5 Crazy Process Mining Ideas

The "Mining Your Business" podcast is back with yet another solo episode. Now, how are you doing, Jakob? I'm doing very, very nice Patrick, thank you. I'm not sure about you, man, but in my free time, I totally daydream about process mining and all the things that it could be applied for, don't you? Um, no. From realistic to plain silly use cases, we have five process ideas that are a little bit more unusual than your typical PTP. Shall we get into it? Let's do it, man. [Music] Hello, everyone. Um, Patrick, so I'm a little bit hungover, and you know what it means. It's time for a solo episode, right? It's time for a solo episode, exactly. This is usually, if you're listening to us for the first time, well, we're not usually recording hungover, but sometimes it just happens. And since Patrick, my dear colleague, is leaving to US, to our US office, actually in like two days from now, right? Yeah, two days. Yeah, so I think he was celebrating with the German office. Unfortunately, I am from Czech Republic and also live here. So I couldn't attend, but Patrick, how was the farewell? And especially how did you enjoy being here in Germany again and now flying back to the US? Um, the farewell is tonight. I had a different engagement in last night. So it's back-to-back, which I am looking forward to, but probably not tomorrow. To answer your question, yeah, it's exciting. Obviously, going back to the US office and seeing all the dear colleagues over there again and spending some quality time with them. Once you get there, so for everyone who listens to us again, for the first time or doesn't know us, we have an office in Austin, Texas. And I've been visiting Patrick before. It's first of all an amazing city, great people in there as well. And what I wanted to say, Patrick, have some taco for me. You know, you know, I will. Yeah. Great tacos in Texas, really. If you never had that, you should probably go to Texas just for that. And I mean it. Yeah, exactly. You know, I knew that as soon as you didn't have a script prepared for the intro, you're going to start rambling about tacos. It's hot coming. Yeah, yeah, it's, you know, everything just ends up with taco. However, I did not write the script that much. I also wanted to say that I'm actually going on holiday in one day. So we are kind of getting ahead with the schedule and with recording before Patrick leaves and everything. And yeah, we wanted to come up with something a little less serious than we usually are. I mean, we were talking about acquisitions of Microsoft recently. We had people from scope that telling us about their process mining initiative. We talked about Biden and all this, all this stuff. And we thought, you know, let's take it a little lighter this time. Let's, let's look at the something interesting, something that doesn't really fit the, fit the fault that much. And we thought for a very long time, actually, I had this idea in my head that we should do an episode about some weird interesting use cases that you wouldn't really thought. Because everybody is just so preoccupied with, you know, with this business processes with me to be ordered to cash, you know, the usual stuff. Yeah, you know, and then I was just talking to Patrick and pitching in this idea. So, dude, do you want to do, do you want to do an episode on accounts payable? Or do you just want to talk about crazy use cases in process mining? And he just looked at me. How are you even asking me this? I think that was the fastest decision I've ever come to. Yeah, so if you guys are interested in accounts payable, I think I have to wait a bit. It's in the pipeline, it's coming, but I think this was an easy, easy, easy choice. And, you know, saying that we will dive into five interesting use cases that we think process mining could be applied. It goes from why don't we do that yet to, okay, this is probably stupid, but it's just so much fun that we want to discuss it anyway. Exactly. Why aren't they doing, that's why they're not doing it. Yeah, also the difficulty could be all, you know, through the roof for some cases. But we will talk about it anyway, because we just want you to get your imagination also, you know, through the roof. So we picked a couple of criteria that we will be discussing for each of these use case and to tell you a bit more about this. So we will start with the obvious question, why is this interesting? Obviously, not everything you want to put into process mining, you know, analysis or dashboards. And that's why we want to kind of brief you in on why this use case that we picked could be interesting and what added value could actually bring. Absolutely. And it should also be noted that the five things that we're talking about came from you guys. So we got some suggestions. And yeah, this is based on that. We obviously added a little bit of our own, but so please get in touch and give us more ideas. We always like to incorporate this into our shows. So. Exactly. So the next criteria will be like what problem could itself because at the end of the day, you don't do process mining just for the sake of it, right? You want to kind of get some interesting insights into problem that you're trying to solve. The next one, that's problem or technical. What would be the case ID? So for you, what's the one thing that we're following through the process? What's the one thing that's building a process? What's related to the what the activities are relating to? What's the central core case that we're looking at exactly? That would be your invoice, your purchase order, your sales order or you'll know it. Use cases. Obviously, once you have the process mining in place, you want to you want to take a look at what would it actually bring? What would you measure? How would you build your reports and around what? So that's definitely interesting and what might be also interesting are the dimensions. Again, you will hear our ideas about that because if you're looking at the standard business process, you will split it by business organization, by companies and whatnot. And in our use cases, the splits might be even more entertaining. Yes. And of course, there are some challenges