>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. You're watching the cube, the leader in live tech coverage, and we're covering the IBM data and AI forum in the port of Miami. Mateus Fuka is here, he's the director of offering management for hybrid data management. Everything data. But see, it's great to see you. It's great to have you. So be here with you. We're going to talk database, we're gonna talk data warehouse, everything. Data, you know, did the database market, you know, 10 years ago, 12 years, it was kind of boring. Right. And now it's like data's everywhere. Database is exploding. What's your point of view on what's going on in the marketplace? You know, I mean it's funny too. You used to it boring because I think it's the boring stuff that really matters nowadays to get, get things to where you get people to value with the solutions you want to be or the modernization. >>Thea. Yeah. Seeking to do on the data estates. Um, the challenge that you have in embracing multi-cloud data architectures. So to get, to get to, well you have to, do, I have to take care of the boring stuff. How real is multi-cloud? I mean, I know multi-cloud is, is real and that everybody has multiple clouds. But is multi-cloud a strategy or is it a sort of a symptom of multi-vendor and it just, we could have ended up here with the shadow it and everything else. >> I think it's a reality and yes, it should be a strategy, but I think more more clients and not they find themselves being exposed to this as a reality with different lines of businesses, acquiring data, um, estates running on different locations, different clouds, you know, and then companies have challenge if you want to bring it all together and actually the value of that data, um, and make it available for analytics or AI solutions. >>You know, you've got to have a strategy. >> So IBM is one of the few companies that has both a cloud and an aggressive multi-cloud strategy. Um, you know, Amazon's got outpost a little bit here and Microsoft I guess has some stuff, uh, a but, but generally speaking, uh, Oracle has got a little bit here but IBM has both a cloud. So you'd love people to come into your cloud, but you recognize not everybody's gonna come in your club. So you have an aggressive multi-cloud strategy. Why is that? What's the underpinning of that strategy? Is it openness? Is it just market, you know, total available market? Why? So first of all, yes, we have a, we have a strong portfolio on IBM cloud and we think, you know, it's the best in terms of, you know, integration with other cloud services, the performance you get on the different data services. >>But we also have a strategy that says we want to be our clients want to go. And many clients might have committed already on a strategic level to a different cloud, whether that's AWS, you know, why IBM cloud or Asia. And so we want to be ready as clients want to go. And our commitment is to offer them a complete portfolio of data services that support different workloads. And a complete portfolio in terms of, um, your, the IBM, uh, hope heavy set of technologies as well as open source technologies, give clients choice but then make them available across that universe of multicloud hybrid cloud on premise in a way that they get a consistent experience. And you know, I mean you are familiar with the term. Oh, you divide and conquer, right? I like to talk about it as uh, you know, um, unify to conquer. >>So our, our mission is really unified experience and unified the access to different capabilities available across multicloud architects. So is that really the brand promise gotta unify across clouds? Absolutely. That's our mission. And what's the experience like today and what is sort of the optimal outcome that you guys are looking for? Uh, being able to run any database on any cloud anywhere. Describe that. >> So I think, um, you'd be talking about chapter one and two off the cloud, right? When it, when it comes to chapter one in my, in my view, chapter one was very much about attracting people to the cloud by offering them a set of managed services that take away the management headaches and you know, the, the infrastructure, uh, management aspects. Um, but when you think about chapter two, when you think about how to run, uh, mission critical workloads on, on a cloud or on premise, um, you know, you want to have the ability to integrate data States that run in different environments and we think that OpenShift is leveling the playing field by avoiding location, by, by giving clients the ability to basically abstract from PI, Teri cloud infrastructure services and mechanisms. >>And that gives them freedom of action. They can, they can deploy a certain workload in one in one place and then decide six months later that they are better off moving that workload somewhere else. Yes. >> So OpenShift is the linchpin, absolutely. That cross-cloud integration, is that right? Correct. And with the advent of the rise of the operator, I think you see, you know, you see, um, the industry closing the gap between the value proposition of a fully managed service and what a client managed open shift based environment can deliver in terms of automation, simplicity and annual Oh value. Let's talk about the database market and you're trying to, what's happening? You've got, you know, transactional database, you've got analytic database, you've got legacy data warehouses, you've got new, emerging, emerging, you know, databases that are handling unstructured data. You got to know sequel, not only sequel lay out the landscape and where, what's IBM strategy in the database space? >>So our strategy has, has, so starting with the DB to family, right? We have introduced about two one, two years ago we introduced somebody called Tacoma