>>Mhm. >>What? >>Hello and welcome to today's session of the 2021 AWS Global public sector partner awards for today's award for the award of best partner transformation, best global expansion. I'm your host Natalie ehrlich and now I'm very pleased to introduce you to our next guest. They are James Lester Global Managing Director, public Safety attic censure. And Lauren a thoroughly director of performance analytics and research of the Seattle Police Department. Welcome gentlemen, it's wonderful to have you on the program. >>Thanks for having us >>terrific. Well, we're going to talk a lot about data and a lot about public safety and how, you know, data analytics analytics is making a big impact um in the public safety world. So do tell us I'd like to start with you James. Uh tell us how X enters intelligent public safety platform turns data into a strategic asset. >>Thanks Natalie. Well, the intelligent public safety platform is all about combining different data sets together and taking a platform approach to using data within public safety. What it does is it allows us to bring a whole host of different types of data together in one place, put that through a series of different analytical transactions and then visualize that information back to where however within the public safety environment needs it and really does four key things. One is, it helps with situational awareness, helps the officer understand the situation that they're in and gives them insight to help support and guide them. Secondly it helps enhance investigations. So how do you join those dots? How do you help navigate and speed up complex investigations by better understanding a range of data sets. And thirdly it really helps with force management and understanding the behavior and the activities within the force and how best to use those critical assets of police officers and police staff themselves. And then finally what it does is it really looks at digital evidence management. How do you actually manage data effectively as an asset within the force? So those are the four key things. And certainly with our work at Seattle we've really focused on that force management area. >>Yeah. Thanks for mentioning that. Now let's shift to Lauren tell us how has I PSP you know, really helped your staff make some key contributions towards public safety in the city of Seattle. >>Yeah. Thanks. Uh so you know I think our business intelligence journey started maybe a little in advance of the I. P. S. P. With our partnership With accenture on the data analytics platform. And we've been taking that, say my PSP approach since 2015 as part of our efforts to comply with a federal consent decree. So, you know, I think what what we probably don't understand necessarily is that most police departments build sort of purpose built source systems to onboard data and make good use of them. But that doesn't necessarily mean that that data is readily available. So, um, you know, we've been able to demonstrate compliance with the elements of a settlement agreement for our consent decree, but we've also been able to do a whole host of research projects designed to better understand how police operate in the criminal logical environment, how they perform and um, and really make the best use of those assets as we have them deployed around the city doing law enforcement work. >>Terrific. Now, James, let's shift to you one of the kind of key dilemmas here in the sectors. You know, how can you utilize these um, new technologies in policing, um, and law enforcement while still building trust with the public? >>Absolutely. I mean, I do think that it is critical that public safety agencies are able to use the benefits of new technology, criminals are using technology in all sorts of different ways. Uh and it's important that policing and public safety organizations are able to exploit the advantages that we now see through technology and the ability to understand and analyze data. But equally, it's critical that these implemented in ways that engage and involve the public, that the way in which the analytics and analysis is conducted is open and transparent, so people understand how the data is being used, uh and also that officers themselves are part of the process when these tools are built and developed, so they gain a thorough understanding of how to use them and how to implement them. So, being open and transparent in the way that these platforms are built is absolutely critical. >>Yeah, that's an excellent point because clearly bad actors are already using data. Um, so we might as well use it to help, you know, the good actors out there and help the public. So in your opinion, Lauren, um, you know, what is the next phase of this kind of model? Um, what are you hoping to do next with this kind of technology? >>So as we use this technology as we understand more about it, we're really building data curiosity within the management group at SPD. So really sort of, I would say the first phase of a business intelligence platform in policing is about orienting people to the problem, how many of these things happen at what time and where do they happen around the city? And then beginning to build better questions from the people who are actually doing the business of delivering police service in the city and the future of that, I think is taking that critical feedback and understanding how to respond with really more intelligence services, predictive services that help to kind of cut through that just general descriptive noise and provide insights to the operation in a city that has About 900,000 dispatches in a year. It's difficult to pinpoint which dispatches are of interest to police managers, which crimes which calls may be of interest to the city at large as they manage public safety and risk management. And so are, you know, sort of future development