charles Lawson had sickle. Roughly 600 people, especially birds, had roughly 500 people. So pretty comparable. Yeah, yeah. So working out space logistics was was an issues >> making that even even more complicated, right? Yeah. >> One of the things that's interesting about a law firm, like versus a traditional manufacturer or AW financial services firm that has a lot of very fast right writing systems and have to scale on those lines is a law firms feature very complex dogs, very complex in from out of files, a lot of files that are written. But at the same time, you have to be repurposed to a lot of different work flows very sensitive to external contingent regulatory change. And so you have all of that happening, especially, I mean, two years ago from now on MySpace steak, and it was you're getting into brexit stuff, too, so that also had to be a source of uncertainty. So how has it been combining external regulatory issues the way that technology is being used in law firms and some of the new work clothes that you guys trying to support? And then adding, On top of that, the complexity of bringing these two firm GPR >> GPO itself was It was a year old project for us on. Obviously, we've got offices. The Middle East, but obviously is in the Far East on DH in Central Europe has well, so data logistics or where it sits, is an issue for us as well. So GDP, ours being a big project for us in terms of the merger itself. It was it was very, very difficult for the two I T departments to come together on actually work out. How how do we go to one unified systems? Essentially one doctor man, just in one email system. All of that took a lot of plan in law project management on essentially within the legal press itself. We got doubted in the time frames that we had that we can achieve it on within. I think It was 18 month period. We had merged order, different systems and various offices because speech the Bertram and Time is what I had. Offices in Zurich and Geneva were to merge with different offices together as well. So it was. It was a big, big task for the i T department on the firm itself. >> They're very tight migration deadlines. And and as you started to approach those deadlines you had to worry about, Okay, When we're going to cut over, how do we avoid downtime? How do we make sure that we don't? You know, I have bad data, data, corruption and the like. So how did you plan for that? And how did it go? >> So wait, we're here. C'mon on DH. Veen was It was it was a big part of our migration process. So where we had two different parts of the business Different storage systems, Different actualization system's way used to mean a CZ. The middleman basically, to my great data, from one day to center to another, using swink it. So where there was a large amount of terabytes and terabytes, amount of data way had swing kit available to us using team were able to be to be essentially a love the environments into the swing care and then bring them over to the other side of the business. And vain was essentially part on on top of that, making sure that the data that we were coming that will bring in a cross is true and not corrupt on DH, that using some of their technology is sure backups and stuff like that really, really was essential to, you know, do migration going well >> And was was Wien installed and both organizations at the time? Or was that something that you had to sort of redeploy? >> And yeah, So Legacy Charles also had way was actually myself going back probably eight years ago. Version For a time, I think team had 20,000 customers. So to here >> there were version 10 now 33 150 >> 1,001,000, 4,000 month. >> That makes me proud that we invested in vain when we did good car. So yeah, it was It was a good call from us, and essentially three other side of the business did not have. But then we just wait. Expanded our Venus State to look at both sides and then bring him across on. And then, ever since then, we've grown our vamos state across the world, across all of officers. So >> So how did you do that? So that was that was another migration that had to occur. And did you? You kind of do those simultaneously. Did you do the theme of migration first, and then bring the two systems together? >> Do you seem to do Stouffer special sauce in the migration? >> Yeah. So Veen was essentially a tool that we used to my great data sensors from one data center to another using their backup technology using their replication technology, we were able to replicate all of one side's virtual machines to the other. And then that gave us that gave us the flexibility as well. When when we had the limited down time periods that we've had, they give us the flexibility to actually Circe the business is during these particular ours. We're not gonna be able to You're not gonna have access to these systems because we're going to bring up systems from point A to point B. So veen was essential to them if >> you had to do it over again. If he had a mulligan, what would you have done differently? What what advice might you give to somebody who's trying to go through a similar migration? >> I would say Give your partners and lawyers more realistic time. Pray the time frame that we would get. >> Or don't let them give you an unrealistic time for him. >> Exactly. Yeah, so says ensured that the amount of work it's it's not just day to itself. You know, we're talking network and we're talking security. We're talking, you know, to to similar sized companies coming together. We were very, very limited time frame, consolidating all of their systems into one which is essential for the two parts of the business to collaborate together because, you know, way could have taken our time. We could have got to take this free four years a CE, far as we're concerned. But the fact that we did do it in such a quick time for him and that business to parts of the business from Day one can collaborate much better with each other. So >> we talked a lot about digital business transformation and you know, our approach or our observations on the digital business transformations, the process by which you altar and change your firm to re institutionalize the work. Change your game. Tomato Grover. All governments model as you use data as an asset, so that's affecting every firm everywhere. How's it affecting a law firm and you know your law from specifically on? How is that going to change your stance in your approach to data protection >> Data is incredibly important to unlawful. A zit is to most most organizations, but in terms