Personal Process Mining Success Stories

Welcome to another episode of The Mining Your Business Podcast. A show all about process mining, data science, and advanced business analytics. Patrick, how are you doing today? I'm confused. You're doing the intro. I'm doing the outro. I'm just generally confused. Well, too bad. We are going to break a lot. We will be talking about our past projects and the successes we've had. We will tell you about how much money we saved our customers, how much data we migrated, and what is Patrick's biggest passion project. Let's get into it. We've been doing this job of process mining architects for quite some time. And we thought we could just go back in a history a little and think about what success we've achieved. And when I told Patrick, let's do a success story. He was like, nah, we don't have any success. I was thinking, we actually do, Patrick, don't you think? Yeah, I mean, the more I thought about it, you know, you made a very good point. We should highlight a little bit our own personal successes. You know, we talk to so many people and they talk about what they've achieved at their companies and those are all great things. But I think it's time for us to brag a little. What do you think? Yeah, bragging sounds exciting. And what also sounds exciting is having this solo episode because as you mentioned, we did a lot of guests and it's amazing and just to talk to these people and just to name a few. I don't even go to name a few. Just look up our website, miningyourbusiness.com, where you can actually find all the people we talk to, listen to the episodes and just get the value out of it yourself. It's really cool. I think you will not regret. And it's been a great ride. And I was thinking, let's do this solo episode today. And since it's Friday afternoon of the time of recording, Patrick is and we will be very honest in this one. Patrick is slightly hung over right, Patrick. And slightly. You were building that team spirit yesterday, weren't you? Absolutely. Team spirit is important. Team spirit is 100% important. So we were thinking, let's just take it's really easy for this one. It's going to be a pleasant journey of an episode where we just talk about what we've been doing in our project. We will obviously need to keep some secrets for ourselves. We can't really say names of these customers of the problems that we were solving. But we will still try to be as open, as frank as possible. So let's do it, Patrick. Yeah, let's get into it. All right. And I guess my first project, or it's not really my first project, but first item on my list here that I want to mention as a success is customer of mine, which I've been working on for almost two years now. Exciting customer really, where we were working on a accounts payable process and purchase to pay process. And it's been, I would say, at times a bumpy road, because already at the beginning, we had a bumpy. Why bumpy? Well, you know, when you come to a company that just had a massive data leak, and you all want them to extract all their data into some cloud environment. You know, they're not very happy about it, are they? Yeah, yeah. That's the type of stuff that gets the data security guys into a bit of a frenzy. Yeah, 100%. And there was so much pushback already when we were starting with this project, when we had to ensure that this pipeline of data extraction was just a secure and unstable and everything. And we, I remember back in the days, I had to also communicate a lot with cellonists where we were creating a very custom extractor for the customer for a specific one. It was a headache, don't get me wrong. Like I had to talk to so many people and answering questions, I had no answer for. That was a really learning experience because there's only so many times where you're in a room full of experts when you are clearly the least educated person on the topic and you are the person who's supposed to give them the answer. So. Well, I guess in that situation, you just have to mediate and find the right people to tell exactly what's known. It's definitely a lesson in humility, that's for sure. Yeah, that'd be good. I mean, if you were the smartest guy in the room, that'd be a terrifying room to be in the Acro, true, true. However, it's not really. So this is already a success in itself, but it's not really the success that I want to mention. Success we actually got with this customer came later on after almost two years of having a nice looking dashboard full of very interesting use cases in place. And what we've been struggling there with was the adoption. And do you know it? We, everybody knows it. The adoption of process mining is a whole different story than an implementation of process mining. I keep mentioning it almost in every episode and how important people skills are in this role and in this type of project. And so what we did there, the company was getting a little. I would say. So. How to name it? Nancy? Probably. Yeah, nervous is a good word, because, obviously, when you are paying a lot of for service provider for the implementation partner and everything, you want to, at some point, see to get some value out of this solution, especially when everybody's just having the success