welcome to this quick demonstration of servicenow's in-platform process mining solution called process optimization this demonstration is going to be based on the Utah release now many of you may already know that servicenow provides both in-platform reporting and analytics to help people understand what is going on with a given process now what you're looking at here in this dashboard is a dashboard is welcome to this quick demonstration of servicenow's in-platform process mining solution called process optimization this demonstration is going to be based on the Utah release I always like to start my process optimization demos inside of a dashboard like the one that you're looking at here many of you probably already familiar with the fact that servicenow provides both in-platform reporting and analytics to help stakeholders on the platform get an understanding of what is going on with the given process the example that we're looking at on the screen here is something an incident process manager might use they get information around kpis related to their process things like their backlog or nttr their first call resolution rate and the in-platform reporting analytics is super helpful in helping them understand where they stand as it relates to these kpis the targets they set for themselves and the momentum that they have towards those targets but I've been doing this for a little while and one of the things that I've learned over my years covering analytics Solutions is this is that information tends to be one of the most addictive and contagious things out there you give somebody a little bit and they always want more that's the first thing that would happen if somebody in leadership would be looking at a dashboard like this is they'd start to see that their nttr is is climbing and they'd want to understand why that's happening and this is where traditional analytics tends to fall short traditional reporting and analytics are great at answering questions about what is going on but they struggle to answer questions about why those things might be happening that's where process money comes in process mining helps us start to answer some of the wide questions behind all those kpis on our dashboards so in a single click now I can drill down and get to the process optimization workspace to start understanding a little bit more about those whys so what I'm going to do now is I'll just walk you through some of the ways that you can analyze data and some of the ways that the process optimization workspace provides you insights around inefficiencies and non-conforming activities within your process so the first thing that the incident process center will be presented with is the summary and insights page of the workspace and this is going to give us information around the data that we mine so for example in this case we've got 22 000 incidents that we mine those 22 000 incidents took 1600 routes to get the closure and on average it's taken them eight days to get there were then presented with some key performance indicators as related to the process that we're focused on remember we're focused on incident here so things like mttr and our first call resolution rate are relevant kpis it's always helpful for the person that's doing some process analysis to have an understanding of the bigger picture impact within the organization then as we move down the screen or presented with some improvement opportunities I mean these are out of the box inefficiencies and non-conformant activities that we provide for every process on the platform that process optimization is available for customers certainly have the ability to provide or create their own Improvement opportunities they're called finding definitions under the covers as well as the ones that we provide out of the box so for example things like hey of those 22 000 incidents that you mine did you know that 64 of them uh were open and resolved by the same person that doesn't seem like something that should be happening or hey eight percent of these incidents are going directly to resolved and closed why is that happening they're so easy to solve maybe there's some opportunity for some self-service automation inside of these 1800 incidents and the one that we'll focus on inside of our demonstration here today are these 8400 incidents that are going to awaiting caller info and things that go to a waiting caller info certainly are going to slow down the process and have a negative impact on our mttr so we'll dig into those a little bit deeper we could drill right here and focus on those 8 400 but I want I want to show you some other ways in which you can use the workbench to analyze your data as we dig into this opportunity now these Improvement opportunities are my favorite part of the solution like I said we provide them out of the box but you have your ability to create your own as well and I always like to compare these to more you know relate these to when I was playing hide and seek as a kid when I was playing hide and seek at home I kind of had a home field advantage I knew the two or three best hiding spots so when I was it I was much more efficient at finding my friends than they were at finding me same is true with our business processes many of us know where the inefficiencies are or what some of the inefficiencies within our processes are we just always haven't had the data to quantify the impact of our hypotheses process optimization is going to help give us that additional visibility that additional data so we can then start quantifying and prioritizing the Improvement opportunities inside of our organization now as I move down the screen I get what is called variation analysis variation analysis is going to show us all of the individual routes that these incidents took to get the closure in this case the 1500 of them and I can look at them by let's say the route that was taken by the most number of incidents maybe the routes that have the longest average duration or maybe the routes that have the most steps involved with them and for each of these individual routes that were taken I could always in one single click jump over to view the visualized process map of these routes and start digging further into the data now whereas variation analysis is going to show us the entire route that an incident took in this case bottleneck analysis shows us the individual hops or transfers inside of those the individual incidents so maybe I want to look at the long running transfers or handoffs within the process so I can look at these by total duration or average duration maybe I want to look at the Hops that were taken most often or repeated most often I should say and I can do Max repeat occurrences so bottleneck analysis looks at the individual hops within a an incident that an incident took whereas variation looks at the entire route so lots of powerful information or Insight right here on this summary and insights page and from each one of these places I could drill down to the analyst workbench which is my visualize process map I want to start showing you some of the different ways in which you can work with the visualized process map to further dig into the data and identify and proven opportunities so the first thing we're going to do is we're going to come over here and we're going to look at the some of the basic statistics again again we were looking at 22 000 incidents that took 1600 routes on average one week to get to closure and the standard deviation of two weeks now you might be saying she said whoa Dan you keep talking about these 1600 rounds that map doesn't look like there's 1600 different routes there I mean that's because by default we just showed you the ones that are the highest volume routes all right here we're looking at 30 percent but if I were to expand this out to show you all the routes or 90 of them in this case you're going to see it can get pretty busy pretty messy pretty fast um and if there's anyone that's watching this that's part of a governance risk and compliance team each one of these individual lines is something that scares you right because those are all risk opportunities