with these use cases. Of course, they're a little bit out there. So one, there's an unfamiliarity with getting data into these structures that we're not familiar with. And obviously, there's some overall difficulty associated with just how you get the data. That's always a challenge with the typical ERP systems. This is fairly easy. But with the use cases that we're going to be talking about, this could be a little bit more difficult to do. And obviously, the last part, who would pay for it? Because at the end of the day, having a process mining initiative is a big endeavor. So obviously, it needs to pay off or someone needs to fund it, someone needs to think that this is worth it to do. So the question is, who would pay for it? Yeah, but we will leave these questions, especially with the overall difficulty and with the challenges to academia. I know that there are a lot of academics listening to us. Maybe they'll get some ideas for their thesis. And you know, who would pay for it? We all know that there are free process mining tools also in the market. We interviewed at least one of the founders of such such tool. So yeah, just go learn Python and use process mining tools that are already available. Absolutely. Shall we kick it off? We shall Patrick. What's the first one? The first one is American football. The kickoff to touchdown or however you like to call it, we were thinking about looking at the American football genre and in there looking at individual ball movements. Can you explain a little bit? Oh, yeah. So first of all, this idea actually came from our colleague Nicholas Müller. He was also on the podcast all the way back all those episodes back. But I think this is for me, this is like, I would say no brainer in terms of why this is not a thing yet. So if you ever followed American sports and be a basketball, be baseball or American football, you would quickly find out how crazy Americans are about data. They are enalaising everything. Seriously, everything. Yeah, exactly. This is the third dunk from a person named Marcus who has a grandmother called Dorothy who was born in Connecticut or something like that. That's to the, like, that you can't believe, right? So there's a ton, a ton of data. And also why I think it's so interesting is that there's also so much money in the sport, right? And teams are already spending tons on data analysis and trying to get the edge if you can kind of see a quality or an insight that could give you like a 2% edge in some play. I mean, when you do it, 100% and I just wanted to mention speaking of this data, I remember last year and last season there was this. I think a guy played a guy with the same name. There was a quarter back and then some some defensive player and there was a statistics that this is the first time ever that the guy with the same names got like all these, you know, fumble and the tackle and big and everything. The defensive player just did everything for the quarter back, which is just crazy. Crazy likelihood. Anyhow, speaking of the white's interesting. So I think I was trying to do a little research on that and I didn't find any, any paper or any study on application of process finding in American football. I did find a study on European soccer on European football, but not American one. Why? Well, one of the reasons I was thinking was that maybe, you know, process finding kind of rooted and started in Europe and it's just getting us. So maybe they just didn't jump on that just yet or maybe it's just not really out on the internet, who knows. However, the interesting part for me is that as we mentioned the guys in the US, the analytics, they love their data. And I think all the data is pretty much already available. And how I picture this is that you would basically track every action in offensive action in in the league. So what would be the case ID for me would be the ball movement, you know, if you ever watched American football, a cool attribute pickups picks up the ball or doesn't actually that's also one of the one of the cases. Yeah, also can happen. And you would basically track whatever, whatever happens to the ball from the moment the referee or the snap stars. So from the moment the center snaps the ball to the water back all the way until it's end of the play. And you know, this is when they are interesting because when you're attacking, you basically have three downs. So this is one of your first first dimensions, you would see like, what is your first down happy path, what is your second down happy path, what is your third down happy path. And then you snap the ball or your process starts and then you see, okay, water back picks up the ball. He and then all this variation start like in 70% of cases, he just handed over to his running back. However, in 30 or 25% of cases, he's actually throwing the ball. And then it just that the tree just goes crazy because if he's throwing the ball, he can he can actually, you know, he can be tackled and go down. He can also throw the ball and it would result in an incomplete or he throws the boat to a wise receiver who picks it up and just, you know, runs for the touchdown. And then, you know, your ideal path is that you every action should end up with a touchdown, right? That's, that's the perfect scenario, unless you have one minute to go in the game and you're up 20 and you just want to do the time to run out. Yeah, absolutely. But I think this is also really, really interesting because in this case, I think the outcomes or weighing the outcomes of these specific paths is incredibly important because there you might look at, okay, I have 99% go through through this path, but it's the 1% outliers where, for example, the center snaps, quarterback, fumbles the ball. The defense recovers and you have a touchdown scored on yourself, right? Or you have a safety or something, something that doesn't usually happen, but it is detrimental to any drive in the game, right? So it's that 80, 80, 20 rule, but like a little bit more specific, right? So looking at these outliers and maybe also what contributed to these outliers could be very interesting to look at. Yeah, I think if you were analyzing the dimensions that you're looking