Data Warehouse Planning, Execution, and Validation for Successful Outcomes

Transcript

Jess Carter: The power of data is undeniable and unharnessed, it's nothing but chaos.

Speaker 2: The amount of data, it was crazy.

Speaker 3: Can I trust it?

Speaker 4: You will waste money.

Speaker 5: Altogether with duct tape.

Speaker 6: Doomed to failure.

Jess Carter: This season, we're solving problems in real time to reveal the art of the possible. Making data your ally. Using it to lead with confidence and clarity. Helping communities and people thrive. This is data driven leadership. A show by resultants. Welcome back to data driven leadership. I'm your host, Jess Carter. On today's episode, we're talking to Matt Tischler, chief Financial Officer at RuffleButts, an online children's clothing retailer. Matt, I'm so excited to meet you. Welcome to the show.

Matt Tischler: Thank you. I'm so happy to be here. Thanks for inviting me.

Jess Carter: Yeah, I'm not kidding when I say I have been a fan of RuffleButts for a while. Excited to meet you. Really was just buying probably three or four RuffleButts swimsuits. Those are our favorite gear last night, but they have been our only swimsuits we buy for our kids since they were born.

Matt Tischler: That is so good to hear. I hear that often and it makes me happy every time.

Jess Carter: Well, so RuffleButts has been around for, you said, like 16 years?

Matt Tischler: Yeah, we're just celebrating our 16th, our Sweet 16 this week. So we've been around for 16 years.

Jess Carter: That is awesome. And so have you grown up with RuffleButts? How long have you been there?

Matt Tischler: No, I've actually been here for it's almost two years at this point. So I came on about two years ago to honestly post investment by a private equity firm to help to professionalize some of the functions and then obviously kind of get into some of the data and analytics part of it.

Jess Carter: That's awesome. So where was Matt for the first 14 years of RuffleButt's life? What does your career look like before that?

Matt Tischler: Yeah, so, I mean, honestly, I was an economics major in college. I switched from physics to economics because I couldn't see myself doing physics all day. I remember the day I quit, physics was the first part of the second part of my life. Right. So it was a good decision, honestly. So I majored in Economics and business and really loved the quantitative aspect of it. So did a lot of advanced econometrics in my undergrad and honestly, right out of college, worked at Deloitte for about four years, which was great. Just kind of professional services training. I love that kind of consulting element. However, I wanted to work inside of a company and I wanted to grow my career in finance. So I then kind of stepped into Yum brands. Worked at Yum for about four years and was really great training. Love Yum as a company, as a brand, all the brands that they have, but then really wanted to get into a little bit smaller company to have my hands in more things. And that's where I left Yum and went to a public company at the time, Zoe's Kitchen, which was another restaurant concept, which reminds me a lot of RuffleButts because you'd always get comments of everyone loves Zoe's. It was just kind of the brand that everyone loved. I was there for four years and that's really where I got my hands dirty on data and analytics at a very deep level. And a lot of it was out of necessity and teaching myself lots of things to be able to get to the right answer, teaching myself SQL and whatnot. And honestly, from there went back to Yum for a couple of years and then had really always wanted to kind of work within different industry and that's where RuffleButts came to mind. And it's been such a great fit getting into the private equity space, but then also getting to work with such a loved brand and fast growing brand as well. So every day is very different here and I wear a lot of different hats, one of which is kind of leading our data and analytics kind of platform and roadmap.

Jess Carter: Okay, so tell me a little bit more about this data and analytics roadmap. So when you showed up at RuffleButts and you've had all this other really rich experience, and you've talked about economics, finances, data, when you walked into RuffleButts kind of what was your 1st 90 days, what did it look like, what did you find? Where did you kind of get your sense of direction?