stories, how things are great and how much money they're saving because of the process mining tool. And we weren't there, unfortunately. It pains me to say, but sometimes you have to admit to it to move on. And why do you think that is? Wow, why did it take so long? I wish I had an answer for that, but I don't. I always felt like I was doing a lot of things right, but I simply probably didn't put enough of thought of what the users would eventually be doing with it and who would the users be. So while I was focusing on having everything right and everything correct, I wasn't focusing enough on actually enabling them to use it once I would step out. So it was kind of a problem, I would say. Right. And you put this down as your biggest success, right? I did, because then I said, okay, guys, we need to change it. And you know, having all this experience from doing mining your business podcast and talking to all these incredibly entertaining people, I started to look at the problem from a bit different ways. So we had everything in place. And they just named a couple of problems that they wanted to solve, which we, you know, like we allocated use cases that everything was in place. And all they had to do was just to use it. And so I thought, let's sell ourselves to them again for like a prove it, prove it contract. We give you, I don't know, two or three weeks of our services. And if we don't have any results after that, like, it's on us. We are out, we disappointed you, and we are so sorry. You don't, you know, you deserve someone better. And I was really rough on myself, because I really took it personally to this project, because I was also the one who was there from the very beginning. And I really thought that it could be me. And so we took this one use case in duplicate payments. And if you don't know what it is, then every organization that pays like millions of invoices per year is likely, very likely. It's almost guaranteed that they will pay for some stuff twice. Unknowingly, or by mistake, it just happens. And my colleagues, actually, they have developed, and I will be very specific in Noxana Kostya, who would develop this algorithm to check for duplicate payments. And we've successfully implemented it across different customers. You know, we are just scanning through all the invoices from different systems, different channels, for different criteria. You know, we are comparing based on different fields, such as vendor name, reference number of the invoice, amount of the invoice. And we can get very, I would say, technical and statistical about it. So we can give you recommendations of possible duplicates with some, some probability, right? And all the customer needs to do is validate it, right? They need to check the data and say, OK, this is an duplicate or not. And this, imagine that you have this at your hands for a year and you don't use it. And so I was thinking, what can we do? How can we actually go the next step and make sure that they are using it? And this is why I'm so excited about what solonesis currently offering, because their latest implement are the latest features for us, the data scientists. That's basically the, I would say, the user interface, where you can actually make actions. And these actions are then processed back into solonesis. And you can actually, like, communicate with the system by just clicking on things, which wouldn't be previously possible because previously it was just a reporting tool and, like, analysis. Now it's more like an proactive interface. And so we designed the system for them where they would still get the same results. Still would get the same recommendation recommendations. But we would go the next step and tell them, OK, all you need to do is just click a button here. And then basically whatever you do, we get the feedback back into soloness. And we show you, then, like, the ratio of how many you actually processed, how many are still pending, and then, obviously, how many are duplicate payments and how many not. And I remember when I was doing a workshop on this with the users, there were, like, 10 people in the room. Well, in the Zoom room, let's be specific. I wish in the room, but I guess it's not 2019 anymore. And I was just looking at some random example, right? So I click on the invoice. And so here's a, here's a duplicate recommendation. And you know what? Let's check it right now. So I asked the user who had access to SAP. So I gave her the number of the invoice. And she checked. And this invoice was interestingly worth of 60,000 euros. And she was just looking in the data for a bit. We were like, why? Looking at it. And she was like, right, so this is a duplicate. And we completely missed it. And like, at this instance, I headed them. Like, you could feel it in the room that, you know, the management was like, I think, OK, maybe we're on to something here. Why didn't we catch it before? And so did you ever show them how many duplicates they even had? Yeah, so that's the thing. We started to track it. And it happened in January this year. And we started to do it since then. And now it's time of recording is almost end of April. And since then-- and this is a really crazy number. We are about, at the right now, at 2.8 million