What happens behind the scenes to create a visual data disaster? Data visualization is often taken as a given once you land buckets of data together. This week we go behind the scenes of a data disaster to learn why that is not so. Felix Schildorfer tells us a wonderful story rife with political maneuvering, management cheerleading, IP sabotage, cross-functional politics and data not actually owned by the company and ultimately how he won in the end. His story has more twists and turns than a Bay Area detour. Learn what he did to deliver the ultimate outcome: a visualization dashboard loved and used that supports real business value. Please help me spread the word about Customer Centric analytics. Rate and review my podcast on iTunes and tell me what you think by writing Allison at email@example.com or ambitiondata.com. Thanks for listening! Tell a friend!
Connect with Felix at Felix.firstname.lastname@example.org
Key Concepts: Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research
Who Should Listen: CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers
Allison Hartsoe - 00:01 - This is the Customer Equity Accelerator, a weekly show for marketing executives who need to accelerate customer-centric thinking and digital maturity. I'm your host, Allison Hartsoe of Ambition Data. This show features innovative guests who share quick winds on how to improve your bottom line while creating happier, more valuable customers. Ready to accelerate? Let's go!
Welcome, everyone. Today's show continues our visual data disasters theme with a look behind the curtain at the people and processes that feed the creation of a full-scale visual data disaster to help us get the inside scoop on this complicated topic is Felix Schlidorfer. Felix is the principal data scientist at first retail. Felix, welcome to the show.
Felix Schlidorfer - 00:59 - Thanks a lot for having me, Allison.
Allison Hartsoe - 01:00 - Tell us a little bit more about your background and how did you end up in visual data in the first place? Because I know like me, I, you know, I didn't study this in school. How did you end up here?
Felix Schlidorfer - 01:13 - So, um, my background is a bit complicated, so I'll try to summarize it as best as I can. I'm currently living and working in Vienna, Austria, which is also where I grew up and went to high school. I was always a very big guy. Currently in about 6'4", 230 but I've been up to 290 before and a pretty good football player. Football actually means American football.
Allison Hartsoe - 01:36 - So you're not the typical like, you know, I visualize a data scientist with nerdy glasses and you know, a little guy in a cubicle, are you?
Felix Schlidorfer - 01:44 - Oh no, no I can. I care. Lots of people tend to be very surprised when I walked in.
Allison Hartsoe - 01:49 - I can bench two and someone in each charm
Felix Schlidorfer - 01:54 - basically and I am a nerd, so um tend to catch people off guard. I was pretty good American football and new data, and you want to play college football, so after a lot of work and the role of backstory don't need to get into. I actually got recruited by the University of Pennsylvania. I graduated with a double major in mathematics and economics and statistics. So you can imagine there's a lot of visualization in economics and statistics already. So that's kinda where it all got started anyway. On to back to Europe. To gain more experience in the field and um, that's so I kind of started working at different data science projects and then later on kind of end to end data visualizations as well. I'm trying to talk about later. Um, but actually I'm already preparing to move back to Silicon Valley this fall. So that's pretty exciting.
Allison Hartsoe - 02:53 - Yes. But we would love having you here on the West Coast for sure. Can you tell us a little bit about the work you specifically do as a data scientist? I think most people who listened to the show understand data science, but there's such a broad definition of the, within the work that people do as data scientists. What do you specifically do?
Felix Schlidorfer - 03:14 - Sure. Um, I have done data science work. I've worked in banking insurance, and currently, I'm in healthcare, but I'm not sure that everybody would agree that my most recent work is strictly data science, but I feel like it, it's included in the general sphere. So most of them are recent projects. How should you wish data architecture and data visualizations? To be more specific, most of my current projects or what you would call an end to end project, meaning my responsibility starts with going to the end user, see what kind of data product he wants and then going all the way back to that data source and seeing what's available and what should be available and what can be done.
Felix Schlidorfer - 04:01 - And um, most of my work is actually building up the entire infrastructure and processes that enable to enable that data to travel from the data source to the end user and then kind of appear in front of the end user in the most, the most useful and kinda create a functional end product that the customer can be.
Allison Hartsoe - 04:30 - It's and just for the listeners of the show, we often talk about customer equity, and I'm getting that information about your customers to be surfaced inside the organization. And so the reason I invited you, Felix, to the show particularly is because with that end to end project, would that end to end project capability, what you're actually doing is stepping on one of the most important features that I hear from chief analytics officers, which is you've got to be able to communicate the winds that you have around the customer and data visualization is a big element of that. We often think that you can pull all this information together and it just kind of happens, but it's a lot like a garden and that what you plant is what you get on the other side.