sequel engine. That gives you a consistent, um, experience in from an application and user perspective in the way you consume, um, data for different workload types. Whether that's transactional data, um, analytical use cases, speak data overdue or fast data solution events, different data architectures, everything, you know, with a consistent experience from a management perspective, from a, from a working behavior perspective in the way you interact with, with this as an application. And, and not only that, but also then make that available on premises in the cloud, fully managed or now open shift based on any cloud. Right. So our, our, I would say our commitment right now is very much focusing on leveraging OpenShift, leveraging cloud pick for data as a platform to deliver all these capabilities DB to an open source in a unified and consistent. >>Uh, I would say with a unified and consistent experience on anybody's cloud, it's like what's in any bag was first, you know, like six months ago when we announced it. And I think now for us doing the same with data and making that data, make it easy for people to access state our way every to the sides is really, but Ts, what's IBM's point of view on, on the degree of integration that you have to have in that stack from hardware and software. So people, some people would argue, well you have to have the same control plane, same data plane, same hardware, same software, same database on prem as you have in the cloud. What's your thoughts on that degree of homogeneity that's required to succeed? So I think it's certainly something that, uh, companies strive to get to simplify the data architectures, unify, consolidate, reduced the number of data sources that you have to deal with. >>But the reality is that the average enterprise client has 168 different data services they have to incorporate, right? So to me it's a moving target and while you want to consolidate, you will never fully get there. So I think our approach is we want to give to client choice best different choice in terms of technologies for for the same workload type. Potentially, whether it's a post test for four transactional workloads for TB, two for transactional workloads, whatever fits the bill, right? And then at the same time, um, at the same time abstract or unify on top of that by, by when you think about operators and OpenShift, for instance, we invest in a, in um, in operators leveraging a consistent framework that basically provides, you know, homogeneous set of interfaces by which people could deploy and life cycle manager Postgres instance or DB two instance. >>So you need only one skillset to manage all these different data services and you know, it reduces total cost of ownership is it provides more agility and, and you know, you know, accelerates time to value for this client. So you're saying that IBM strategy recognizes the heterogeneity within the client base, right? Um, you're not taking, even though you might have a box somewhere in the portfolio, but you're not a, you need this box only strategy. The God box. This is, this is the hammer and every opportunity is a nail. Yeah, we have way beyond that. So we, we are much more open in the way we embrace open source and we bring open source technologies to our enterprise clients and we invest in integration of these different technologies so they can, the value of those can be actuated much more in a much more straightforward fashion. >>The thing about cloud pay for data and the ability to access data insights in different open Sozo, different depositories, IBM, one third party, but then make that data accessible through data virtualization or full governance, applying governance to the data so that data scientists can actually get reef that data for, for his work. That is really important. Can you argue that that's less lock-in than say like they say the God box approach or the cloud only approach? Yeah, absolutely. Because how so? How so? Because, well, because we give you choice to begin with, right? And it's not only choice in terms of the data services and the different technologies that are available, but also in terms of the location where you deploy these data services and how you run them. Okay. So to me it's all about exit strategies. If I go down a path and a path doesn't work for me, how do I get out? >>Exactly. Um, is that a concern of customers in terms of risk management? Yeah. I think, look, every, every costume out there, I daresay, you know, has a data strategy and every customer needs to make some decisions. But you know, there's only so much information you have today to make that decision. But as you learn more, your decision might change six months down the road. And you know, how to preserve that agility as a business to do course corrections I think is really important. So, okay, a hypothetical that this happens every day. You've got a big portfolio companies, they've done a lot of M and a, they've got, you know, 10 different databases that they're running. They got different clouds that they're using, they've got different development teams using, using different tooling, certainly different physical infrastructure. And they really haven't had a strategy to bring it all together. >>Uh, you're hired as the, uh, the data architect or the CTO of the company and say, but Tia's, the CEO says, fix this problem. You're not, we're not taking advantage, uh, and leveraging our data. Where do you start? So of course, being IBM, I would recommend to start with clapping for data as the number one data platform out there because eventually every component will want to capitalize on the value that the data represents. It's not just about a data layer is not just about a database, it's about an indicated solutions tech that gets people