 Live from Las Vegas, it's theCUBE, covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. Welcome back to theCUBE's live coverage of ServiceNow Knowledge 18 here in Las Vegas, Nevada. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. We are joined by Dr. Mateus Egelhoff. He is the program director at Siemens AG. Thanks so much for coming on the program again. Hey, CUBE alum. Yes. Great to see you again, my friend. Great to be back. I'm going to go way back. They have a bromance brewing. I think so. Yeah, most definitely. So, Mateus, at Siemens, the Now platform is really a key pillar of your digital transformation. Why is service integration such an important element 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 traumatic 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 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 becomes more important to have the overall view of the whole value chain. I wonder if I could ask you as a seasoned ServiceNow practitioner, you've seen a lot. We were talking, kind of joking about sometimes tech company marketing is ahead of what they can actually do. ServiceNow, 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. Yeah. So we started our ServiceNow journey in January 2014. So roughly four years ago. 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 set up the platform already in the integrated way of having not single processes, single databases, but rather having a 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 an excellent tool, but the challenge is not setting up the tool. It is, as John Daneho 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 377,000 employees, this is a scale where you need to have a focus on the change topic, that they are really applying the processes 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 because 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 seem to be, 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 you knew that going in was ServiceNow kind of transparent or helpful in that or was it just going to drop off the software and give us a call if you need help? What was that experience like? Luckily we didn't knew because otherwise we would have not started. We would have known all those challenges. And therefore ServiceNow was really helpful because there is out of the box functionality that you can kickstart. 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's why we also procured licensees 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. ServiceNow learned a lot and it was good to have them on board. And you were 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 roadmap 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 the nasty portion. But that's why I made a roadmap 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 an additional process. I have additional services. Hey, this country also wants to step in. 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. Great. Because we just heard CJ Desai up there on the main stage preaching one thing. But I know before the cameras are rolling you were telling us that you're actually doing a lot with the data that you're collecting. So talk about 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 his main keynote mentioned that it's 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 ServiceNow. 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 want to then 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 dashboard to visualize is the data correct. First step, second one is then, then we are displaying the data in Tableau. So visualization layer doing the typical reports where you can slice down by division, by country, by service, by cost center, 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 the 1.3 million tickets? And the first reaction was, I don't understand because we have five 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, okay, then let's use just the English portion, 700,000 tickets. And I 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 two messages are important. You really have to learn how to leverage the 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 Azure. So we gave, or the thesis was, hey, Azure, can you tell us how to increase customer satisfaction? And again, we gave Azure with some nice medical 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 a ticket was picked up. But we got really time stamps, so we can also now adopt our SLAs 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 a 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 a fleck 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 service now user. So if I understand it correctly, you're extracting data out of service now. I think you mentioned off camera, you bring some of that data into SAP HANA. You mentioned Watson, Tableau is the Viz. And you said Microsoft Azure 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. There's not one out-of-the-box solution, is there? No, no, and that's why we are now applying different technology to really learn what is in for us and quickly do some 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 all this in the platform for you? And is that what you're pushing him to do? I think the ratio will get higher and higher. What ServiceNow will be capable to do, like the prediction of tickets and the automated routing, that should be native 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 leaders. 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 going to be interesting to see what kind of merger and acquisition activity ServiceNow does. Certainly Mike Scarpelli and John Donahueho in the financial analyst meeting were hinting of acquisitions. You would imagine, they've done some in AI. You would expect they'd do others. I wonder if we could ask you about the climate in Germany with regard to machines replacing humans in 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 Azure, that is a core only artificial intelligence can do. 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 resolver groups' applications, they already reduced the ticket number. They reduced the cycle time. The number of, or 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'm saying, yes, it will reduce some of the jobs, but hopefully the nasty, more administrative work. And on the other hand, it will create new opportunities, especially in the integration layer where you need human intelligence and people who can act on and keep the ecosystem alive. That is nothing a machine can do. Mateus, 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 theCUBE's live coverage coming up just after this.