Allison Hartsoe - 05:27 - And so for the listeners of this show, I, I really thought it was interesting how you, you really can build success or failure out of those, those initial pieces that are planted in the data visualization seeds. And maybe you can elaborate a little bit on that as far as why, why a visual data disaster needs a little bit more planning? Um, you know, why is it important to care about this earlier, a creation of the disaster? Because hey, you know, it's, it's all, it seems like you can just get to visualization if you just have the data. I think most people say, Oh, I've got the data, I'm going to slam it together. Um, why is the end to end prospect so important and why are the seeds that go into it so challenging?
Felix Schlidorfer - 06:28 - So I think there is a very hard and very real problem, especially for really large complex organizations, especially if they have a long history and legacy infrastructure can be incredibly hard to gain those really valuable insights of the data. And often enough companies are flushed with data. They don't know what to do with it. Um, but it's not as useful as they often think. And then blamed it on the general attitude towards data that we used to have. We used to store data to remember it, not to gain insights out of it. Nowadays everyone wants these insights. Everybody sees other people doing it. But unfortunately most organizations, the entire data infrastructure was built with a completely different goal
Allison Hartsoe - 07:24 - Felix, that is a really interesting insight. I don't think I've ever heard anybody put it that way and it's, it's incredibly important. You know, the way we structured it initially is what we expect to get out of it. Nobody expected to get insights out of large legacy systems perhaps.
Felix Schlidorfer - 07:40 - Exactly. And a person in management might hear a lot about the more than uses of data and naturally would want to be a part of it. Um, they didn't want to use all of the data that they know they have to gain extra value in their business. And the first actual step is, of course, to look at it, and that's where they are really starting to realize that, well, it's not that easy and a lot of people tend to be very surprised at how much work goes into work goes into creating displaying valuable insights.
Allison Hartsoe - 08:14 - Now, I, I know we have a great story to tell today about this, um, the creation of a data disaster based on how much work goes in. So let's dive into this example and the story that you personally experienced in trying to create this end to end data visualization for our company.
Felix Schlidorfer - 08:38 - Okay. Yeah. So the story I'm going to tell you about is, um, my project at a large organization of hospitals. So much
Allison Hartsoe - 08:48 - nameless.
Felix Schlidorfer - 08:51 - They were under pressure to reduce their spending. And I'm talking about media politics, there was really a lot going on there, and they were constantly looking for better ways to do so. My initial assignments or was started directly to the central agency which wanted to use data visualizations to make better decisions. They wanted to start with five areas, free medical, surgeries, radiology, and patient care and to general administrative financials and cleaning personal. So I imagined this project. And we were initially told we would get our own sequel database and their general environment was data available where we'll be able to work, do data transformations log in more data and kinda create a proof of concept
Allison Hartsoe - 09:42 - Perfect strategy.
Felix Schlidorfer - 09:44 - Yeah. Uh, at the moment this is recording is, has been over a year ago and we still have not gotten the environment as we were promised back then. There's something but just nothing that we can use as we want it to. Um, so go back to about a year ago, um, and as we'll be starting in the first weeks of his project as we were waiting for his environment, um, we just kinda didn't want to sit around idly, and we wanted to create the dashboards ahead of time. That, of course, is a lot of work because we had to find the correct KPIs, transform the end process with the little data that we had. So
Allison Hartsoe - 10:30 - So, are you basically going on a bring me a rock kind of mission, you know, where the company says, I know there's something interesting in the data, can you bring me a rock? No, not that rock. I want this rock. And you're kind of iterating on what you think they want.
Felix Schlidorfer - 10:43 - Yes, exactly. Um, so what the company really wanted was for us to show them how to use their data best. Unfortunately, they didn't really give as much data to start with. So we kind of started to explore what could be valuable for them. And we started to create data to kind of show these insights. So creating dashboards and data visualizations can be incredibly easy if you have clean data readily available, it's very, very hard work to write data by hand to produce quality visualizations that are not only realistic but also useful that you can use as a proof of concept. And we needed to do that to show what could be done with it.
Allison Hartsoe - 11:38 - So, you need to set the vision to get the momentum and turn away you. You had to build different mock data sets you; you had to create the data or, or where you actually had to get the data through.
Felix Schlidorfer - 11:52 - So we had some old data from prior projects and which were used and we had a and but the rest of the data we had to just kinda write ourselves.
Allison Hartsoe - 12:07 - So. So you try to get the visualization together of here's what it could be, here's what it might look like and that, what did you do with that once you had it?