or something that's not conforming in many ways in many cases and something that we should be digging into I'm going to bring it back to something that's a little bit more manageable in this situation maybe are just our top 30 percent now for many of us process mining is is a new muscle it's not something that we're familiar doing so people often ask Dan like what do you look for first when you're looking at one of these process maps and for me these Maps look a lot like ski trails I think back to when I was skiing anytime I started going down the mountain and then for any reason I had to go back up the mountain it certainly wasn't the best experience for me and it wasn't the best way to get down the mountain so I'm always looking for lines initially that are going back up the hill like this one here um in this case here because those lines that are going back up the hill they usually represent some form of rework in the process that we would like to try to eliminate to help us become more efficient in doing the work that we're doing inside of an organization so in this case here I see a line and this is a waiting caller info in progress okay something that's going potentially back in the process and I can see that I had 2600 unique incidents that took this trip as part of their life and then 3 300 total occurrences of something making this trip that means there's potentially 700 instances that took this trip more than once in fact one of them took this trip nine times now probably the most important metric though here is this 19 years of total productivity right so just in tickets that went from a waiting quarter info to in progress there's 19 years worth of productivity packaged up in those 2600 instance that seems like some time that I would like to reclaim now another way to look at that is to use our bottleneck analysis we saw that on the summary and insights page but you also have access to it here on the map itself so I can come in here and say hey only show me the tickets that are going through the state of awaiting caller info at some point and I can see this one here that a waiting caller info in progress step and there's our 19 years worth of productivity but that's not a one-way trip Right tickets have to we're supposed to go from in progress to awaiting caller info so there's the other half of that trip in which I've got 22 years worth of productivity so there's 41 years worth of productivity just packaged up at incidents going from in progress to a waiting caller info and back that seems like an opportunity so let's focus on those so what I can do is I can click on this awaiting caller info node and I can say hey just show me things that go through that transition on the map so on a single click I narrow it down to those 8 400 incidents that went into a wedding call or info and if you remember from the summary and inside the page I had that finding definition or that Improvement opportunity card that told me it was 8 400 incidents that we're going through when code and if I could have just jump start it right from there to this view with the information but I wanted you to see a little bit more now the next thing I can do is I can start using my breakdowns on the left hand side of the screen to further analyze these incidents that are going into awaiting code info so I can start looking at them by assignment group or by category or priority but a common way to start analyzing this is things or as looking at the channel in which these incidents are coming in and is there a way that we can improve the intake experience for these incidents so I can see here that the majority of these incidents that are going into a waiting cover and for coming in Via self-service and portal but third on the list is email and email stands out to me because of the average duration while the majority of them are coming in Via self-service and portal things that come in via email have a whole additional week of average duration in those two channels so let me just focus in on these ones that come in via email and you know what that might make sense emails are pretty unstructured channel so you might have to go back and forth with the user wants to get some additional information but still a whole week there's got to be some opportunity there so what I can do next is now that I've narrowed it down to things coming in via email going into a waiting caller info I can start using this histogram to help me just look at the outliers now whereas the majority of these 1200 or almost 1300 incidents that came in via email and went to wait and caller info and they only went to that step once well I've got some outliers here things that went into a wedding incident that went into a waiting caller info two times I have 115 of those and then I have another 20 that went through a what do you call info 20 times or three times sorry then I have another 11 that went through a wedding color info four times and another four that had to go through a wedding color info five times so maybe I just want to focus in on these outliers those are my biggest problem areas so I can highlight those and say apply filter and it will narrow it down to the 150 incidents that came in via email and went through a waiting caller and found more than once and I can see here for those 150 their average duration goes up by an entire week what I can do next is one of the benefits to being in the platform is that I can always get down to the detailed records themselves and do some further analysis so right from here I can say hey I want to look at those 150 incidents and dig into each one of them individually or potentially I want to use some machine learning to help me get a better understanding of what's going on with those incidents so I can either run root cause analysis or I can use cluster analysis to help me analyze the short descriptions and descriptions the unstructured data within those 150 incidents and I can see there's a bunch of them here that involve somebody changing their call center which sounds like something I probably should have a catalog item for then I have another set of them that look like hey I just got a whole bunch of people that are trying to change their email address in their profile again something that I probably should have a catalog item for and if I do maybe I just need to mark it a little bit better or maybe I need to improve that intake experience potentially I should create a virtual agent conversation to allow people to change their profile and their email address virtual agent conversations are a great way to make sure that we capture all the relevant information up front and we don't have a lot of back and forth so now that I've used process optimization to identify and Improvement opportunity what I can do next is I can come in here and I can actually send a note to somebody to say hey at able could you take a look at the intake experience could you take at the intake experience for people wanting to change their email address maybe attach a snapshot and post that so now able to get notified to come in here and dig into this a little bit further or I can create a continual Improvement initiative or an automation Center request this direct connection with two of the applications on the platform that are designed to help stress capture Improvement opportunities to ensure that they don't get lost in the mix make sure that we understand all of the different Improvement opportunities and that we can then prioritize them and make sure they get acted upon too often we do a lot of analysis and the only thing that happens is we tell our friend at lunch what we found out right these are opportunities to improve we want to make sure that they get captured tracked prioritized acted upon and Then followed up on inside of an organization and this connection with these two applications on the platform allow us to do that this was just a real quick introduction to some of the power and some of the flexibility that process optimization offers to help you start digging in to your given processes on the servicenow platform and identify inefficiencies and Improvement opportunities