Matt Tischler: Yeah, so, I mean, when I first joined, I mean, the good thing is RuffleButts had a pretty solid foundation in that. They had an ERP set up, they had NetSuite, which was a very common, great ERP. I just had never fully utilized it and needed to figure out how to get data out of it that I could use to kind of understand what's going on in the business. So I would say one of my first things I did was there wasn't a lot of kind of more formal, kind of weekly reporting as you'd expect in like a PE backed company. It had been small founder led for many years. And so a lot of the information there was, it wasn't bad, but it just wasn't up to the level that I wanted it to be and that our PE sponsors needed it. So some of my first things were to really just understand, hey, what do we do in sales yesterday? Okay, let's make sure I have a report to be able to understand that by channel, by product category, everything, and then kind of building up that weekly kind of report to basically then have a scorecard of what's going on in the business. But then anytime you develop something like that, it generates more questions. And that's honestly where I very quickly hit a wall with NetSuite CSV exports is you get to a point where it's like, hey, well, what gets into that number? And it's like, well, I don't know. Give me 4 hours and I'll go figure it out. Right. So I was unable to kind of drill into data, which is something that I knew was possible once you just get the right infrastructure set up. I remember even in my first month, I was trying to figure out, okay, how do I kind of get a data model or data warehouse solution in place? But it wasn't until probably about a year after I joined that I was able to actually do that because there were a lot of other things on my plate at the time that I needed to tackle first. So it was more of a, hey, let's get something that works for now, and then we can go make it better a little bit later. And that happened right now. It's April 23. It was almost a year ago that we started our journey.

Jess Carter: That's amazing. Well, and this is so interesting for so many reasons, but I think a lot of people will go buy a solution, a SaaS product, which is great, but then understanding the value of a warehouse behind it. If you were to try and tell a friend, if somebody was like, well, why isn't that enough? It really is. That 4 hours of deep dive every time someone wants to unpack something, that's the value.

Matt Tischler: Yeah, and that's honestly where I kept hitting walls, like I said, because you'd pull information by a certain dimension, and it would stop there. If you wanted to go any deeper, any kind of trend analysis would, like, explode Excel very quickly because it was just so much data and it would take so long to do anything. And that's where I mentioned a lot of the analysis in the business previously was good information. It was just very snapshotish because that's all you can do if you're running a quick CSV export. But if you wanted to look at really trend based information, if you wanted to dive into kind of customer details and analytics and buying behaviors, you're going to have to build something different and not work in Excel and Google Sheets.

Jess Carter: Well, and like, God forbid, have you ever been in this situation, too, where you get maybe to a board meeting with your private equity firm and you show them something that took you hours and hours to build, and they're like, cool, can I get that every whatever. And you're like, no.

Matt Tischler: This literally almost killed me. So the answer is maybe once a.

Jess Carter: Quarter I can, if you help me fund a warehouse.

Matt Tischler: And actually, funny story about that. I remember I think it was one of my first couple of board meetings. I actually did do a bunch of CSV export out of NetSuite and used a free trial of Alteryx because I know Alteryx decently well to kind of pull together a picture of the customer. Right? And so I did all that for a board meeting. The board loved it. And then they're like, yeah, we'd love to see this more often. I was like, Well, I mean, that literally took me a couple of nights and weekends and CSV exports, it took hours to download. But that was also then a little bit of the selling point of, hey, if you want this more readily available all the time, we need to go invest in a proper data solution because it's just impossible to do it consistently without that. I mean, anybody can do an ad hoc analysis, but how do you do that in a consistent way? And it's that consistency that really helps to drive that value of the data at any business, really.

Jess Carter: Right. Then you pursued a data solution in a warehouse, and so you had I remember some of the parts of the story where you kind of knew you needed cloud, knew you needed SaaS, knew you needed a warehouse. So walk me through the story. Walk me through kind of what that felt like from realizing your need and then fulfilling it.