Felix Schlidorfer - 12:21 - Then once we've created these dashboards and still no sequel environment available at this point, we'd go to the decision makers and we tell them like, here's this proof of concept. We've worked really hard on it, please just let us let us get to the actual data, and we'll make it a reality for you. And they absolutely loved it. I don't know if you've ever seen Power BI and it's really quite impressive, especially if you compare it to legacy systems such as just Excel or once we were using at the company was IBM Cognos. So the decision makers actually loved it, and so they said go ahead, just work with IT, and they'll give you what you need, and at this point, we just hit a wall.
Allison Hartsoe - 13:13 - Wait. You're working with IT, and you hit a wall. I've never heard that before.
Felix Schlidorfer - 13:17 - Yes. So IT says they already have all of this because they do have IBM Cognos and they do have dashboards. They're, they're wondering why are they taking responsibilities away from us? Why are you trying to give us extra responsibilities we won't be paid for. And the. Because the way we kind of worked around this things as well. We see you have your dashboards and I am doing these certain things. Our dashboards are still going to do different things, so we're not actually in any kind of competition.
Allison Hartsoe - 13:48 - and, and I think we've all been in that situation, whether it's with internal partners or whether it's as an external vendor where somebody perceives you as competition when you're actually partners.
Felix Schlidorfer - 14:00 - Yes. So after a little bit of back and forth, we kind of get IT to maybe not be on our side but at least support us and then we can start to realize to why you were so hesitant in the beginning, and it allows the data that we wanted. They didn't really have access to as well. I ended up where it gets.
Allison Hartsoe - 14:25 - They didn't want to tell you, but they didn't have access.
Felix Schlidorfer - 14:28 - I, I think so. Or to maybe just didn't want to think about it because it will make extra work for them. Um, the, the problem is, is that the hospital generates a lot of data and the data is stored in a central data warehouse for them. That's 14 different hospitals, lots of different systems and it's all stored in the same location but not in a unified way. They store in the same location, but it's not all put together in a central database.
Allison Hartsoe - 15:04 - Talk a little bit for a second about what that means because I think there is an assumption that if I slam everything together into an Amazon S3 bucket, that all of a sudden I've got what I need to visualize data.
Felix Schlidorfer - 15:18 - So there are a lot of different hospitals which kind of send in their data and they are sending their data from different systems. So let's say if you are managing surgeries, we have a certain kind of software that collects the data for surgeries. It's not the same software that collects data for radiology appointments. Everybody kinda has their own different hospitals have their own for the same kinds of areas.
Allison Hartsoe - 15:41 - So, granularity is different or the dimensions inside the data are different, or both?
Felix Schlidorfer - 15:45 - Everything is different. Kinda they often the same kinds of data that isn't collected their feet. Even if it's the same, just two different hospitals that are offering the same system for the same area such as surgeries, that kind of the setup of how the data is collected might be different. There might be fields that are, that have to be filled out in one hospital but don't have to be filled in the other or have different names. So surgeries were called different things in every hospital, and all of that data was collected in a central data location. But now that database and location are really; it was really just a physical location with all of their computers lined up next to each other. They were maintained by the different vendors, the data source such as Siemens, SAP and those kinds of companies that are doing that kind of work.
Felix Schlidorfer - 16:43 - So we realized that the data really isn't into IT hands IT's hands for a lot of these systems that we needed. It was actually will list of vendors. So we kind of switched over our strategy and we say let's go and talk to different vendors and see if they can give us ideas.
Allison Hartsoe - 17:02 - So, you streamed with ideas.
Felix Schlidorfer - 17:05 - Yeah. Yes. Um, and they and there we had different success. We, um, some of them were fairly compromised. They didn't quite understand where you were talking to us since we haven't gotten the signed go-ahead from the central agency because they were still looking for approval concept. Um, but there where some are competence. I, um, was usually the air but not particularly helpful. Then there were other vendors that were a complete disaster, namely where we actually sent a request for data to them, and we got 'em a bill back saying, Oh if you want to look at the stage where you just have to pay this much every month.
Allison Hartsoe - 17:55 - Oh, so they thought you were trying to purchase the data.
Felix Schlidorfer - 17:57 - Yes, and actually we found that even though the data was produced in the hospital and was used by the hospital if you want it to have reports from it, the data's still belonged to the vendor.
Allison Hartsoe - 18:13 - That's, that's, that's like, that's like the bank charging you a fee to get to your, your own money, right?
Felix Schlidorfer - 18:21 - Yes, every time you want to check your account, your account balance, you have to pay.