at, and I already mentioned like the number of down that you're playing, but also a quarter, if you know American football is played in quarters. And as you are closing out on the game, the stakes get higher and you know, the pressure gets on. So maybe you'll be interesting also to look at, okay, maybe against this specific team on a home game. They are specifically vulnerable at the end of second quarter, because their focus just goes down and they just want to go to the locker rooms already. And maybe if we expose them for this specific action, because you know, their defense is already or worn out, this could be resulting in a higher conversion ratio. And this is ultimately this 2% edge battery that you were mentioning and these dimensions, I can think of so many you could look at the. It's so interesting because you wrote down QBRB wide receiver and I think it's it's a really good point because there's such a vast amount of combinations of players that you can play. I mean your quarterbacks are usually going to be the same person, right, but you can always see when players are a little bit, you know, they've run like seven downs already and they're tired, you switch them out, right. What combination of players and what combination of plays works well against this particular defense setup, right. So there's a whole bunch of variability in every single play that you do. But thankfully there are so many plays that are happening every season that you can probably for or synthesize some sort of trend out there and figuring that optimum combination that optimum play to run in that moment in these weather conditions in your home stadium or away, right. Getting all these dimensions and figuring out if I do this, I have a 2% better chance of making it seven more yards than if I would to do this play, right. No brainer. We super interesting it might be difficult to translate this into like follow up seasons because the teams just change so much. But you could make a point that if you were looking at say at the performance of a quarterback over specific water back and just measured, let's say that you only measure his performance over the years and see how he evolves. You could see like from year to year, whether he inclines to running a specific play, whether he inclines to let's say, you know, in process, money can measure the throughput times whether the time that he holds the ball after the snap is after certain certain point is just detrimental to the result of the whole play. Maybe he just should should do something with it a fourth of the second faster or something. And that could also lead to maybe saying, well, your wide receivers aren't being getting open down the field so he has less or more time to really find one that could result in more sacks or he needs to scramble out of the pocket more and there's a whole bunch of. And leading or effects that come from such a simple calculation, okay, my throughput time from snap to throw is longer in like you be than most teams that has consequences. I mean, I would love to see process lining use case on NFL. I think honestly think that this is the future. Of course you do. Please don't quote me on that. And if you, you know, if you think this is also a great idea and eventually put some effort into it and trying to come up with something, just let us know because I would I would so much love to see this. And then I would be like, I told you so guys was the great idea. Now, we should probably talk about the difficulty as well. I mean, it is actually surprising how much data there is out there. I mean, you can track player stats all the way from their college days all the way to right now. The games also have up to date really real time data that is being recorded about these games, right. And obviously not to mention all the replays that you can watch to really, you know, scrub the data and actually get this stuff out of there. So the access to data and I think it's already in a pretty good spot because like we already mentioned data analysis does take place for for large teams and things like that already. So this wouldn't be that big of a stretch to do, I think. Yeah, I 100% agree again, the obsession with the data is already in there and we would just feed them with yet another way that they do look at it. Again, if you saw the movie Moneyball, it's actually about baseball, but I guess you get a point. Yeah, it's again, just a new way of looking at the data and who's to say that this wouldn't be successful. And again, 2% in these 2% edge or maybe even less is still an edge that in such a competitive environment where the teams are basically all equal. And at any point of time, anything can be any other team. We see it every season. This could make all the difference. So in my opinion, it's just a measure of time before somebody introduces it and then everybody will be like, why didn't we do it earlier? Yeah, absolutely, absolutely. So I think there's definitely a use case for it. There's definitely a strategic value from it and so. Yeah, and before we move to the next one, I also think that this idea of process mining could be applied to many sports. Actually, I can think of tennis, you know, every, every surf. I'm sorry, I'm so good on tennis terminology, but every surf could be a specific process in soccer maybe as well. So if you, let's say, if you start with every, every, every game or something and see what do you end up with, you know, could be also interesting. Absolutely. I mean, there's some that are not suited like track and field, like the 100 meter dash, like gun goes off. And there's a throughput time of 9.86 seconds before you're in the goal and yay. Yeah, you know, anyway, I love that the data analytics is just so much rooted into sports and that sports is producing so many interesting data sets. That's, yeah, it's crazy. And I'm just looking forward for these technologies to be applied there as well. Absolutely. Shall we move on to the next one? Let's do it. I think we spent a big chunk of our episodes already in a fellow. Yeah. So the next topic we wanted to talk about and that's also dear to my heart is music production, right? So what is music production really