agenda. Our road map, if you will for the next several years is really focused on developing intelligent processes that make use of all of that data, boil it down to what's critically important and help direct people who are most familiar with the operation. To those uh those events, those critical pieces of insight that might be helpful in allowing them to make better management decisions. >>Yeah. And what what are some of the key areas that you find this platform can be effective in terms of uh you know public safety, certain criminal activities James. >>Um I think the PSP has a wide range of applications so certainly looking at how we can bring a whole range of data together that previously has maybe been locked away in individual silos or separate systems. So public safety agencies are really able to understand what they know and the information that they have and make it much easier to access and understand that information. Um I also think it's allowing us to perform levels of analytics and therefore insight on those data sets, which previously public safety agencies have have struggled to do. Um And in the case of Seattle focusing on the uh force management aspect, I think it's helped them understand the activities and behavior of their workforce um in context and in relation to other events and other activities to a much greater depth than they've been able to do previously. >>Terrific. Well, Lauren obviously, you know, this was a really tough year with Covid. What impact did the pandemic have on your operations and some of your more modern policing efforts? >>Oh, I mean, obviously it radically changed the way that we deploy forces in the organization beginning early in March. Uh you know, like most of the world, we all moved home trying to keep up the pace of development and continue to manage the operation. But as that was happening, you know, people are still living their lives out in the world and out in the city. So we pretty quickly found ourselves trying to adapt to that new use of public spaces, trying to identify problems in an environment that really doesn't look anything like the previous couple of years that we were working in, uh and uh, you know, data and and really sort of the availability of technology that helps too identify what's new and what's interesting and rapidly develop those insights and get them available for police managers was critical and helping us identify things like trends in potential exposure events. So being able to identify uh, you know, just exactly how many calls involve the use of personal protective equipment, use that to forecast potential exposure for our workforce. Be able to track exposure reports in the field to be able to determine whether there are staffing concerns that need to be considered. Uh and all of that. Uh you know, we're able to pretty rapidly prototype and deploy dashboards and tools that help folks, especially the command staff, have kind of a global sense for how the operation is functioning as the environment is literally shifting underneath them as uh, you know, uh the use of public spaces is changing and as dispatch procedures are changing as public policy is changing related to, you know, things like jail booking availability and public health and safety policies. The department was able to stay on top of those key metrics and really make sort of the best minute by minute decisions based in the data. And that's really not something that's been available, uh You know, without sort of the ready availability of data at your fingertips and the ability to rapidly prototype things that direct people to what's important. >>Yeah, thank you for that. Now, James, I'd love to hear your comments on that. I mean, has the pandemic altered or, you know, given you any kind of fresh perspective on uh you know, modern policing efforts using these kinds of platforms? >>Well, I think that the pandemic has shown the importance of using data in new and different ways. I mean, one thing the pandemic certainly did was see a shift in in crime types. You know, traditional street based volume crime declined, where we saw increases in cyber and online crime. And therefore the flexibility that police services have had to have in order to shift how they combat changing crime types has meant that they've had to be able to use data as they say, in new and different ways. And think about how can they be more disruptive in their tactics? How can they get new types of insight and really platforms like the intelligent public safety platform help them become much more flexible and much more nimble and that's certainly something that's been required as a result of the pandemic. >>Yeah, that's really great to hear. Um you know, Lauren going to you, I'd love to hear how specifically I PSP was able to help you uh you know, the Seattle Police department as well as statewide inquiries and end investigations. What kind of enhancements were you able to receive from that? >>Uh Well, you know, I mean in terms of investigations, uh the way that Seattle deploys the intelligent public safety platform, our focus is really primarily on deployment of resources that force management, the accountability, piece of things. And so from our perspective, the ability to onboard new data sources quickly uh and make use of that information in a kind of a rapid sort of responsive function was really critical for us but um you know, certainly and I think as as most communities are exploring new ways of approaching community safety, uh the intelligent public safety platform uh for us was really effective in being able to answer those, those questions that are coming up as as people are reforming the way that policing is deployed in their communities, were able to reach out and see just exactly how many hours are spent on one particular function over another, something that