Allison Hartsoe - 18:26 - Oh my gosh, this is the business to be in.
Felix Schlidorfer - 18:31 - And that's what we found out, Oh my God, who signed these contracts was smart enough to give these people these contracts
Allison Hartsoe - 18:41 - and do you just coming back on that, do you think these were contracts that were signed ages ago and someone just didn't realize that that was indeed the case? Like you said in the beginning there, there's that, um, you know, if you're going to use it for storage, you don't really care as much about reporting insuree ad hoc expense for reporting. But if you're going to use it for analysis, then it's a whole different animal on, on getting the data back and actually using it.
Felix Schlidorfer - 19:05 - That is the best example I can think of when talking about infrastructure that was meant for remembrance and that analysis, if somebody looked at this and said, well, we can get our best value if we just store our data. It didn't think about what we will be able to do in the future with that data and what kind of insights they were Kinda missing out on?
Allison Hartsoe - 19:31 - You've got the, IT like your central IT company. Do you think it would have been easier if they had been a stronger partner in reaching out to the vendors? What would this have helped you at all? Or was it simply a, a red herring to kind of deal with them because you know, they didn't actually own the data in the first place.
Felix Schlidorfer - 19:54 - I feel like a lot of things would be a lot easier if IT would have been a stronger partner. I'm just with finding out faster where the issues lied. It was always a guessing game what a new problem was going to pop up and if IT would have been a stronger partner for this moved along faster. Even though a lot of the same problems with subsistent, it would come up a lot quicker.
Allison Hartsoe - 20:23 - Was it the fact that you had to basically dig into that department and discover what was available instead of someone basically saying, here's the menu, here's what we have.
Felix Schlidorfer - 20:35 - Yeah, that's what exactly is that. I'm just to process into work of digging. Took up a lot of time and could have been made a lot easier if we would have had more cooperation.
Allison Hartsoe - 20:49 - Are you talking about weeks or months? How long?
Felix Schlidorfer - 20:52 - We're talking about months.
Allison Hartsoe - 20:53 - Wow. That's a lot of back and forth. Okay. So you're talking to the vendors, you've got data from different machines. What happened then?
Felix Schlidorfer - 21:02 - Um, so we're making progress that we're kind of figuring out what kind of data is available, what isn't, and we're restructuring what we want to do. Um, what's interesting though is as at the same time as people are getting ready for GDPR. Oh Gosh, yes. And um, we're talking about very sensitive data here. So it was extremely relevant to us.
Allison Hartsoe - 21:23 - Sure. Our patient data are always sensitive.
Felix Schlidorfer - 21:25 - Yes. Um, and also displaying patient data, so we have to make sure it was aggregated correctly and we, of course, weren't allowed to touch a lot of it initially. We were asked to touch it at that point, but it wouldn't be later on. So we have to make sure that all the processes are set up correctly. And this is where we kind of changed our approach again, and it seemed to be the real business case here was to create a central functional database and all the necessary pipelines to be, um, by GDPR and that it would actually be functional.
Allison Hartsoe - 22:07 - Wow. So, so basically a modern database.
Felix Schlidorfer - 22:11 - Yes. Um, something that could be used for analysis
Allison Hartsoe - 22:16 - that makes sense. And I mean is, it's back to what you said at the beginning, you have to have the data landed in such a way that it's ready for analysis. So how did you do that?
Felix Schlidorfer - 22:29 - Um, so at this point, this is when we actually sell our first dashboard project.
Allison Hartsoe - 22:37 - Oh my gosh. And how many months is this into from a point where the executives were like, Yay, we want this
Felix Schlidorfer - 22:43 - 6, 7. Ah, it was, it took, I told you it took me months to dig all this out, and this is the first dashboard could. And suddenly I'm in the meeting was sold, and we're talking about different dashboards. Suddenly the next kind of bomb hits, namely the head of IT says, oh, we have all of this. And they're talking about the same KPIs that we invented the same KPI data processes that we use the same visualization instead we've presented to the management to sell this. This is all stuff that didn't have prior when we started this. But now six months later, suddenly they seem to have all of this.
Allison Hartsoe - 23:28 - Oh no. So all of a sudden IT who wasn't your partner all of a sudden is like, you know, Et Tu, Brute? Suddenly turns around and pulls out the knife. Oh my gosh.
Felix Schlidorfer - 23:41 - Uh, they would backtrack on this really quickly though because, uh, this is not just one project that we're working on there. There are lots of projects don't are we're working on organizations. So future cooperation and the good kind of environment is really important. And it was kinda enabled my boss to kind of put his fist and said, no, this is our intellectual property. You do not get just to take it after we presented it to you in goodwill. And that did make him backtrack. But it was a very uncomfortable week.