entailed? Well, it usually involves an artist, it revolves, it involves some sort of recording session and some sort of mixing and studio, you know, magic. And then some sort of release, but usually coupled with some marketing, some merchandise and things like that, you know, that's, I don't know, I'm being very, very productive here, but that's usually the core of it, right? So why is it interesting? Well, we would like to know, I mean, every big artist or every artist in every studio would like to know, hey, I'm spending a lot of money making the song. I would like it to be successful. Is there anything that could tell me if a song will become successful or not? Right? Right. That's why it's interesting. So what you're saying is that you would be trying to optimize your way of releasing your music so that it makes the biggest possible splash on the market. Correct. Correct. So I mean, this is, this is obviously a very subjective field music is very subjective in its nature. So figuring out the release strategy is one part, but also song structure. Right? You could have that as dimensions, like what type of song am I writing? Am I writing a big love ballad? Am I writing a summer jam? Am I writing a country song? You know, it could be anything, right? But in figuring out what to do in these specific cases, to make it a success is, is I think pretty big. So it should basically confirm my theory that if you release a Christmas song in the middle of the summer, you might not really succeed. Yeah. Yeah. I think that you're onto something here. I think we do a little bit deeper on that. Dude, it's July. And it's literally is just July right now. So exactly. So get ready for Jakub's Christmas jingles. Yeah, I think I think maybe we can find a loop in the market. Maybe if we just do that, people get excited. They're like, I like times. Yeah. It's just six months since Christmas. Actually, well, seven, but well, I mean, then again, this is one of those things. You know the cliche. You go into super market in November, October, and they're already starting to blast Christmas music. So there's it's not just, hey, it's December. It's time for Christmas. No, no, this starts way beforehand, right? And if you want to crunch out a turnout, a Christmas song, when do you need to start by latest? Like how long does it take to write a Christmas song? I mean, this is one of those things. You really want the artist to take their time with it or you just want to crank out the most formulaic Christmas jingle you've ever heard that'll trend, right? Yeah, it would be also interesting to categorize this into different packets like who, what, what, first of all, I mean the music style. That's the obvious one, but also like, you know, in standard business processes, you always have these amounts. So, you know, what is the total amount of an invoice or something? And here you could, for instance, measure a number of lessons or another dimension for interest that comes into my head is like whether the music group is already established or whether it's their first ever song. What market they are in, I think that the song production will be very different, you know, from Germany, Czech Republic, all the way to, I don't know, Vietnam or something. So, these types of inputs would be vital for the production. You could also, for example, look at what production company is helping you with because maybe some are more likely to succeed and not obviously to probably go hand in hand at the bigger the music group, the more established the music group, the more the lessons they get because they already are on the market. But would be very interesting to see are those, those rare cases, those rare unicorns that produce the first song and the song just goes viral. That's exactly what I would like to analyze. Like, what is the magic, what are the magic beans for them, right? What is the formula that some songs just got it and some don't and you could argue that the difference between them is known as system on the other hand, you might be missing everything else because, you know, music production is a complex topic. It's not just the song, it's everything else. If you have a strong brand, strong social media, you know, you'll take a picture with some famous guy on Instagram and your song is suddenly going to be trending or, you know, we are living in, in a, in a times of reals and ticktocks and what, what not. And you never know if some famous famous influencer is going to put a stupid video with, with your song in it and your songs just going to go crazy. Yeah, absolutely. And also, I think you'll be surprised to find how many artists have had a failed music career under a completely different name and just paid a lot of money to rebrand and redo and just start over and then make it big, right? So, there's, there are some, some pitfalls here and I think a lot of it has to do with them, of course, money, like how much money can you stick into this? You know, there's a difference between somebody from a, more established in the music industry family and then has the connections versus someone that's making music in their bedroom, right? Yeah, these are two completely different starting points. Yeah, yeah. So, Patrick, who would pay for it? Well, I mean, if you could, if you told the production companies, hey, I have this tool that would let you analyze your entire catalog and with 90% chance, you'll probably hit the Billboard 100 if you, if you find the formula, then, you know, who wouldn't want to pay for that? I mean, there's tons of people that would love to crank out nothing but formulaic, formulaic songs that kind of reach the Billboard top 100. I mean, there's a lot of money in that. Yeah, give us million dollars, we'll do it for you. And the data and some data scientists as well. Yeah, I mean, the only challenge is, of course, breaking down such a subjective, such a subjective thing into, into data, right? Because how do you, why do you love careless whisper? Exactly. You can't really put your finger on it, right? So, it's, it's, it's not really quantifiable in a way. There are some markers, of course, what chord progressions and things like that. You use, but in the