perhaps could be available for a co responder model, or take a look at, you know, this sort of natural experiment that we have out in our criminal logical environment as people are using spaces differently. And as we are approaching enforcement policy differently, being able to take a look at what are the effects of perhaps not arresting people for certain types of crime? Do we see some displacement of those effects across different crime types? Do we see an increase in harm in other areas of the operation? Have we seen you know increases in one particular crime type while another one declines? How is the environment responding these rapid changes and what really is a natural experiment occurring out in the world? >>Yeah I mean it's really incredible um Having all that data at our fingertips and really being able to utilize it to have a fuller perspective of what's really happening right? What what do you think James? >>Yeah. I mean I think being able to really utilize different data sets is something that police forces are seeing to become more and more important. Um They're recognizing that becoming increasingly data lead can really help improve their performance. Um And the challenge to date has really been how do we bring those data sets together but not then require police officers to way through reams and reams of data. I mean the volumes of data now that organizations are having to manage is huge. And so really the power of the I. P. S. P. Is being able to filter through all of that data and really deliver actionable insight. So something that the police officer can go and do something with and really make a difference around. Um And that's something that that's absolutely critical. And modern day policing is increasingly having this data driven evidence based approach to help make it far more effective and really focused on the needs of its citizens. >>Yeah and as you mentioned, I mean the algorithms are really driving this you know, um giving us these actionable insights but how can we ensure that they're acting fairly to all the stakeholders James. I'd like you to answer this please. >>Um Absolutely. I mean, trust and confidence within policing is absolutely paramount. Uh and whilst the use of these sorts of tools, I think is critical to helping keep communities in the public safe. It's very important that these tools are deployed in an open transparent way. And part of that is understanding the algorithms, making sure that algorithmic fairness is built in so that these are tested and any sort of bias or unintended consequences are understood and known and factored in to the way in which the tools are both built and used. Um, and then on top of that, I think it's open, it's important that these are open and transparent, that it's clear how and why departments are using these technologies. And it's also critical that the officers using them are trained and understood how to use them and how to use the insights that they're starting to deliver. >>Yeah, and thanks for mentioning that Lauren, what kind of training are you providing your staff at the Seattle police department And you know, how do you see this evolving in the next few years >>with regard to algorithmic fairness, what kind of training along those lines or training >>with the I. P. S. P. And all these other kinds of technologies that you're embracing now to help with your public safety initiatives? >>Well, you know, I think one of the one of the real benefits to becoming an evidence based organization, a truly evidence led organization is that you don't have to train folks uh to use data. What you have to do is leverage data to make it work and be really infused with their everyday operations. So we, you know, we have police officers and we have managers and we have commanders and they've got a very complex set of tasks that they've been trained to work with. It's really sort of our mission to be trained in, how to identify uh you know, the correct UX UI design, how to make sure that the insights that are being directed to those folks are really tailored to the business they're operating. And so to that extent, the analytical staff that we have is really focused on sort of continuous improvement and constant learning about how we can be mindful of things like bias and the algorithms and the various systems that we're deploying uh and also be up to date on the latest and how police operations really are sort of deployed around the city and ways that we can infuse those various management functions or those police service functions with data and analytics that are just naturally working with people's business sense and they're uh really sort of primary function, which is the delivery of police service >>terrific. Well, James lastly with you um just real quick you know, what are your thoughts in terms of being able to extend the power of I. P. S. P. Beyonce Seattle uh in the broader United States? >>Well I mean I think my PSP has huge applicability to any public safety agency in in the US and beyond and we're already seeing other agencies around the world interested in using it and deploying it um Where they basically want to get uh and be able to utilize a wider range of data where they want to be able to drive greater insight into that that data set um Where they want to be confident in deploying open and fair algorithms um to really make a difference. Um And if we to take the the specific example of the U. S. And the work that we've done with Seattle then I think tools like the intention public safety platform have a huge part to play in the wider reimagining of policing within the US in understanding officer and departmental behavior and actually opening up and sharing information with citizens that increased levels of trust and transparency between public safety agencies and the communities and citizens that they