Allison Hartsoe - 24:19 - Oh my God. So, so your boss did, did he have like a private discussion with the lead of IT or was this more of a public meeting?
Felix Schlidorfer - 24:27 - This was a public meeting. Um, this was a public meeting, and intellectual property and lawyers were discussed. I was in the open. It was rather an ugly thing to be at.
Allison Hartsoe - 24:41 - Wow. So it was like lawyer up baby become, this is our stuff.
Felix Schlidorfer - 24:47 - It was weird. This is our stuff, and we're ready to go the distance for it because we've worked for seven months for it.
Allison Hartsoe - 24:53 - Wow. And then what happened?
Felix Schlidorfer - 24:56 - And then I kinda worked.
Allison Hartsoe - 24:59 - Now, do you think if they, if-if you hadn't been successful, if you hadn't had the resources to basically bring the team and say, hey, this is really our IP. If they had tried to execute it, do you think they would have been successful just following the template of what you laid out?
Felix Schlidorfer - 25:18 - I actually don't think so. I think they kind of saw what we showed them and didn't necessarily know all the work that was behind it and not saying that it wouldn't have been possible for them to do it. I don't think they could have dared on technology that they insisted on using, but they will have a lot harder time than just to pay us. It would take a lot more work to try to replicate everything than to just pay us through what we already did.
Allison Hartsoe - 25:55 - Well, and, and this is kind of the idea of data flow and the dynamic nature of the data is. It's not like you just presented at one point in time and yeah, that might get you part of the way, but it's the landing and the cleaning and the processing and then is that KPI is still valid. There's a lot of dynamic management I think that is overlooked when someone tries to rip off your project.
Felix Schlidorfer - 26:20 - Oh yeah. From then on things kind of smoothed out because, uh, in the next meeting we actually sold two more other dashboards which we're working on currently. So we're currently working on three of them. That's five, but I'm happy with it. It's a work in progress. And um, we're also kind of getting really moving on that central database, data quality starting to heighten. They're starting to report more data that we need, that we want data hygiene and the entire decentralization of the reporting is still an issue, but I feel a lot more positive about being able to tackle it. Admittedly, there were times during the project when I was just like; I'm, I'm just waiting for my boss to call it off. I'm still going to work, but I'm just expecting next week they're going to be a, Oh, okay, this is not going to work. But it never happened. We kept pushing, and it paid off in the end.
Allison Hartsoe - 27:16 - Wow. So do you think it's a little bit like, you know, like you're trying to get some critical momentum going and you know, IT is essentially becoming a bit of a, of a hill and you're pushing this visualization boulder forward and you know, it's harder and harder and harder, but eventually you surmount that hell and you start getting critical mass.
Felix Schlidorfer - 27:40 - Uh, I, unfortunately, I think it's like the, I think it's the same kind of tough grind throughout.
Allison Hartsoe - 27:47 - Ah, okay. So there's really. There's no top of the hill.
Felix Schlidorfer - 27:51 - No, it's um, it was actually a pretty interesting conversation when we sat down with one of the people that bought one of the dashboards, and you said you guys do know there's going to be some pushback from a lot of people, this is not going to be easy. Projects aren't easy here. And it was one of our more senior people who had been at that organization for good five years and he said, I don't even know what an easy project is supposed to be anymore, but I guess I'm, I don't know how a project works that doesn't get this kind of pushback and so used this point, so don't worry about it.
Allison Hartsoe - 28:30 - So it's really just consistent persistence and grit to just keep after it. Did you find yourself just saying the same things over and over or did you find yourself coming in with new twists on the same objective to get that, you know, to keep or maintain that persistence?
Felix Schlidorfer - 28:55 - I feel like we switched our attitude about two, three times and we went from, oh, we're just gonna. We gonna have a database. We're just going to show them the data real quick too, oh, we need to figure out what she daily comes from too, Oh, when you divide us, we need to build a central database that they can use in the future. This kind of different approaches really did work, and I think that had a lot to do with the moderate success of the projects.
Allison Hartsoe - 29:28 - Alright, so let's say that you know, I don't want this kind of implementation disaster to happen to my company. I really want to be able to get into the data and surface it more quickly and easily. What can I take away from the story? What, what should I learn from your experience?