euros of savings in invoice. Like, or even prevented or caught duplicate payments that can be eventually reversed or requested to be paid back. Oh, wow. 2.8 million. Since the start of January. Start of January. So four months, four months, man. That is well. It's crazy because, you know, all it took, the data was always there. All it took was to think about designing a system around people and have one positive experience, one positive spark that would result in them going into it and actually thinking, wow, this thing can help me. That's actually crazy, you know, considering we're all about the technology and the data and stuff like that, it always comes back to the people. The people are such an integral part of most initiatives, be it process money or any other, right? There's always-- you always have to keep the people in mind. Yeah, yeah. So again, big thumbs up, especially for our developers who did that. I just, you know, I'm just, yeah. I'm just here taking the sweet fruit of our labor. But yeah, I'm very happy about that. And that's definitely one of my peaks so far of having this milestone. And also, I'm glad that I just did this proof kind of a deal. It worked out, and I hope we will be-- or I know we will be implementing some new processes pretty soon, so all good stuff. Very well done. All right, man, what's up with you then? Well, what did I put on this list? Yeah, so this client took about a year, a little over a year of my life to really complete. It was a migration. That's what we came in for. They had an on-premise system that we then needed to migrate into the cloud. And with that came, of course, the big question. Are our KPIs going to match at the end of the day? Right, we see this number on the left side. Are we going to see the same number on the right? That was the big question. Hold on, hold on, hold on. Did they come to us because they heard our episode about, you know, on-site versus cloud and how to migrate to cloud episodes that we did, I think, was like episode 9 and 10. No, I was already going-- the migration was already going on with that episode. OK, OK. Yeah, yeah. So I'm sure they listened to it afterwards. And I'm sure they heard of your references about themselves in that episode. But they're going to hear a few more from me now. So it was a lot, a lot of data. I think it was the largest cloud implementation of Solonus to date. Yeah, there might be some more now since then. But at that point, it was a lot, a lot of data, right? And whenever you deal with a lot, a lot of data, data and JSON starts to become a big problem, right? So one of the big-- like I said, one of the big questions is, is it going to match, right? And you just don't know, right? You have the kind of building blocks. You have the code that used to make it run on the on-premise system. You have the data model that was loaded in the on-premise system. You have the analysis that are showing you and are calculating these KPIs and stuff. You have all that stuff, right? And now you're moving it, and you pray to God by the end of it, the numbers match, right? There's nothing worse. Because I mean, imagine just a lot of legacy code. One of the big problems was there was a lot, a lot of legacy code. Years old legacy code, where developers had left not written documentation and things like that, and we were trying to figure out what it is that this code was. That sounds very familiar. Yes, probably because I complained about it for a year. So it took a lot of time. I mean, also we're kind of translating it from Oracle into Vertica, so all of the different functions and stuff aren't the same. So you kind of have to think around it. How can I really translate it? And not keep it one-to-one, because already the function don't match, so that was a big thing. Then we start encountering some problems. Problem one, the performance. Now these queries, these transformations, the transformations that are changing the data, the raw data, were at some points so inefficiently written that essentially it just wouldn't run anymore in the cloud. So this became a big, big problem for us, because all of a sudden it changed from a one-to-one migration to a performance optimized migration. And now the hairs on the back of my neck start to raise a little bit. Oh, I remember Patrick before this project who actually used to have hair. And now I'm looking at Patrick after the project. And you are both, my friends. Significantly changed is what I would say. Yes, I think I've also started getting wrinkles and stuff from this. Essentially the problem is, as soon as you start changing the transformations, they're no longer one-to-one. And you pray to God that the changes that you made, optimizing them, actually result in the same data at the end. And I count this, I know I'm complaining a lot, and there's tons of things that I could say about why this was very, very difficult and took so long. These were five sprints back-to-back-to-back with a very tight schedule, with code reviews that happened, where literally every single line of our code was analyzed if they had proper indentation and in the formatting and things like that, naming schemes and stuff. All things that are good, I agree. But we just had to translate