of, you know, one of one of the things that's quite important in terms of law firms. We work with the financial institutions, so we held information by that. We hold personal data way hold all times of information. Charles Oscar speech leads works with Aware is of law apart from Kunal. So the areas of law that they worked with his vast in terms of the amount of data that we hold and essentially I mean, for us data is the most important thing that runs the firm and having visibility tow our data. How do we How do we work that data? How do we then market based on the data that we have? How do we market ourselves from that data. You know, there might be one area the business that's dealing with a family issue, family law. But then, you know that that could correspond with the litigation issue. You know, how do we work that data? To be to be an advancing to our businesses is extremely important. For >> what? What do you think of the announcements this week? I'm kind of curious. I was liketo ask the practitioners of what they think about. You know what was announced. You had, uh, well, you had the ve made $1,000,000,000. That's kind of fun and cool, but But you had the with the program, which was kind of interesting. The whole ap I look the beam availability orchestrator, where they're really talking about recovering from backups as a host that needed to recover from, you know, a replicated instance. You know, some of the automated testing stuff was kind of interesting. They talked about dynamic documentation, things you saw this week that you'll actually go back and say, Hey, I can apply that to solve a problem. Sure. >> So, essentially, I think I've been a really good question is very relevant to us many of not just ourselves law firm but many of the other law firms around the world are now looking at cloud based services now for us. I mean, this was a big thing five years ago way you know, everyone was talking about public clouds. Us. We're now we're now looking clouds and where basically, we've bean pushed by the vendors themselves to go towards cloudlike Citrix, for example. Their licensing model was based around their services. So is Microsoft in Mike's off? You don't you don't really have, you know, exchange anymore. Within premises you have off 365 A lot of the SAS applications are moving toward the cloud on DH. What wrote me? I had to say doing the keynote in regards to act, too. And how team are trying to be the visionaries in terms of look at that cloud is their next big thing for the next 10 years, offering often a crucial and for businesses like ours who have limited exposure to cloud technologies limited understanding, essentially having a tool that could migrate from one cloud to another. It's fantastic, you know, we've offered, you know I've spoken to, obviously are United directors around the other law firms where I wanted to have gone to the public cloud. But they don't know how to come back in and having a tall that essentially gives you that flexibility to bring it back in house to go form a ws to zoo. Or if there's a particular assess application, for example, that piers better with a W s. But you've got your other application that piers with that particular application is your Why would you want to have in the door? You'll probably want to move into a W eso for us, I think. What? The message coming out of'em on this year has bean really, really helpful for us. >> So So when you started with theme, they had it said 20,000 custom You like the 20001st customer on DIT was coincided with the virtual ization, you know, craze. Do you feel like the team knowing what you know about them, you have a lot of experience with them Consort of Replicate that success in this town intendant and in Act two, >> I think when I first looked at them, Wow, this is really, really simple. It's a bit like an iPhone. You know you given iPhone to your grandmother or to your children, and they have to play with it. And I see the beam as an intuitive piece of software that easy fighting professionals to get on with it, as their slogan said a few years ago. It just works. It does just work. Wear were great advocates of him. It's worked wonders for us. We've acquired smaller businesses using we've managed companies using and when I see you know, when you go to the sessions and you see the intelligence behind their thinking, I think going back to your question I think Wei si oui, si, vamos a strategic partner for us when we see their vision and we believe in their vision, and I think what they're doing in terms of what they working on next few years, I think we're well favor there, and I think, you know, essentially, that's where the most of their business is going to come from, >> where you sit down with, you know, rat mayor over over vodka and he says, Tell me the one thing I could do to make your life you know, easier, better you can't say cut prices s a hellhole. But what would you advise him to >> make my life better >> other than Jim instead of >> yeah, eyes that >> would make you crazy. >> So in terms of a zoo, a technology, >> your business relationship or something, she'd like to see them do that would. I >> think in terms of mergers and acquiring companies, seen license rentals will be a good thing. I know, I know. They give you a valuation license keys, and that's something that you can use. So, for example, if we were to acquire a company that has hundreds of servers and PM's having license rentals for a period of time, able >> to spin it up and spin it down actually allowed >> Exactly. Yeah, that would be an advantage. I think in terms of what you know what they're doing in the marketplace, and a lot of law firms use him. I feel I can't do any more than they are doing now. And in all the years that we've used to be my fingers on eight years now, but we've only had one serious problem, and the way they got that problem, you know the way, the way they communicated to reverse the way they a lot of different teams across the the Europe and the US go involved. I think, you know, in terms of service, in terms of software, in terms of what they what they do for us. I don't think there's anything more to add. Teoh. Right? Maia's vision. >> That's great for their custom of it. Well, thanks so much for coming on. The Cube is not heavy. Really? Thank you very much. You're welcome to keep it right there, buddy Peter, and I'll be back with our next guests right after this short break. We're live from Miami at the front of Blue Hotel. You're watching the Cube from Vienna on 2019 right back.