to do analytics over the data, the life insights from the data. That's number one. But even if you are, you know, if, if, if it's not I the IBM stack, right, I would always recommend to the client to think about a strategy that that allows for the flexibility change to change course wide and move workloads from one location to another or move data from one technology stack to another. >>And I think that that kind of, you know, that agility and flexibility and, um, translate into, um, risk mitigation strategies that every client should think about. So cloud pack for data, it's okay, let's start there. I'm gonna, I'm gonna, I'm gonna install that, or I'm gonna access that in, into the cloud. And then what do I have to do as a customer to take advantage of that? Do I just have to point it at my data stores? What are the prerequisites? Yeah. So let's say you deploy that on IBM cloud, right? Then you have, you usually are invested already. So you have data, large data estates either residing on share is already in the cloud. You can pull those, those, those datasets in remotely without really moving the workload of the data sets into a cloud pixel, data managed environment by using technologies like data virtualization, right? >>Or using technologies like data stage and ETL capabilities, um, to access the data. But you can also, as you modernize and you build your next next generation application, you can do that within that managed environment with OpenShift. And, and that's what most people want to do. They want to do a digital transformation. They want to modernize the workloads, but we want to leverage the existing investments that they have been making over the last decade. Okay. So, but there's a discovery phase, right, where you bring in cloud pack for data to say, okay, what do I have? Yup, go find it. And then it's bringing in necessary tooling on the, on the diff with the development side with things like OpenShift and then, and then what it's magically virtualizes my data is that, so just on that point, I think you know, the, what made us much more going forward for his clients is how they can incorporate different datasets with adding insure in open source technologies or, or DB two on a third party vendor said, I don't want to mention right now, but, but what matters more is, so how do I make data accessible? >>How do I discover the data set in a way that I can automatically generate metadata? So I have a business glossary, I have metadata and I understand various data sets. Lyft, that's their vision objective business technology objectives. To be able to do that and to what's watching knowledge catalog, which is part of topic for data is a core component that helps you with dead auto discover the metadata generation basically generating, okay, adding data sets in a way that they are now visible to the data scientists and the ultimate end user. What really matters and I think what is our vision overall is the ability to service the ultimate end user medicine developer, a data scientist, so business analysts so that they can get a chip done without depending on it. Yeah, so that metadata catalog is part of the secret sauce that'll that that allows the system to know what data lives, where, how to get to it and and how to join it. >>Since one of the core elements of that, of that integrated platform and solution state board. What I think is really key here is the effort we spend in integrating these different components so that it is, it is, it looks seamlessly, it is happening in an automated fashion that as much as possible and it delivers on that promise of a self service experience for that person that sits at the very end of that. Oh, if that chain right, but to your sex so much for explaining that QA for coming on the cube. Great to meet you. All right. Keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the cube from the IBM data and AI forum in Miami. We'll be right back.

live from Las Vegas it's the cube covering service now knowledge 2018 brought to you by service now welcome back to the cubes live coverage of service now knowledge 18 here and Las Vegas Nevada I'm your host - Rebecca night along with my co-host Dave Volante we are joined by dr. Mateus Egelhoff he is the program director at Siemens AG thanks so much for coming on the problem yes great to see you again my friend veteran these two go way back they have a bromance brewing so Mateus at Siemens the now platform is really a key pillar of your digital transformation why is service integration so so it's such an important element of your vision of your strategy because service integration is really the place to be in the former days we concentrated to manage one service one provider but if you really want to integrate and be responsible end-to-end you really have to own the whole chain from the demand side to the supply side so you really have to span the whole value chain from the customer to the provider and back from the provider to the customer that's why it is so important to play the integrator role because if you own that whole value chain end-to-end you can optimize the value chain and also do some dramatic changes in that value change to kick out some of the providers that do not really add high value or you can optimize costs by combining some of the steps and that's why service integration is so key because then you have the whole end-to-end view and you gain the whole inside of that value chain and also the net the next topic I want to add is the typical service management topic is also changing over time because what to do with for example Microsoft Exchange Online you don't have to do much management on that one because that is used by millions of users so what to do actually and that's why it comes more important to have the