serve. >>And you know, on that note, do you think that I PSP is useful in terms of collaboration efforts, you know, with other police departments, perhaps in other states? Um you know or just just as a global national effort. Lauren, do you see that kind of potential in the future? >>Yeah and actually we do that now. So one of the really sort of powerful things about having all of this data at your fingertips and I would say having this kind of awesome responsibility of being the steward of this type of asset for the community. Um and and really sort of for the industry at large is that we're able to take the data and rapidly develop new research projects with researchers around the world. So the Seattle Police Department maintains a network of about I think we're up to about 55 current researchers and institutions. I think we've got about 33, institutions around the world. People really working on real time problems related to the things that matter to our community right now. And having this data available at our fingertips allows us to rapidly develop data sources. We can actually get on a call with one of our researchers uh and build out a table for them to use or start exploring the data in an ad hoc querying layer layer and, you know, making visualizations and helping the researchers form better questions so that when we develop their data, when we deploy it to them, uh they can pretty quickly get in there. It's in the format that they're looking for, They understand it. They can run some tests and determine whether the data that we provided for them actually meets their needs. And if it doesn't, we can develop a new set pretty quickly. I I think that also that research function, that discovery function that were enabled through the use of these data is actually helping to bring together uh the community of law enforcement around this this idea of Collaborative understanding of how policing works around the city, you know, sorry, around the world. So of 18,000 or so law enforcement agencies in the United States, there is broad variability in people's competency in their use of data, but we're finding that agencies that have access to these types of tools or who are starting to develop access to these tools and the competencies to use them are coming together. Uh and beginning to talk about how we can understand sort of cross cultural and cross regional correlations and patterns that we see across our multiple operations. And although, you know, those are varied uh and and range around the country or even around the world, I think that that collaboration on understanding how policing works, what's normal, what's abnormal, what we can do about it is really going to be powerful in the future. >>Yeah, Well, this is really exciting. Yeah. Well, what are your thoughts? >>I was just going to build on the point that Lauren was making there because I think I think that is a really important one. Um you know, when when you look around the world, the challenges that different public safety and policing agencies face are actually dramatically similar um and the ability for policing organizations to come together and think about how they use data, think about how they use data in a fair and transparent way is something we're really starting to see and that ability to share insight to experiment um and really make sure that you're bringing lots of different insight together to further the way in which police forces all over the world can actually help keep their citizens safe and combat what is an increasingly rapidly and evolving threat. Landscape is something that we see tools like the intelligent public safety platform really helping to do and if one police force starts to use it in a certain way in one jurisdiction and has success there, there is definitely the ability to share that insight with others and get this global pool of understanding and knowledge all furthering the level of safety and security that can be delivered to communities in the public. >>Terrific. Well, thank you both so much for your insights has been really fantastic to hear. You know, how these new technologies are really coming to the aid of public safety officials and helping secure the public. That was Lauren a thoroughly director of performance analytics and research at the Seattle police Department and James Schlesser. Global Managing Director, Public Safety at its center. And I'm Natalie early, your host for the cube and that was our session for the AWS Global Public uh, partner Awards. Thank you very much for watching. >>Mm

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>Welcome back to the Bellagio in Las Vegas. Everybody, this is Dave Vellante and we're here day two of UI path forward three. The third North American event is the cubes, second year covering UI path. The rocket ship that is UI path. PDC is here, he's the vice president of AI at UI path and Rudy Coon who is the chief marketing officer and co founder of process gold UI path. Just announced this week, the acquisition of process gold. So Rudy, congratulations and you may as well PD. Thank you. So that's cool. Um, process gold is focused on process mining. You guys may or may not know about them, but really maybe, maybe you cofounded the company. Why did you co-found you and your founders process gold and tell us a little bit about the problems that you're solving. Yeah, right. You know, um, many years ago I started my career with IBM and I used to be a business consultant. >>And typically if you try to implement any kind of technology like RPA, but back then we didn't have the LPA. But if you try to figure out what the real process and the company are and you ask people, please tell me how does the process where it looks like. Usually people cannot tell you. They say yes we have a documentation but it's outdated the moment you print it. So