it from a whole bunch of legacy code, undocumented legacy code, right? So I guess this is one of those things that you can do in a migration, just optimizing it along the way so you don't run into the same problems that we did, right? So that kind of multiplied all these issues. And I still remember the last sprint, because the sprints that were five of them got progressively more difficult as time went. So the, it's like, you're almost there, except you're it out. Exactly, so the data models got bigger. The transformation started getting, more source systems were added the longer, like these things went on. So in the last one, sprint five, definitely one of the hardest things I've ever seen or code, right? That we needed to unpack. And I still remember the data model loading for the first time, right? I've done all the migration, I've written all the code, translated everything, you know, sweat dripping from my brow, everything. Punch that, punch that key, the data model starts loading. And I'm just so eagerly waiting to see if the numbers match, right? Because that's the make it a break at point, right? And my god, I look at the data left side, KPI says 10, right? Right side, KPI says 10. Okay, that's one. And I just start going through and checking the dashboards, checking the process explorer. Yeah, that matches that much, that matches that much. And I just go through and literally everything matches. Oh, wow. Everything matches one to one. And I couldn't believe it, really. I thought I was maybe duplicating the dashboards in on-premise and I was just looking at the same one. No, no, it was-- It was cringed shut there, right? It's just cringed. Yeah, exactly. So I was flabbergasted at the fact that it matched so well. And it was one of the happiest that those colleagues have seen me. They saw me laughing and smiling, say, what are you so happy about? And I said, well, look at this. And they were equally positively surprised. And we were able to migrate the rest of the stuff successfully. And everyone was happy. And this is one of those things where it's so much work and there's so much build up. And the payoff, finally, giving them the end result and it being perfect, this is a bragging episode. I'm allowed to do this. It was a perfect migration. And we did it really, really well. And customer was happy. The performance was amazing afterwards, super, super quick. It basically, a lot of them were loaded maybe once a week because they were so large to load. But we were able to get that down to four hours. So huge, in dramatic improvements in a lot of aspects of their implementation. And yeah, that was-- I count that as one of my biggest successes. Wow. Congratulations, man. So if I ever need support with transport and migration, I might just watch out to you. Yeah, you may, but I might just throw a brick at you. I think I've done enough migrations for a lifetime. That's true. It's actually our second one, right? Third. Third. Oh, wow. You were an expert by now. Yeah. It seems like it. Cool, man. Let's go back to my site. Yeah. I would say similar kind of a project in terms of scope and just the overall complexity. I know what you're going to say. Yeah. So my project really-- I didn't lose my hair. Luckily, they're still there. And they're still there. I'm joking. I'm joking. And just to be sure, I also keep my beard in case I ever lose my hair. So at least I have some facial hair on my head. Anyhow, let's stop about beards and hair. Let's talk about the project, plan 12. This project was a very specific and multiple ways. So first of all, the scope was insane. This customer, they wanted to implement-- it wasn't that much of a process mining, rather like a business intelligence, because they wanted to look at the very specific parts of the processes in terms of performance. So they designed these-- they call it process performance indicators. So it still had a lot to do with the process, but it just wasn't process mining per se, not in the way that we are used to that, that we know it. And it really was more like a standard BI reporting. The first huge obstacle was just how many different areas they wanted to measure. And we are really talking about procurement, about sales, deliveries, HR, a make domain, as well. Finance. Sorry, what does make? It's like production, maintenance. It's also making stuff, OK. Yeah, they just call it make, but it's a standard stuff. You know these tables. Yeah, we've got a very, very interesting one. The boom explosion. Everybody loves the boom explosion. They love materials explosion, yes. Yeah. And also very specific use cases for their business. Yeah, I'm not going to name it right now, but again, very challenging. And I have to say, when I started the project, I knew like 30% of this stuff. And now imagine that you're going to a workshop with this customer. And I will be very honest, that the overall level of preparation that went into this project was very, very low from their site. And you know, you are going to a workshop and you expect that they know what they would want to do, but what usually happened in these workshops that they started to argue about what they should do. And you were like sitting there thinking, OK, this is going well. This is