end, you don't want to make a tool that just says, build this one song in this exact same formula because songs about are also about exploring and making new things. So, formulaic songs are also maybe not the best. Yeah. Yeah. So, this is music production. I think we should move to the next one before we do Patrick. I'm just going to tell you that I'm not, I'm never going to dance again. I'm so sorry to hear that. All right, up next. Farm yields. And this is actually something that we have been discussing in our second episode about what is process mining to certain degree, right? So, if you remember our what is process mining episode, which interestingly is our most viewed, more, most listened to episode ever by far, like it's, it's crazy. I guess, you could say. Yeah. Yeah. You could say that. Yeah. That's a very nice word. Thank you, Patrick, for being so smart. Stop it. Okay. So, farm yields. Why, why talk about farm yields? Why is that interesting? And what do we mean by farm yields? Right. I just kind of slapped that on there as a, as a title. But what I'm talking about is the whole farming industry. Right. We, we have a limited amount of land. Right. We have limited resource. We have limited water. We want to reduce pesticides and all these things. But at the end of the day, we want to get as much crop yield from our farmlands as possible. Right. So, food production is incredibly vital to any functioning economy in my opinion. It's not really controversial. Yeah, exactly. So, so having that down and figuring out what results in the best yields is obviously a very, very interesting point. What would you actually track in this case? Was it be like the seat or would it be like? Yeah. I mean, the seed would be interesting, just because you could see very interesting things when you do it on a seed level. It's just a problem. It's just data collection on that point. So what I was thinking, you could track plots of farmland. Right. Because farmers have different plots of land that are on different hills and, you know, separated by fences and things like that. And they grow different crops on different fields. Right. So you could track a plot of farmland. Right. And there you could track the sun exposure, the how much you water it, the rainfall over the season, the pesticides that you use the timeline of it all. Right. So when you plant the seed. Right. Because I don't know if you know this, but I mean farmers have like so much knowledge about how they get the most out of their land. Right. Right. But it's like such old timing knowledge. A lot of the times like things that were passed down from generations to generations. So when you see three crows crossing from east to west at 5 a.m. on a Sunday in Easter or something. That's when you know you need to go out and and plant some right. So it's just like really, really old knowledge that is probably accurate. Right. They know what they're doing. They've been doing it for hundreds of years. But maybe there's more quantifiable data that tells us when the optimum time is to really plant the seed. Yeah. Patrick, I do come from a long line of farmers. So I know what you're talking about. I did work in agriculture when I was young. Yeah. Yeah. Yeah. It was a good times. Good time. But I completely get it. And what would be interesting is always I love the time dimension. Right. So how long is the optimal way under what circumstances to get the highest yield? Should you already like best decided today or should you just wait another day or two so that you actually achieve this optimal performance? And I'm sure that there are that there are like stations or research laboratories that are looking at this in a very very lab-like conditions. But with process mining, if you were, if it was possible to track this with some data set and this actually brings me to the challenge because it's not so easy to track the farm, the plots of farmland. And, you know, compile it in a digestible data set. But if you could do that if you figure out some smart way on how to how to tackle this, you could have a sample in a real life and not really in laboratory. So you could actually start making these points about the sunshine, about the rainfall and so on. And look, okay. So maybe if I on this farmland, if I switch every two years, the type of crops that I'm sitting here, maybe is going to result in something better than what I've been doing now with changing it every year. You could also start looking into some, you know, data like what type of crop would you change there? And, you know, eventually just you're looking to optimize the way of what you're going to get out of it. And again, I think this itself makes the case for our farm yield process. Absolutely, absolutely. It's one of those things that I think some of these do and I've seen them do this or talk about it at least where they say, yeah, this grape was grown on this hill and it was harvested this and then, but the grape that's on the next hill. It tastes like so much different because it gets more sun in the evenings and things like that, right? So there's a if they say any if you believe it, if there is such a difference and it really does make a difference in the yields and the flavor and all this things, then it does really make sense to really go that granular into your crops setups. Yeah, I still bet that at the end of the day, you would have like this, this old guy who's been farming for 40 years and you'd be like, this is, this is a secret. Yes, this is complete garbage. Let's just throw it away. I'm going to watch the birds to tell me when to go out and so much. Yeah, what is this? Are you using technology to create a super, you know, borderline or something? Exactly. And I mean, it goes to show like you don't want to obviously replace that knowledge, right? That's worth gold, right? That's what's been feeding us for the last 100 thousands of years, right? So you would likely want to complement that skill set with tools like these, right? And again, farming has been around for most as long as humanity or modern society, which, well, surely allowed it so. But I'm not going to into history because then I could very quickly could be exposed on