the idea was um, actually I came across process mining more than 10 years ago and I met the guy in, at the university of and he had this bright idea to reconstruct business processes solely based on digital footprints from any kind of it system. I mean, think about it. You, you use SAP, you use any kind of other it systems and you take the data that is left behind after the execution or the support of a process. >>You take it, you push the magic button and you see what the process really is, like an extra races and from business processes. But we, we saw that in the demo at the a analyst event. I thought it was like magic. I mean I think it's actually, I think of a small company like ours easement even though the number of processes we have and the relative complexity and by the way, half the time people aren't following them and but you were able to visualize them. So. So first of all, why did you acquire process gold? What was the thinking there? So you know, just to pop one level up the stack, what exactly are we trying to do as a company? And you are about as we are building this whole new set of platform capabilities, right? We used to have product lines in studio, orchestra and robot, but now when we look at the whole customer journey and all the elements that need to be there in that customer journey, we essentially have to weld something, what I call the operating system called a self improving enterprise. >>And what that means is that our three elements you need to combine. You need to have a measurement system in place, which can quantify the ROI of your automations. Of course you need a really solid RPA platform like ours to do the automation itself, you have to be able to bring in pieces for doing complex stuff, cognitive stuff using AI. And then you need a scientific way of planning those automations using tools like process board because you have to do process mining. Once you complete this, watch your cycle, you can keep doing more and more of the automation. Essentially you're feeding the beast of efficiency in your organizations. So essentially the way this worked, we can't do, don't, don't have the means to do the demo here, but you essentially pointed your system at a process and it visually showed me the steps and laid them out and in great detail. >>Um, and I said, wow, that's like magic. Um, but this stuff actually works. You got no real customers using this if you do. Yeah. Okay. >> So you know, we worked for companies like, like portion Germany, maybe you have heard about them. They, they build cars and they are using process code for part of the production process. Today in today's world, every process, no matter how offensive is a physical process like production or purchasing or whatever it's used or it's supported by it and at least a lot of data behind. And this is exactly that, the goldmine for us. So we extract this data and again, you know, we have a lot of algorithms in the, in the software. It's, it's sort of magic as it is a lot of mathematics, which is magic for me. But um, it works. Yeah, just take the data, you pushed a button and just see the process with all the details. >>As you mentioned, like stupid times, bottlenecks, compliance issues and this three, the, the, the source, you know, if he wants to see the process, you can then decide is it, is this process now suitable for automation or maybe should we first optimize the process and then vote for automation. And this is key for, for RPA. >> Well, I think, you know, I'm talking a lot of customers this week and last year offline as well. A lot of times we'll tell us the mistakes they made is they'll, they'll automate a crappy process. Yup. This presumably allows me to sort of highlight the shine a light on some of the weaknesses and the weak links in the chain. >> So process optimization is a big deal, right? Both in the pre automation phase and in the post automation phase. Once you automated a process, you need to know what are the bad things that are happening there, what are the blockers, what are the nonconforming steps that you're taking? >>So that's in the post automation but also in the pre automation phase where you haven't even decided what exactly are you going to automate. It's really hard to quantify what are the high ROI processes, right? I can go in our bottle, automate something which is not useful at all for the users, right. And so we want our users to a wide making those mistakes. And that's why we are exposing these powerful, powerful set of tools where you can use all these tools to easily document your processes, manage your processes, use process mining to look deeper into how our people and the different entities in your organizations working together. You know, historically if you look at stuff like all of in all of human history, there have been certain processes, but as computers came on and stuff, you look at it on in, in scifi movies, everyone has always, as Rudy says, the X way for the enterprise. >>You always wanted to have this Uber system that can understand everything that we are doing and tell us, you know, how can we improve stuff? Or what can we do better? Because as a species that fuels our evolution. And so this is, it's, it's, it's fundamental to a lot of things that people do in every day and almost in every action that they did. >> So the in the secret sauce is math, right? So again, please, the secret sauce. Yeah, it's math, but you've got to have some kind of discovery engine as well. I mean this is, it's a system. So maybe can you give us a little bit more idea as to what's under the covers? Well, you know, it all starts with data and the data we need in the beginning, it's very, very simple. We need only three different attributes. The first attribute is what we call the case ID. >>So the case ID is a unique identifier for a case and it depends