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