my like, let's not go there. The point is that farming has been a focus of optimization of humanity for so long, you know, you had all of that. All kinds of improvements over over these hundreds of hundreds of years. And the technology hit it also pretty, pretty hard. I mean, it's not that long when over 50% of all all the population was working in agriculture. And now if I'm not mistaken in developed countries, it's what like less, less, less than 4% probably even less. Which is crazy because, you know, through the technology advances, suddenly we don't need to send hundreds and hundreds of people onto the farmland. Because what we need is one optimized machine or some tractor or, you know, you are, we are starting using these, what's it called, the thing? It's drones, exactly. And why not taking it to the next step with the data onwards? I'm sure that there is already a bunch of ways that they are analyzing their crops. And maybe process mining could be a next line. Oh, that'd be such a cool idea to have an automated drone schedule that just goes and takes pictures of your crops and analyzes like how much they've grown and things like that. This is a thing. This is a lot of things. Let's do it. Yeah, out of interest, I know that there are there are companies that like get these data from the satellites satellites. And they can like analyze the, you know, some some scenarios of countries and how well the crops going to go that year because of how the how the visuals look like. And then they can make like some bets on it in terms of I don't know trading or something, you know, what's going to be a price of a bushel of corn or so. And this is just crazy like there are so many possibilities with the data and what you could do with it that just I think my tiny little brain just can't really work with it. I think it's also important to mention, you know, due to climate change, the crop yields and things will be more of a thing, you know, the soil will be stressed harder. The crops will need to go through higher temperatures. And so, you know, having that extra edge that might get you through some incredibly dry periods or something would also probably be really, really helpful. But at the same time, look at the countries like Netherlands. I think Netherlands is one of the biggest producer of of vegetables in the world, which is, you know, just look at how, yeah, Netherlands, dude. Okay, I learned so much on this podcast. Yeah, dude, farming and you know, this because they are using the technical advancement for their own good. And maybe they're also using process mining who knows what's in the last is up to in the German. I wouldn't be surprised. We're watching you. Shall we move on? Let's move on. Alright, so this one's a bit of a somber one. We were thinking about in the field of sociology might maybe looking at the life to death process would be fairly interesting. I mean, humans are such a such weird creatures and they go through a whole host of different activities. You could call it, right? So different parts of their life, you know, school and work and, you know, you, you marry or you have a partner or you don't and, you know, that results in different paths in your life, right? There's a whole, I mean, it's incredibly complex, right? So there's not one thing that you could really point to and say this will predict your life or anything, but there are probably a lot of common denominators in all of our lives that we might want to look at. What would the happy past look like? Again, also very subjective. You probably want a very long cycle time, you know, as long as you can get it really. Yeah, that would be a good happy path, you know. Yeah, this is, this is just really an idea. I think it's a little, it's just a wild idea that it would be pretty cool to observe some relations. Like what does happen when you do a certain turn in life? And I'm not necessarily talking about, you know, taking or doing some hard drugs, but maybe, you know, how likely you are to end up, I don't know, studying in a new university based on not necessarily the country because they're obviously statistics for that. But in a city or in, let's say, how many brothers or sisters you got to have to do a certain certain step in life to, again, maybe the university success ratio and stuff like that. So you could, if you, if you don't look at it from the wide perspective that you're really tracking everything that happens from the moment you're born until the moment you die, and you look for some specific sociological use cases of analyzing the life, key choices and the impact on them or impact on your overall happiness or likelihood of becoming something specific or actually not ending up in some specific situation. This could be very interesting. Absolutely. Absolutely. I think one of the things that would be interesting would be looking at rework rate and like how many times do you go through specific activities. And I know we were joking about this before the show, but if you reach the death activity and you accidentally do that twice because they mistakenly buried you or something, I think that would be funny outlier to see. Yeah, 100% so they we have here are Mr. Jesus who died and it's just starting to walk again. What's up with that? Can you look at that case? So at the age of 33. That's interesting. Yeah, crucified and then three days later emerges from a cave. I mean, that's that's a crazy. So I guess when you crucify a person, they're just going to stop where this is going. We thought you this is going to be a wild one. Yeah, exactly. Right. I think it's I think it's a very interesting use case just but I mean specifically if you wouldn't if you're looking at it from a governmental side. Now, if you were to imagine how do I get my my constituents to to study write a well educated populace has been official to everybody when they say medicine or anything else really. So how do I get them to do that? Is there a specific sequence of when you introduce kids to math or something that kind of determines what feels their interest. Yeah, I mean, you could analyze this forward some backwards a thousand times. So imagine you start using this to optimize Texas