on the process. If we talk, for example, a very simple invoice approved process in the case that it would be the invoice number. When we talk about claims management or with a claims number or a purchase number, whatever the second attribute we need is the timestamp. And every time we find the timestamp in a system like SAP or lock file or database, this time subsume a timestamp actually represents some sort of activity. So we need a case ID, timestamp and activity and solely based on this data we can already show you how the process looks like. And then we enrich this data with other attributes like let's say supplier or invoice amount to give you some more ideas and some statistics. So this is the data we need. We, you know, we transformed this data, we access directly the database. >>So there is no, there's no need to extract the data. We directly access to data and we transform it and then it will be represented in our application. So you get rid of full transparency of what's going on. So when you were a consultant, you mentioned you're a consultant at IBM, you would sit down with a pen and paper and talk to people about what they did. Maybe time and motion studies and studies, you know, you know, this process mapping workshops, everybody comes out and just allows it. So you sit together with people in the room and at the end of the day you have more processes than you have people there. And everybody's telling you a different story and you know exactly that. Not everything is totally true. So a lot of gray area. Yeah. And the maps that you had to build and people simply don't know what the processes are. >>It's not that they don't want to tell you, they simply don't know. Or as I said before, different people have different processes and they don't follow those. There's no standard to follow. She's pretty, what's the vision for how, how process gold fits into UI path. So as a problem was talking about in his keynote, and Daniel talked about this too, um, a lot of our customers came to us, uh, to automate the processes that they already know about for the processes that they don't know about. We have this whole set of tools, the Explorer set of rules that we are releasing. Process world is a part of that. But essentially now you don't need to know what processes to automate. You can use an automated set of tools to do that process scored, as Rudy was talking about, can go in and look at these log files, uh, ordered logs that are generated by your systems of record. >>Um, and then be able to visualize, optimize our process. But the technologies are really complimentary because these guys, uh, used to work in the backend systems. That's why, you know, that's where most of the process mining works works in the back end looking at the audit logs, but you have as has, you know, we have really strong background in understanding the gooey in the front end, uh, understanding of apps, controls and the control flows that the users have using our computer vision technology. When you combine these technologies, there's a magical effect that happens. Like if your backend does not contain the audit, log off some actions that people are taking in the front end. Let's say it's a small application which does not generate that are the, once you combine these two data points, this is one of the first in the industry on the wonderful kind system that can look across all the different spectrum of applications and be able to understand the processes at a deeper level. >>Technically when you make an acquisition, you obviously looking at the technology and how it's going to integrate, how challenging will it be for you to integrate? What have you done any sort of, when you did the due diligence, you know, a lot of companies are really dogmatic about integration. Others frankly aren't that let's buy the company up by another one. What's your philosophy? It >>was kind of a match made in heaven. I remember the first time I talked to Rudy on the phone and uh, you know, are at the end of the day our philosophies aligned like almost a hundred percent because at the end of the day process goal and UI bad is all about that customer obsession, delivering the value to our customers. And the values are saying we want our customers to get out of this mundane tasks to automate the tasks as optimally as possible. And so both the companies, the, the, the outcomes aligned pretty well. Now the mechanics of the integration, um, I think both do. Both the companies are, these aren't you know, dot com era companies where you know, somebody came over the an idea and did this take Rudy and the team had been working in this area for 10 years. They have organization knowledge, they have the expertise and so does you have adults. >>And so we will take what I'm, what I call a loosely coupled approach where we can choose common customers, we can choose comments that are features that we are going to work on and that's how we will integrate. But again, the focus of all this is to deliver the value to our customers. Not think about the mechanics of what the integration would look like. I think one of the most exciting things that I'm hearing is this notion of the processes that are not known. Um, because so many processes today are unknown, especially as we go into this new digital world. We used to know what processes we want to automate your point, some technology at it. Okay great. We're going to automate now with this digital disruption that's going on. You actually may have no idea. You may be making processes up on the fly, so you need a way to identify those processes quickly and then those ones that are driving our ROI. >>Um, I'm interested in your thoughts on AI and ROI and how to measure that, how those things fit together. So, you know, AI, this is I think the biggest problem in the AI right now. There's a lot of hype in