a discussion for a different time. Yeah, like this meme of a dog that's sitting in the burning room and saying, this is fine. This is exactly how I was feeling. And you are kind of expecting them to lay the groundwork very, very firm and give you exact instructions on what they want to measure and what they want to build. But you find yourself coming out of the workshop having one screenshot of some guy showing some random stuff in SAP and a name of custom transaction. And then you're like, OK, guys, what do I do with this? Like, what data do you need? But like, I have no idea. And to make things even worse, the core project team was about six or seven people. But over the course of the project, we talked to like 50 or 60 more other people who were experts in the domain or the business users or the consumers of the reports. And these people are also from Oracle's the globe. So it wasn't unusual that you would have a course with people from like a central Asia, from Eastern Asia, from Europe, from America, and so on so far. So we're already the work, how it was scheduled, was very interesting and very strange, also, I can say. This customer also, the working work, was different for them as they weren't working on three days, but on Sunday. So there wasn't 100% overlap. And also, culturally, this customer was just difficult, I would say, because that's nothing like against the people itself. I think the people were extremely lovely, but just the way that they are looking at work and that the general way of understanding cooperation between those two parties. And you know how it is that there is a difference in when you work with European and when you work with Americans. And then there's also difference when you work with people from Saudi Arabia or Central Asia and so on. So you could tell these differences and sometimes it just led to a very unpleasant escalations and stress that was completely unnecessary and would be all caught if just the definitions at the very beginning were very precise and very concrete, which unfortunately they weren't. And so as an analogy, would you say that you're kind of an architect and they came to you saying we want to build a house and then you say, okay, what kind of house would you like? And they just have no answers to your questions. - Yeah, 100%. And I think when I was describing to a friend of mine once this project, because it made me very stressed as well, because they wanted to build 70 reports in two different tools. So one process, money tool, one, another BI tool, and not to say that I didn't have an experience with one of those tools before and I had to learn that as well. So they already added some to my stress level up there, but obviously we can do that. So we just did. And I was going to the project as, let's say, a main project manager and also the main data scientist supported by a very, very talented colleague, colleagues actually two of them, another my colleague, but also another working student who is great, but wasn't there like half of the time. And then imagine that you're working on such a large project in two and a half people, having to jump into the course all the time just to cover for the person who's working part time. And it's just, it's just at so much unnecessary complexity. - Oh yeah. - And this could be easily prevented and if everything was just well designed would be great. And like the bottom line is that we actually made everything in time and we actually went through all the UATs user acceptance testing and like passed all the reports. So when I was looking at the reports for the first time or for the definitions, I was like, there's no way we're going to build this. Not all of that. And there's no way we're going to validate this and get the approvals. And yet like nine months later, there it was. We had 70 very well documented and built reports in two different tools over many different domains and they all got approved. And we finished the projects and came up like heroes. And I was very proud of that because it was very challenging, very stressful, but luckily we managed and came on top. - Yeah, I still remember that time 'cause sometimes you would get up to walk to a meeting and I could just hear you mumbling on pleasant trees in your breath and I know it was stressing you out a lot. - Wasn't it overlapping with your a little stress project? - Yeah, yeah, just talking about it. - Exactly, we were just not, we were just kind of regurgitating all the silly things we had to deal with. - Yeah, these mammoth projects are just, like they are great, but also very, very bad at the same time. Very draining. Anything that goes for longer than six months, it's just so tiring. - Yeah, and I mean, they're necessary. And it is, we are good at what we do, right? So that is some quality we would, we want to, of course, provide throughout the entire project. Not just at the beginning and where we're still enthusiastic, but now we have to push through this and get it all done and that takes a lot of energy out of you. - Yeah, yeah. But all in all, I'm glad about this project because it's taught me about domains. I didn't even know they existed before. Well, for instance, I had no clue that there is an HR module in SAP. Well, there