Felix Schlidorfer - 29:48 - There's a lot of lessons I learned during that year. Um, one of the most important ones I think is just two things really brought us a positive output at the end. First, it was just our blind devotion to the cost. We were willing to put in the work. We were willing just to go the extra mile to show that this was something worthwhile. The other side was just feedback from the management, the actual people that wanted that requested it. They loved it. So we were pushing and pulling, and this kind of ongoing dedication really kept us on the playing field for that really long amount of time.
Allison Hartsoe - 30:35 - Now, I guess the logical question here too, and I probably should've asked this earlier, if management is pulling, why weren't they able to pull IT in line?
Felix Schlidorfer - 30:47 - I think it was a very precarious time in that organization where management wants things, but they weren't necessarily always stick out their neck for it. IT, if you couldn't completely guarantee a success a project. People just wanted more and more proof of concept, of course, didn't have to really help us that much while we're still just doing a proof of concept
Allison Hartsoe - 31:11 - When, when you say people wanted more and more proof of concept to reduce risk is, is there a tipping point to that were, you know, you're happy to give more proof of concept? Uh, because you know, at some point, it will all click or is it more of a delaying tactic on their side where they can't quite figure out whether they want to take that risk or not.
Felix Schlidorfer - 31:36 - I feel as though that both of those are true as I, it a certain amount of proof of concept is always necessary, but I think a lot of points of our kind of journey, we actually had a proof of concept. We knew this was going to work, but they really just in the end and sometimes seemed like they just wanted to have the finished product already in front of them. I see they didn't want to have any kind of risk and then implementation, they want to have basically two all ready for you to already be here when you're buying it.
Allison Hartsoe - 32:14 - Got It. Okay. What else did you learn?
Felix Schlidorfer - 32:17 - Um, I learned that it's always important when dealing with IT to put, to run things up the ladder really fast. Meaning we've called the top management over the littlest things with IT. Yes. Because just getting a passport password for a minor account would become a true free week ordeal with IT. So instead of delaying the project by a month, it was my, uh, we would just annoy basically the CEO a 100 times and call them and keep calling him till we finally got what we needed.
Allison Hartsoe - 33:03 - Wow. So, so you were willing to go that distance too, to reach out to the CEO and be the squeaky wheel to get it done? Mostly because they probably had your back and they said they wanted it and yet you, you weren't willing to make an excuse. You are willing to go back again and again and again and say, I need it. I need it; I need it. I'm stuck, you know, help me, help me. Help me.
Felix Schlidorfer - 33:28 - Thinking about it was also risk management in a way is that since we would always report everything directly to management, we would kinda take away the responsibility from us. It was almost a game as a who has the responsibility to do something at this moment, and we have to be really good at pushing that away from us because we already had so much in our back.
Allison Hartsoe - 33:51 - So are you saying that there is a tendency to load the responsibility on the vendor? It's like, like, like a game of hot potato. It's not mine, not mine. It's the vendor's fault.
Felix Schlidorfer - 34:01 - Yes.
Allison Hartsoe - 34:03 - That's, that's some pretty heavy political strategy that definitely works. What else occurred to you when you were, you know, thinking through what made this work?
Felix Schlidorfer - 34:15 - Coming back to the proof of concept? Um, when the end it worked out for us, but I feel like a lot of smaller players that maybe didn't have an ongoing engagement with the kind of been, um, the law was lost if they have given as much as we did
Allison Hartsoe - 34:37 - I'm going to try to understand. Can you elaborate?
Felix Schlidorfer - 34:41 - Um, at that moment in the meeting when we basically came in, and they said, well, we can do everything you can do. I feel like if there weren't a working relationship with upper management on that product, we would just go away.
Allison Hartsoe - 34:55 - Uh, so basically having the air cover from upper management was critical to getting the project completed.
Felix Schlidorfer - 35:04 - Yes.
Allison Hartsoe - 35:05 - And, and was that something that developed over time or did you feel like you had that from the get-go?
Felix Schlidorfer - 35:10 - Oh, I had that from the get-go because I came into the project and there were people that I'd been working with other organizations for five, six years already.
Allison Hartsoe - 35:20 - Oh, okay. Okay. So this was kind of embedded in the relationship between companies.
Felix Schlidorfer - 35:24 - Yes, it was definitely embedded in it, and it was definitely was one of the sorts of topics on the dinner table.
Allison Hartsoe - 35:32 - Do you think if you hadn't had that deeper relationship would you have been as successful?
Felix Schlidorfer - 35:39 - Absolutely not. I think that was crucial that kind of like the social relationship with upper management made all of this happen.