this space. We are tracking close to 3000 different AI startups in the world and uh, nobody can actually put a number to the revenues or the valuation, the real valuation because of this ROI quantification problem, right? Um, let's say I have a company, we'd say, Oh, we are the best in class. And understanding faces short, how is it going to be useful to an enterprise if you cannot measure what well you official recognition system is adding to your enterprise, it's not good enough for the business people. Because at the end of the day, my, I can have the world's brightest PhDs telling me I have the state of the art model in the world, which does law, but in fact cannot translate it into business value. >>It doesn't really work. And so that's why ROI quantification is so in parking and you have to make sure you align them econometrics of the AI, uh, measures and the business KPIs so that if, for example, so your data science team should be able to know what metrics they have to improve in order to get a better ROI for the business. So you have to align those two things. And that is part of research that is not really prevalent in academic circles. Interesting. I mean, you've seen some narrow successes in I'll call AI, you know, things like a infrastructure optimization. Okay, great. Makes sense. What I'm hearing from you is identify the KPIs that are going to drive your voice of the customer defines value first to take away, identify what those KPIs are. And this every business has thousands of KPIs, but there's really like three or four that matter, right? >>So identify those top ones and then you're saying measure on a continuous basis how your system affects those metrics. So in economics this is called the treatment effect. Uh, so for example, if you water my term sales and marketing processes, the KPIs that matter to you is what is your conversion rate from when the leads hit your system to when the revenue is realized or what is the total revenue that you're making? Right? As you said, there's only two or three top level gave you as that really matter. And now if for example you put an AI system in place that treats your leads differently, you should see an increase and uptick in revenue. And so that's what I mean by the Ottawa quantification. So if you instrumented the system properly, put it in the right quantification measurement system in place and have the auto optimization mechanism, that's how things should work. >>You know, with with cross mining we can even add additional KPIs to the picture KPIs you usually don't have because if you ask a company, nobody can tell you how many different variations of the process you actually have. And with process mining we can exactly measure how many variations there are. So if you are up to streamlining to simplifying the process to speed it up, we can actually tell you if your optimization effort is successful or not because we can show you how the number of very our variations is going down over time. Even if we, you know, we can also measure the, the success of RPA implementation. So it really pros we use process code and pro money not only for identification of processes but also for the monitoring of processes after an successful RPA implementation. I can see so many use cases for this. >>I mean it's like my mind is just racing. I mean sales guys in one region and sales gals in the other region doing things differently. You've got different country management doing things differently. If I understand you correctly, you can identify the differences in those processes, document them, visualize them and identify the ones that are actually optimized or help people optimize and then standardized across the organization to drive those metrics that matter. It's very powerful. It is really powerful. You know, as I said, we are living in the golden age of this system that can self-improve your companies. I mean this, this was the Holy grail of all of computer science work with technologies like process score with RPA, with AI. I think we are at that inflection point where we can realize that. So we got to go. But I'll, I'll give you guys sort of the last, last word, each of you. >>So actually first of all, Rudy question, how large can you tell me how large the process gold team is? How many people? We have grown with 60 people. 60 equals zero. We are based, our headquarter is in the, is in the, in from the Netherlands. Um, so this is where we are very close to university. This is where our developers basically are located. And uh, I'm based in Frankfurt in Germany, but for now, let's see what the future will be. So what's a home run for you with this marriage? The home run, you know, since we are in Las Vegas, I was wondering if you hit the jet park Jack photo, if we hit the jackpot. But I actually think of the customers, our customers get the Jaguar because this combination of, of your technology, of our technology, this is really, you know, good answer. So that as I was gonna ask you the same question PD is, I can't even tell you, um, almost every one of the UI path customers has expressed interest in process glow, right? >>Because right now we have a portfolio of products, but the interest that we are getting in process board with the process mining offerings is unparalleled. So Rudy is right. Our customers are the ones which are driving this inhibition and the integration. And I'll be able to actually acquire this solution. I forget, I have my notes with relatively near term, right? Yes. We are gonna make it available to our customers as soon as possible. Awesome guys, congratulations. Really great to have you on the cube. Thank you. All right, and thank you everybody for watching. We'll be back with our next guest right after this short break. You're watching the cube alive from the Bellagio UI path forward three. We were right back.