or something. Oh, and by the way, speaking of Texas and government, one of the use cases that didn't make the cut was actually my idea on looking at the pools. So whenever you know, you're yeah, you're choosing the new government poll polls got it. Sorry, I am really my pronunciation on this one is just. You know, English is hard. And I was I was a joking. I guess now it's not so funny because of the war and everything, but you know, when you have a polls in a country with the dictatorship, you could just look at those manual touches after. The the the the rooms are close, right. So here are books of the of the votes was opened manually touched and there were some changes from from A to B. What's up with that? Yeah, I'm not sure dictators would appreciate that level of transparency. Yeah, but it's still correct me up. And shall we go on to our final? Oh, yeah. So I know you're waiting for this. I am waiting for it. You were texting me last night and giggling. So I'll be honest, I really went wild on this one. Don't be offended. Maybe we should make this episode explicit. I don't know. Let's see about that. But it's it's a dating process, right. So we all are subjected to dating other, you know, men, women, whatever. And I think it would be so interesting to just analyze this process from start to end. Like use the sample who use the data set of all the dates that are happening throughout the world and try to find and just look in the patterns and it would be just so, so funny to look at these things because, you know, your case ID would probably be the date or a partner depends really. I think the date would be you could make points for both because the partner then you see how many dates he went on and so on. But if it's just a date. Yeah, I think that'd be interesting because well, you could look at how you go from if you date someone, how that like that, how that goes right, but what if you date multiple people at the same time. But then you would have, then you would have like multiple cases for your person. So imagine that your dimension, there would be a, you know, Patrick Bogner and you would select the cases, you know, that the Patrick Bogner goes on. And then the case ID would consist of you and the case ID of your partner and you would have like multiple cases there and you could see, okay, how successful you are there, you would see that one only case that you have, right. I know this is going to end in ridicule. Yeah, but I hope you're following us on this one and I had so many idea that you could, you could like like measure. I was, when I actually came up with this idea, I was yesterday in a cafe when I was after work preparing this episode and there was a table of two young ladies sitting next to me and they were just giggling all the time they were drinking, I think some drinks and everything. And at some point, they just went on on Tinder, the dating app and they were just scrolling through guys and making stupid comments and I just couldn't stop laughing, right. And then I thought, this is a great idea. You could make, you could make a swipe to sex process out of this. Yeah, just hang with me and okay, I mean, it's as funny as it is, I think it brings up a good point. You know, dating and marriage and just general partners in life, right. I think we could all agree that most people seek companionship in whatever form that may be and having some sort of analytical insight about what determines the success of it. The success and failure of these partnerships is, I think, from a sociological point of view, very interesting. Yeah. And maybe not call it swipe to sex. I think, Patrick, you're trying to turn this too much. Yeah, I think I was trying to not have the explicit flag on Spotify. Yeah, but overall, again, the throughput times, what is the impact of, what is the impact of someone waiting with a certain activity for a certain time? The dimensions, right. You could finally make a point whether money or looks actual in matter or whether it's something else. I can take this one. They do. How would you know? Well, you know, being so wealthy and so good looking, I can definitely confirm this is true. I think I can't believe you put birth sign on this list. The first thing that came up to my mind because I was just discussing it with someone the day before and she was asking me what birth sign. Why would you care? That's even better. Yeah, but I imagine that it was actually significant, right? Yeah, maybe Taurus is really do go well with Libras or something. Who knows? Yeah, or other things. Obviously, H is the easy one. The common interests you have together. So maybe, maybe if you are just like the other, maybe it's not the best thing to date or who knows. Or the source of the date, I already mentioned Tinder, maybe, you know, you would find out that dating colleagues, not the best thing. I think it's also interesting to, for governments, right, because you can see it in a lot of governments having population declines. And there's initiatives to try and get people to date, you know, and, you know, have kids and things like that, right? So there's an actual use case for this in a way. How do you get people to date? How do you get people to successful dates, right? Yeah. This is actually a, as funny as this is. This is actually, I think, a very good use case. Yeah, I think by talking about applying dating in process mining is not going to increase our chances on this. No, no. So I think one of the challenges that I wanted to highlight, because this, this came to my mind, as I was reading this, and as you were texting me, I was thinking about tracking this data. I mean, this is obviously a massive invasion of privacy, if I've ever seen a little bit. I mean, also, if you just do a personal tracking, just imagine that I'm sorry, honey, I'm sorry, I have to interrupt this the session. I must log my kiss and my B.I. tool, you know, it's not that, it's not that, you know, doesn't really set the mood. Yeah, you would just anonymize it and it would be fine. Yeah, of course, of course, people would love their government to have some sort of information about their, about their very intimate details. Oh, man, I really, really like