Allison Hartsoe - 35:48 - You know, and I just want to emphasize that because I have heard this before and it's definitely an underestimated element. Anytime somebody looks at a data visualization project or any kind of analytics project, the people who are most successful often are incredibly politically savvy across the organization. And I don't mean that in a, you know, in, in a manipulative fashion, I mean that in a friendship fashion, they are very much reaching out, creating ties, asking how they can help to other parts of the organization. And they are incredibly well respected. They're almost like the internal influencers when they beat, when they take on that mantle of a, let me help you, if you give me this, I can give you that. And I think that's what drives a lot of the dramatic successes that I've seen. So I'm pleased to hear this came from your company too because it is such a crucial element.
Felix Schlidorfer - 36:52 - Yeah, I, it's, um, you know, I've studied mathematics and Statistics, and I studied a lot of numbers. Um, I'm fortunate to have also played a lot of team sports, so I'm already used to all of this socialization and all of that political pressure. But I feel like if I had just done the normal computer science stem background without a lot of work in groups and in teams, I would've been overwhelmed, uh, points. Uh huh. And you definitely need a thick skin. You need to be able to socialize and with data science.
Allison Hartsoe - 37:36 - Yes. Yes. So, so basically you're bringing the playing field into the board room.
Felix Schlidorfer - 37:42 - Yes.
Allison Hartsoe - 37:43 - Any other insights you want to share?
Felix Schlidorfer - 37:45 - Um, so the last insight I want to talk about would be about having clear leadership. If you don't have good leadership in your company, a lot of temporary solutions crop up, and those temporary solutions usually are just that they're temporary because when leadership changes, these kinds of solutions changed as well. And you ended up with legacy infrastructure that you to be like. And at the company for example, they did have a head of statistics but he didn't do a lot with statistics because it was a legacy role that he kind of, he kind of just tacked on to this person that had other things to do as well and it didn't produce a very good result. So being able to clearly define leadership and responsibilities is crucial when working in an organization. And I could definitely see it at that project.
Allison Hartsoe - 38:45 - Was there an opportunity to take that person who was in charge of statistics and add on to their responsibilities or were they really just not interested?
Felix Schlidorfer - 38:55 - They were not interested.
Allison Hartsoe - 38:56 - I see. I see. So whereas in some organizations you might have had somebody step up and say, Hey, I'm willing to be the chief data officer, chief analytics officer, or I'm willing to help broker this across the organization with you. You didn't have that. You had to build that for yourselves. But I think in other successful organization they've had that internal leaders step up. But, but still, the problem of is that leader solving for something short-term or solving for something longterm, uh, remains the learning here. And what you just said is they really need to think long term.
Felix Schlidorfer - 39:36 - No, I don't actually want to blame any of the individuals of the organizations for any of this. I feel like they were, we were all kind of just in a situation with the organization though wasn't ideal. The organization was under a lot of pressure. Um, because of our turnovers, there was no clear leadership and anybody who would want to stick their head out, if they failed, they would be immediately gone. They don't particularly blame anybody for the position that a token all of this. It was just we had to do this thing, they have to do this other thing, and it worked really well when both of our interests aligned but when they didn't. It was just difficult for both of us.
Allison Hartsoe - 40:30 - So would it be fair to say that when you have a culture that's not, that doesn't reward risk-taking or a lack of risk-taking culture, that it has a severe dampening effect on your ability to create amazing visualizations that drive the organization forward? Yes.
Felix Schlidorfer - 40:53 - Yes. I think that if you have a culture that encourages people to go out of their way and go the extra mile, you can get a lot of things done a lot faster than if you have a culture to kind of punishments punishes failure. Because that's half of it. It's for your organization to take risks, you need to be accepting of failures.
Allison Hartsoe - 41:20 - That is the truth, and that is I think the most difficult thing that companies have to wrestle with. It's not easy to. It's not easy to accept failure, especially when you're under pressure, and you're always looking to justify every failure as something that was on the path to success, and you don't want to have to talk about missteps. I think that that's a valid point and for many organizations are the real economic driver. Oh yes. Yeah, yeah. Well, Felix, this has been a really fascinating conversation. I'm going to summarize for us in a minute, but if people want to get in touch with you, is there a way that they can reach out and you know, get more details or try to understand more about what they should do? Can they, can they get in touch with you?
Felix Schlidorfer - 42:10 - Sure. I'm on Linkedin. It's just my name Felix Schlidorfer for um, it's Felix, F-E-L-I-X and Schlidorfer are S-C-H-L-I-D-O-R-F-E-R. And um also my email address is email@example.com.