overall view of the whole venue changer what if I could ask you as a seasoned ServiceNow practitioner you've seen a lot we were talking just kind of joking about sometimes tech company marketing is ahead of you know what they I can actually do service now obviously tremendous platform that makes it sound easy but it takes a lot of work to get there but once you get there you get a flywheel effect and you can add more and more because of the platform so talk a little bit about kind of where you started and how long it really took you to get to a point where you could really start driving major value for your organization so we we started our ServiceNow journey in January 2014 so roughly four years ago yeah and we started with the typical incident problem change service request portion but my goal was from the beginning to really have a high degree of automation and integration in that platform that's why we we set up the platform already in the integrated way of having not single processes single databases but rather having single source of record in the system and when we started of course we thought hey it's a great technology and it is a great technology it's a excellent tool but the challenge is not setting up the tool it is as Sean Donahoe said it's the change in the organization because by implementing such a huge tool with one process having it completely across all organizations in 149 countries with three hundred seventy seven thousand employees this is a scale where you need to have a focus on the change topic that they are really applying the process is because otherwise it's not of usage and this had a big impact on how we are providing the services because ServiceNow is more or less the window where it gets obvious how your services are looking like so it's not only about setting up ServiceNow you have to change the processes you have to change the organization you might simplify also the services they are quite a little bit too complicated to be handled in the portal and all that work has to be done in parallel and I always use the phrase there the dark side is coming up of an organization and I'm pretty sure each organization has a dark side of legacy system gaps in the process steps the data is not correct the data is not validated it is not one scene DP and all that stuff has to be pulled away connected otherwise you don't have the end-to-end chain you don't have the degree of automation that you want to leverage and this roughly took us two and a half years and and you knew that going in with ServiceNow kind of transparent or helpful in that or was it just gonna drop off the software and give us a call if you need help exactly we didn't you because otherwise we would have not started all those challenges and therefore ServiceNow was really helpful because there is out-of-the-box functionality that you can kick-start however if you want to leverage ServiceNow in that environment the out of box functionality is nice and a good starting point but you have to add some of the functionality like the integration layer is not there like data analytics not there yet so you have to add some of the topics but therefore it is good that ServiceNow was there that that's why we also procured licenses but on the other hand we engaged also professional services because we also wanted to make ServiceNow responsible for the implementation that this is really a lighthouse project also for ServiceNow and of course for us so it was a win-win so Evans now learned a lot and it was good to have them onboard and you're able to show quick enough value to get credibility in the organization to really fulfill your vision exactly so what we basically did we set up a road map based on savings because it's always easy to introduce a new tool a new portal a new process whatever always nice but when it comes to shutting down existing ones this is the difficult and nasty personnel but that's why I made a road map of clearly showing hey now we can shut down this portal now we can shut down this legacy tool and based on that the savings kicked in and the people really saw hey it works hey we really can shut down and get rid of some of the legacy dark side topic and then typically to a platform then the platform momentum starts where everybody wants to get on hey I have an additional provider I have initiative process I have additional services hey this country also wants to set em then the platform starts to grow and gain some momentum so that everybody gets up and this is also challenging then regarding the release how to handle all those demands I want to talk about data and because we just heard CJ Desai up there on the main stage preaching one thing but I know before the cameras are rolling yours you were telling us that you're actually doing a lot with the data that you're collecting so so talk about stop what it is you're doing it's because the collecting the data is the easy part in a lot of ways it's then figuring out okay what is the data telling us and then what do we do about it exactly so CJ in this main keynote mentioned that is not a good idea to pull out all the data outside of ServiceNow I'm agreeing but unfortunately only in two years or three years time when the intelligence is in service now that's why Siemens has decided to pull out really on a daily basis all the data from ServiceNow into a separate SQL database and then a first important step starts the qualification of the data is the data quality correct because the high degree of automation only works if the data is correct and of course if you wanted and display the data and do the analytics it's also key that the data is correct that's why we have established a data health - want to visualize is the data correct first step second one is then then we are displaying