is. - Oh, I mean, I did know that. I just have no idea what it looks like. - So, you know, you have to learn hard way and it's a broadens your horizons and eventually, if it ends up being a good project that you learned and I think it's worth it. - It's funny because every time I ask you about, "Hey, have you done with this before?" And you say, "You go back to that customer." I did something similar at that customer. And so I know that you took a lot from that. - Yeah. All right, man. Then there is your little baby, right? - Yeah, yeah, my little labor of love. So, I'm a bit of a Python nerd. I call myself a Python King, which, you know, of course, I am. But one of those things that I started doing when I started working with Solonus and process mining and things like that was I saw a big opportunity to optimize a lot of the things that we were doing, right? We have manual things to do, you know, checking columns from tables and doing, copying from one place to another and checking where, what table we are using and this transfer, right? We have a lot of manual work to do, right? So some of this stuff is ripe for automation, right? So I just started writing down little functions for myself that I was constantly using. So not needing to reinvent the wheel because before, before I got there was essentially just a bunch of scripts were flying around. Everyone knows this tale, like everyone has some local, locally stored script or something that they're using to do something and just sharing it via Slack or something like that. Not very organized, right? So I said, okay, that's cool and all, but I'm just gonna start aggregating all these things that are coming across my desk. All the things that I'm using and kind of building them on top of each other, right? 'Cause there's some functions that use this and we can kind of build on top of each other. And so this just started growing, right? And all of a sudden, I realized I just have way more than just elicit functions. I've got a module here. I've got multiple, I have no. I've got multiple modules here, actually. And a whole bunch of different functionalities that I can now all of a sudden organize. And all of a sudden, people started noticing and they're like, oh, you have this module that can do this stuff. I'm like, yeah, yeah, you can use it, here you go. And then I realized everybody that was, including you, have some cool ideas about what you needed for what you need automated, right? So I just started gathering everybody's ideas, everybody's sometimes ludicrous ideas about what they wanted to automate and just started working on it. And you know, obviously did a GitHub so people can contribute themselves, you know, and merging and pull requesting as much as they liked, as well as, you know, writing proper documentation was a huge part of it, right? Because I know if I don't write good documentation, everyone's gonna start out with that. How do I use this? How do you use that, right? So I just started really, really going deep into this Python framework. And it's grown tremendously, automated testing and automatic documentation generation and things like that. So it's a lot, a lot of work went into this. But which, you know, of course, we can't just sell or anything, right? It's more of a indirect savings for us, right? So I'm priding myself on being able to provide my colleagues some functionality that saves some of their time, right? That allows them to do their jobs easier. How much time does it save? Well, since no one can really say, and no one has tracked it, I'm gonna say three billion hours. Wow. Yeah, I know. How much is it? $4.5 trillion, $4.5 million, I don't know. Can I get that saving? No, that's fine. So essentially, it was one of those things that was very necessary. Because as we started growing as a company, we needed a thing to aggregate a lot of these functionalities, right? Everyone has the same questions. Everyone wants to achieve the same stuff. So this is the Python framework that helps you do that. And now it has blossomed into a very nice collaboration amongst colleagues where we're all kind of figuring out what the best way to do these things are and keeping it growing and constantly needing to adapt it, right? Because code changes, platforms change, and all these things. So there's a ton of work to do. And the more I think about it, the more stuff we can add. And now I am, you know, you know when you cook something, I've done for some-- Just kidding. OK, let's pretend that you cook, OK? So and you cook for someone, either. You know your parents, girlfriend, boyfriend, whatever. It's somebody that you care about. And you know me very well, Patrick. [LAUGHTER] This is more for the audience, I understand. So say you're cooking for me, Jacob. And you know that point where I say, mmm, that tastes really good. And you have that pride like, hey, something I made is resonating well with somebody that I care about. You know, that's kind of the relationship I have with this. The thing that I'm building and seeing people use it, like using it efficiently as well. And having it save these people time is that feeling for me, exactly. Like, this is indirectly saving them time. It's a good thing. You know that that's