this use case, and I think it would just be hilarious tracking this. However, we also came up with some other use cases that didn't really make the cut into discussing them into the depth, such as some of them are actually already being implemented. There are the environmental processes, like what impact does, I don't know, your supply chain has on a certain, on a environmental criteria. Yeah, we already mentioned that the government wasting our money, what's the process of that? Maybe that would be, there's a lot of interest in the process of a government wasting our money. Yeah, especially coming from the governments, right? Yeah. Yeah, I mean, we already, I would love to actually talk to someone who's applying process mining in a public sector, because it's, you know, it's hugely inefficient, and I think it would be great, because the data is also there. I mean, they're using ERP systems too, and analyzing this thing would be tremendous and very, very insightful compared to, you know, private companies that are doing everything possible to optimize their processes. Maybe governmental subsidiaries could also think about something like that. For sure, I think anybody who has been in, and I don't want to dig at anybody here, I'm sure there's reasons for this, but in the, the German governmental system or signing up for a, when you go and live somewhere new or something, you have to change your address, and that whole process, you wouldn't think should take as long as it does, but it does. Right, and it's, it's very frustrating to do right, you have to waste four hours every day waiting in line, getting a ticket and all these things, and you're wondering why is this so inefficient, right? I'm sure there's some reason, but I'm fairly certain since there is no economic incentive really to optimize that a lot of optimization has been left in the dust. Yeah, and I mean, we've all been there, you know, you are waiting in a queue at some public space, and then, you know, you, you finalize your turn and they say, yeah, we don't do this here, you have to go to the other, the other building on the other side of the town, where you're going to, you know, take this form and fill it up, and then you can come back to us because before that we can't really help you, like, are you kidding? Yeah, I just spend here four hours waiting in a queue, and you tell me I have to do it over and over again, it's ridiculous. Absolutely, yeah. It's one of those things that also drive, I think drives a lot of people crazy, just the amount of paperwork, you know, we were living in 2022, and we're still doing a lot of things by paper when we really shouldn't, well, one, it's wasteful, and it's also inefficient. Yeah, cause for some OCR tool, well, you, we know the trouble with OCR tools, anyhow, what I'm trying to say here is that there is just so many different things that you could be measuring and applying process mining for. I recently saw this, this, how's it pronounced poll, poll, poll, sorry for that, a poll on on Salinas LinkedIn profile, where they were asking people how long they've been in process mining for, and there was this very small subset, less than 1% of people or, you know, respondents who set one more than 10 years, then about 5% was between 5 and 10 years, and then everyone else was in process mining. For less than 5 years, I'm starting my year 5 actually in a week, and what I'm trying to say here is that there will be more and more and more use cases where process mining is going to find its way to, because it's just a unique way of looking to the data, the company's already understand that, and if we think about it almost anything that we're doing in our daily lives, it's some kind of a process. I mean, how are you doing a shopping, right? How are you cooking? I was thinking about this yesterday, actually, when preparing for this episode, I was thinking, what if we do like a cooking process, but then I realized it's very close to like a production process in a company, you need to check that you have stuff in your warehouse, aka your fridge, then you need to go to the purchasing department, you know, go to the shopping and actually buy the stuff that you need, and yeah, it turned out to be very close to what companies are already doing. Exactly. And almost any electronic device, I know that some of the companies are already experimenting with, implementing this process mining capabilities into a trying devices as a starting point so that they can, you know, if something goes wrong, they can actually look into the logs and find out what was happening there. And I think it will be more and more present in any data set in any way that we work with the data, and it will just be this category that will accompany us on any data analytics journey that eventually process mining will be implemented wherever and whenever it makes a little sense. Yeah, you nailed it. You nailed it. And I think this is where you, dear listeners, come in. We would love to hear your ideas, right? So we have five ones that we, that we just came up with, but we would love to hear from you. What are your interesting takes on what process mining could be used on, right? Because everyone has some hilarious idea or maybe some really, really interesting one that just hasn't been done. So we would love to hear from you and love to see what you can come up with. Yeah, definitely take us on LinkedIn and write us what is your idea we would love to hear that or just comment under the, under the link that we are posting ourselves or just write us an email mining your business podcast at gmail.com and we would very, very much love to hear from you. However, this is the end of the episode. I hope you had at least half as much fun as we did because this was doubtful, doubtfully. This was brilliant. And if you made it all the way through up till here, thank you for that. Thank you for listening. Leave us a review on any tool that you're using and we'll be looking forward to hear from you and we'll be here back in two weeks time with yet another episode of mining your business podcast. Thank you. Thank you. Thank you very much for listening to us and please stay with us. Bye bye. [Music]