the data in tableau so with visualization layer doing the typical reports where you can slice down by division by country by service by cost cent or whatever the typical reporting but we are also doing that data and feeding it into for example Watson so we used Watson to see how intelligent he is so we gave Watson 1.3 million tickets and said hey Watson tell us what is exciting about 1.3 million tickets and that the first reaction was I don't understand because we have 5 languages a mix of languages Portuguese using Portuguese and English German and English and then Watson had some issues with understanding the tickets then we said ok then let's use just English portion 700,000 tickets and said hey Watson tell us now and he said issue ticket problems complained and whatnot and then I thought hey Watson you are telling me that those are tickets that is not the expectation I had based on what the Watson team is telling but to be fair to Watson that's not my point that I'm saying Watson is stupid I'm just saying 2 messages are important you really have to learn how to leverage that new technology and it really takes time so prepare your organization to apply those technology because also your organization needs a learning curve to apply that technology and the second example was with Asia so we gave or that the thesis was hey Asia can you tell us how to increase customer satisfaction and again we gave Asia with some nice mathematical formulas a lot of tickets and based on that model we learned what are the key success factors of satisfying a customer so it's of course how many times a ticket was routed how fast the ticket was picked up but we got really timestamps so we can also now adopt our SLA is to the providers to more satisfy the users and more excitingly based on four criterias we can now predict the satisfaction of the user so we can really say with 86% will that be rating between one and three what is not that good and if so this is now the next step we will feed that back into service now giving that ticket Aflac so the service desk agent can act on it and I think that is the exciting one not only collecting data learning out of it and then acting on it and now based on if a ticket is open we already can predict the customer satisfaction that is great providing guidance to the ServiceNow user so if I understand it correctly you're extracting data out of ServiceNow I think you've mentioned off-camera you bring some of that data into si P Hana yeah you mentioned your Watson tableau is the viz and you said Microsoft Azure exactly as well so like many big data problems you're solving it with a variety of tools that's challenging but you really have no choice is not one out-of-the-box solution is there nope well that's why we are now applying different technology to really learn what is in for us and quickly do is on POC check is it feasible is it a quick win or takes it longer or is the technology not that mature and then really follow up what is most promising is your expectation and desire that ServiceNow does sell all this in the platform for you and is that what you're pushing him to do I think the ratio which will get higher and higher what ServiceNow will be capable to do like the prediction of tickets and the route the automated routing that should be negative in ServiceNow but in regards to artificial intelligence I think there are other companies out there who are more at the front runner and really the lead us so I think it will be always a mixture out of ServiceNow but also pulling out some of the data to leverage other technology it's gonna be interesting to see what kind of merger and acquisition activity ServiceNow does certainly Mike Scarpelli and John Donahoe in the financial analysts meeting were hinting of acquisitions you would imagine they've done some in AI you would expect they do others I wonder if we could ask you about the climate in Germany with regard to machines replacing humans and cognitive functions obviously it's a very employee friendly environment what's the narrative like there what are you seeing yeah I think also big discussions in Germany about that digitalization is that disruptive to the job market and as I said with the example of Asia that is a core only artificial intelligent can do yeah no sense to use humans with a pocket calculator to do that doesn't make sense but on the other side I have also set up a team of 20 people who are doing let's say manual work they are monitoring the tickets for example three people and based on their experience and human factor to speak with the different resolve our groups applications they already reduced the ticket number they reduced the cycle time the number of the closing time was decreased by 20% so these are examples where you need humans because on the other side there are also humans and this optimization of looking at the data speaking with different people that have domain expertise this is really necessary where I see that humans are much more advanced than the machine learning so that's why I see balances of yes we are using Azure Watson and all those nice technologies but we are also ramping up people that really act on the data that they have at hand so there is less anxiety to this idea would you say exactly exactly so and that's why I am saying yes it will reduce some of the chops but hopefully the Nestea more administrative work and on the other hand it will create new opportunities especially in the integration layer where you need human intelligent and people who can act on and keep the ecosystem alive that is nothing a machine can do it is thanks so much for coming on the program it's always fun to have you on thank you we will have more from ServiceNow knowledge 18 of the cubes live coverage coming up just after this