what I get from it. How far do you want to take it? To the moon and back. The moon and back. I don't know. I don't know. At some point, I'll just maybe get an AI to write this stuff on. GPT-3 is getting quite good at this stuff, so. Let me know when I can use your bot to just do all the value creation for me and to talk to people. And also write queries for me and just sit and put my hands up. I mean, sorry. Like, I'll be sure to let you know. So now, after my little Python geekathon, I think it's worth to talk about something very different, which is your foray into sales, what do you say? Oh, yeah, man. I never thought I would, first of all, call it to be a success story of mine or just being proud of it. But generally, I started as a data scientist and I progressively went more and more into this talking curve, right? So you would probably call me a project manager now. You could call me, I don't know. I'm called a team lead official now, based on my contract. But also, you know, we do this podcast together. And I pitch a lot of ideas to customers and so on. And eventually, I just turn out to be this-- I know, I want to say guru, not at all. But more of a salesperson, really, I feel sometimes. And I'm always very happy when my project I work on gets like a prolonged and we get the new contract, the new PO. And actually, when I was thinking about it in this episode, in my projects, I have so far 100% retention of customers, which is just amazing when you just think about it. And the applause isn't as good. So basically, every project I worked on, either as a lead or at least at a part in it, I always got prolonged. And you could call myself lucky. But at certain point, you just think, OK, so probably there's a little bit more to that. And I'm just very happy. And I try to stay humble about it. On the fragging episode, right? You don't have to be humble. But it's a really good feeling. And I am happy to be in this position where I actually can say, OK, my projects go well. And I think I have a lot to do with that. And it's great. And even more and more, I find myself in this situation where I talk to someone who I never told before about our services, what we can do for them. And very often, it also goes well. So what I see that my very big strength currently is that I started from technical background. So when you start as a data scientist who built these processes himself, you learn a lot. And you learn about the pain points these people that the business people have. You learn about how to solve them. And slowly, slowly, you build up the knowledge to address them and to get them on the other side. And when you already have this, and then you start building up this people's side when you actually know how to talk to them, how to listen to the customers, and how to unlock whatever they are trying to find. It's massive. And you can tell them the difference between a person who's just about a selling part and doesn't know that much. And I don't want to say it's their problem or their mistake. It's just the fact doesn't know that much about what is really, really going on. And I'm very happy that I can differentiate myself from this because I know very often, or about a lot of processes, and then coming into this position where I can actually position our company as being very good at providing the service. I think I'm going too far with this. But just it's a good feeling to sell stuff that you're really, really proud of and knowledgeable. And you know that you can actually help with the problem. And you're not just selling the services or the tool, but you're just selling yourself and your company. And you know that you can actually be a lot of the value for-- So when it's genuine, right? Exactly. Thing you put a nice hit on the nail here. Yeah, genuine. Also, you're just so gosh darn charming. That's nice. So are you. And that's probably why you've got the lead role in the US office. You want to bring about that? I saw my way over there. Yeah, yeah, no. So we put in a lot of work. I think you and I. And from that, I think the dedication, because we're just so dedicated. I mean, it's Friday, 5.30 and we are still recording. But do you even-- Patrick, do you consider it as to be work right now? It's hard to consider this one. Hide the beer that you have right now in your hand. And try. For the audience, I am not currently drinking. I think I've had enough for a lifetime. Yesterday. OK. Yeah, so essentially getting this opportunity to be a team lead of a new location. And you and I are in the same boat here is a great opportunity, because it's not only validates what we have been doing, but also looking into the future. Hey, we're doing a good enough job to be able to share this knowledge and get some people on the same boat. And in the same direction that we were on once in, right? Just for context, both Patrick and myself we've been kind of promoted internally into this leading roles of the new offices of processing. So Patrick has the privilege of leading the US team in our growing office in Austin, Texas. And I took the liberty in leading the check office in Prague, the town of my heart. So being home and having this opportunity to build up a data science team here is just a huge for