Allison Hartsoe - 42:32 - Fantastic. Fantastic. So now let's summarize a little bit and we'll see how much I captured of this, that you can feel free to correct me if I've missed something, but when we talk about why should I care about the seeds of the visual data disaster, what I liked that you said in this section is that the idea of the data that came in as storage data was the legacy systems were originally designed to remember that you had certain sets of data but not to execute them for insights. And today everyone wants those insights, but the data structure has to be built with that goal in mind. And that has to do with flexibility with data governance was the speed with a whole lot of factors that drive analytics and hence visualization. Uh, so I thought that was a really interesting point, and it can be surprising to people how much work goes into displaying those really valuable insights.
Allison Hartsoe - 43:34 - And then we went onto the example that you, that you talked through. And this was such a great story. I love the so many different layers to this story, and there are so many things I hear in common from various other pieces, but I don't think I've ever heard somebody put it together and exactly this kind of this kind of one, two, three, everything hit the mark kind of strategy. Not necessarily strategy but like every challenge that you ran into is one that I often hear in different directions. So what I thought was really cool in this example was the way that you created fake data to get that management team on board, to really love it, to get them to give you air cover. So you had to really express the vision, and you know and also know that that vision was possible and then you're ready to go, but your run smacks into the IT wall, but you don't give up.
Allison Hartsoe - 44:32 - And so even though it's not willing to give you a menu of what's available inside the system, you, you don't stop. You express a lot of grit and persistence and you know, going upstream to the sources of the data and looking for different ways to cleanse it or bring it together. It's almost like you really take the company into your heart and work so hard to bring forth what they can do with their own data. I thought that was really admirable.
Felix Schlidorfer - 45:05 - Yeah, it was, um, it was a lot of work, but definitely rewarding when things actually did work. And I think I just want to come back to the thing that really kept us going was the positive feedback from the end user. I'm a huge defender. That's really important. I don't want to say all that matters, but it is really important for when you pursue a project that you know that the product you're putting out is actually worthwhile
Allison Hartsoe - 45:34 - and that they plan to use it. It's not that you're creating something and casting it into a whole, you're responding to our real desire and need, and you're not letting the lack of data stop you. You're pushing to get the pieces together so the organization can make great decisions.
Felix Schlidorfer - 45:54 - We, we really wanted to change people's lives here because it was healthcare organizations. We were gonna actually impact how people get treated and how people, um, and help people's standard of living not only the people had to work on work in the hospital and help other people. Also, the people are being helped, so it was a good pause all over.
Allison Hartsoe - 46:25 - And, and have you seen them today with the organization? You said there were a couple of dashboards that came out. How are they doing today with getting that momentum of driving by those decisions of using the information that you've worked so hard to put together?
Felix Schlidorfer - 46:41 - This is going to be a longer process because just because we have data and we kinda meant helped them visualize it doesn't mean that they're actually had changed to take the proper steps to act on it yet. I see like this is the next step in how to use data visualizations is nowadays at least know what to do, and I think that is already changed. Things in wording now know what our problem children, what are trying to surgeries are high risk. What can we do to have more efficient cleaning and this in this hospital and the first step has been taken? We know where the issues lie, how are we actually solve them, is another step in this ongoing process, but I essentially see that step being taken.
Allison Hartsoe - 47:32 - Well, you know, and I think that's reasonable. You work hard to get the data together, but the culture of using the data is a secondary piece that again, you don't let go. It's just part of the process of moving the organization along, but what a rewarding process.
Felix Schlidorfer - 47:48 - Yes, absolutely.
Allison Hartsoe - 47:49 - Good. Well Felix, thank you so much. As always, links to everything that we discuss are at ambitiondata.com podcast, and Felix has been such a pleasure to speak with you today. I love the story that you've told and the direction that you've given us regarding the fact that, hey, I really can get done. You really can get through this, and you know, stay the course and go the distance.
Felix Schlidorfer - 48:15 - Well, thanks a lot. It was great being here.
Allison Hartsoe - 48:16 - Remember everyone, when you use your data effectively, you can build customer equity. This is not magic. It's Felix has shown us it is just a very specific journey that you can follow to get results. Thank you for joining today’s Show. This is Allison. Just a few things before you head out. Every Friday I put together a short bulleted list of three to five things I've seen that represent customer equity signal, not noise, and believe me, there's a lot of noise out there. I actually call this email the signal things I include could be smart tools. I've run across articles, I've shared cool statistics or people and companies I think are doing amazing work, building customer equity. If you'd like to receive this nugget of goodness each week, you can sign up at ambitiondata.com, and you'll get the very next one. I hope you enjoy The Signal. See you next week on the Customer Equity Accelerator.