short video is part of the process optimization use case Series where we'll focus on different techniques to identify process inefficiencies non-conformant activities and Improvement opportunities one of the many things that makes process mining different than your more traditional reporting and analytics is the ability to use the steps or transitions within the life cycle of a piece of work to help narrow down the data and identify opportunities for example one interesting way to look for self-service opportunities or potential routing or staffing issues would be to focus on pieces of work that take a long time to get to the in progress state but then take a relatively short time to get to the resolved or closed state as of the Utah release of servicenows process optimization we can quickly isolate these opportunities using the transition filter capabilities on the analyst workbench let's take a look at how that would work so in this case here we've mined 22 000 incidents that we'll be focusing on but again you can apply this technique to pretty much any workflow customer service case HR cases custom applications you've built what we're going to do here is we're going to use the transition filter capability in the lower left hand corner of the screen to capture incidents that are taking longer than two days to get to the from the new to in progress State and then less than 30 minutes from in progress to resolved so we can do this by coming here and saying state is new and then say it will eventually be followed by this state of in progress now we change this value here from followed by to eventually followed by just to say hey if we kept it at followed by that would be we'd be looking for things that went from immediately from the new state to the in progress state eventually followed by it says Hey at some point after the news date it's going to get to in progress this allows us to incorporate things like the assigned field or something like that somewhere in there but we don't really care about all the individual hops we just want to know that it got to the in progress State and then we're going to add this constraint and say hey we want to focus in on the incidence in which it took longer than two days for it to Traverse its path to get to this in progress state so a long time until we move it to an improver state where somebody's actually starting the work and then we're going to follow that up by saying well now after in progress we want to see that it was eventually followed by the state of resolved and we're going to add this occurrence of the elastic terms that are resolved and that'll allow us to make sure that we're only looking in things the entire time that it took to get to resolved like sometimes things get reopened and go through the resolve State more than once we're just saying we want to focus in on from in progress to the last time it hits the resolve state and we're going to add an additional constraint of less than 30 minutes so less than 30 minutes for us to say something was in the in progress state to the result State solving the problem really really quickly and if it's that easy to solve you know what we probably should have a self-service option for them so let's hit apply and now this is going to narrow this down to the 174 records in which that occurred in which we had tickets that took longer than two days to get assigned and there's going to explain about to get assigned but to go to the in progress state you can see here if we start looking at all the different steps to get to in progress they're all going to be longer than a couple of days and then if we focus in on these things that's going from in progress to resolved you'll see here that on average these 174 took nine minutes and if we look at this here and we start looking at the buckets inside of our histogram all of these 174 are going to be less than 30 minutes which aligns to that constraint so we've used the transition filters to narrow it down to things that are taking longer than two days to get to an in-progress state but once they get there we're looking at just the ones that are taking less than 30 minutes for us to resolve just an example of how you could use the transition filters to really start digging in and analyzing the data within the process itself and now we can start using all the other capabilities of the process optimization workbench to focus in on narrow these tickets down even further maybe we want to use our breakdowns to look at which assignment groups these are falling into or which categories of tickets these are most often again just one way to approach doing this analysis there's probably several other ways and a one being potentially adding the assigned to Value to the project as an activity and then looking at the transitions from new to the assigned to and then from the assigned to ultimately to the resolved if you're going to do that I would suggest narrowing down the Project based on the certain assignment group just to keep the assigned two values to a minimum thank you for your time here today happy mining