me. And I really don't take it for granted and I really, really enjoy that. Absolutely. It's one of those things that I very much enjoy doing. Also, I mean, being in Austin, Texas, it's so different to Germany. And it's an adventure, really. And one that I'm happy and proud of to be part of, you know? What do you enjoy about like being in this role so far? Like having to not only lead people, but also take care of an actual whole new location? Yeah, I mean, there's a lot of stuff that comes along with it that you just never thought about, right? So all the things that you need to take care of and everybody that's done something similar knows this. So you just start to have the dealing with stuff that you never really thought about. And it's being open-minded and being dynamic in what you do on a day to day. It can change like from an email, right? All of a sudden your whole day changes and you must rearrange all your plans and be a little bit quick on your feet, right? So that's one of those things that I think was. I didn't, I wouldn't say it's surprising because I kind of knew what was coming a little bit, but not to the extent of it, but it's just a lot of stuff that I need to deal with. I know you as well that, you know, a year ago, you wouldn't really think of doing. Yeah, I mean, just to think how much of a hassle it was to create an official legal entity, like a daughter company in Czech Republic. And you're thinking, wow, it's European Union, it must be so easy. Well, it's not. It's pretty darn difficult and going through this process while you still have to take care of the customers and record podcasts so that you guys have something to listen to. It takes probably a lot of hair of your head, right? Stop talking about my hair man. Just get over it, all right? Get over it. I'm so sorry man, but it's just, you know, it's right on my eyes right now. Yeah, yeah, yeah. So speaking, you mentioned the podcast. I think this is another one of those that we can brag about and, you know, you do listeners are very much part of this what we consider a success. A podcast that has now over, how many downloads? Yeah, I think by the time this really, this will get released, it will be over 11,000. That's wild right now. One second, I will tell you the exact number right now. So as of what date is today? Today's the 29th of April. Oh, so we are already over 11,000. So we have on 11,230 and four. That's a nice number. One to three far. Yeah, nice. Look at that. So, I mean, this is one of those things when, you know, when we started it, we, I think I've said this three times on the podcast already, like, you know, who's going to listen, like, whatever, it's processed money. And all of a sudden, we're 11,000 downloads in. And it's still, it stopped being a number that you can visualize. And now it's just nebulous, right? It's just 11,000. Okay, what is 11,000 of? And you start picturing 11,000 of anything. And you're struggling, right? And now the thing that I was, I thought was so interesting is the listen through rate, right? Yeah, it's pretty high. That's very, very high. And I'm struggling to listen to myself from, like, when you hear a recording of yourself, maybe five minutes or something. Whoa. If it makes you feel any better, I'll struggle to listen to yourself as well. I just want this to be over. You know, I talked to you in this podcast. So often, I'm just having enough. Jesus. No, so that is so surprising that people really take the time and listen to the things we say. And not to say that we don't have valuable things to say, but it's still one of those things that you're like, wow, people care. Yeah, before that, we thank you very, very much. It turns out that you are our biggest success. I mean, yeah, exactly. So, no, really, this is it from, I think I also speak for you, Jacob, that dear listeners, this has been a wild journey for both you, Jacob and I. And we thank you for your listening. Patrick, since this is kind of a different episode, do you want to do the ending for ones? Oh, no. Today, out of all days, out of all days. Oh, boy. Yeah, so dear listeners, thank you for getting to the end of this episode. I hope you have enjoyed our ramblings and our little braggadocious ramblings that we have just done. And if you want to reach out to us, we are very active on LinkedIn. You can also find us on miningyourbusinesspodcast.com and additionally, what else do you say, normally? I think that's basically it. Yeah, okay, great. We love you. All right. Then this episode will be released at some point, right? At some point, for sure. Well, we will see. Maybe we reassess and see. Maybe we said too much. Maybe this is too much of a rambling, but we, it takes so much time of recording that we probably just going to release it anyway. Yeah, exactly. Okay, fantastic. Oh, now you get to do the intro. Oh, because now you tell everyone that we do the intros only after we do the recording. It's a big reveal now. No, I've, I've, I've. You're just a bubble. Oh, no, all right. Thank you very much. Thank you for listening to our show and we will be looking forward to hear from you and you know, talk to you with the next episode of miningyourbusinesspodcast.com. Patrick, bye-bye. See you. (upbeat music)