Matt Tischler: Yeah, absolutely. So, I mean, like I mentioned, kind of from my first week in the job, I knew this is going to be a need. I mean, I'd come from at yum. We had a very robust snowflake architecture that gave me everything I needed. When I was at Zoe's, it was all SQL Server, but it worked and I could analyze anything. So coming into RuffleButts, I knew I needed something. But I also wanted to explore to see if I can do anything within the NetSuite platform, which the answer was no. What I needed was a little bit more robust. Nothing against the NetSuite platform, but it was more of I needed to I call it liberating the data, kind of getting it out of the system. And so I then had two paths and honestly, a couple of different firms that we talked to to basically bring a solution to life. There were two models that I saw, one of which was kind of developing an API type solution to extract the data, and then the other one was using kind of third party software that already does that for you and already knows the NetSuite architecture, which was, spoiler alert, the right answer. The other solution I found was going to be very intensive in terms of both the build and also the maintenance. We're still a pretty small company, very fast growing, very lean. I could not afford to have an army of DBAs and data engineers. I don't have any of those right now. It's all kind of managed through the cloud.

Jess Carter: Wow, that's unbelievable. So was it a fast process to get from the need to the realization, or did it take several quarters? What was that like?

Matt Tischler: Yeah, it was fast. I'll say it was probably the fastest that I've seen in terms of getting there. So it really started with, honestly, once we started the project. The good thing is I had a year under my belt of understanding what I really needed out of the data. So number one was I had to learn the business pretty well. It's my first ecommerce company, so I knew restaurants very well, similar customer kind of data that I understood, but I needed to really understand kind of what I wanted. So that year really helped me before I jumped in, or almost a year. But then really we needed to get the data or the tech stack correct. So I would say the tech stack that we chose, which was five tran from an integration engine, which works very well with NetSuite loading into snowflake, transformed via coalesce into a Tableau data model. Tableau published data source to dashboards. It took us probably our first dashboard, which was very not robust, but it was still a dashboard, was probably published within a couple of weeks, I would say with we had all of our data loading into snowflake within a day. And we started our transformations the second day, which I mentioned. That almost a year of experience. I had kind of a roadmap for the resultant team to essentially then go and kind of focus on, hey, you know, your rights whenever you match these numbers, and here's how you need to do the transformations on it. So having the right tech stack, then having a really good roadmap, and honestly, like guardrails for what it should look like helped us go really fast on the project.

Jess Carter: That makes sense. Well, and as a stakeholder, your background, which I'm glad we covered, sort of allowed you to be the champion, the stakeholder, the person we're gathering requirements from, the user acceptance test. You were able to kind of be all of those things because of how lean you guys are running. And that makes things simpler too, when it's one voice there.

Matt Tischler: That's exactly right. And I would say I think one of the benefits that and I think why I think every person within finance and especially in the CFO seat, needs to know enough about data is everything is data is what I tell a lot of people is if you think about a PNL, that is a summation of different data elements. I mean, that's literally how NetSuite works, right? So if you understand the data behind it, you can be much more dangerous in doing anything you need to in that standpoint. But yeah, it was helpful that I was able to kind of for better or for worse, I was the one delivering the requirements, doing the acceptance testing, checking to make sure everything was good. But I think that helps us go faster. And there's no bureaucracy or no committees or anything like that. We just went fast and we're able to get something out the door pretty quickly.

Jess Carter: And you would say that's something that you got out the door quickly. So it sounds like you kind of did sort of one pipeline at a time or one dashboard at a time. You built all of that through. Okay, so you were also able to generate value quickly. Like when we talked about a dashboard in a couple of weeks, that was a usable dashboard for data driven decisions.

Matt Tischler: I imagine right up to that point, I was kind of refreshing data daily, kind of posting in slack. Here's our daily sales yesterday versus forecast versus last year. I was able to replace that with a dashboard that was self service that people can then drill into in a matter of weeks or maybe a month, essentially. And so selfishly, it got me back 15 minutes every morning. But then also people were then able to self serve on the analytics side, and then also we could drill deeper. So if we want to look at, hey, why were we off yesterday? We saw a lot of swim. We also saw a lot of peril. Was it boys, girls? Is it timing of last year? That's why our comp is off over last year. So all of that would have taken hours, and we don't have anybody to do that analysis except for myself and now a couple of other people to do that. So we were able to immediately deliver value and then honestly get tableau in the hands of people who need to use it every day to be able to run their own analysis and create their own workbooks or dashboards so that they can understand the business and everyone's then speaking the same language and also they have the same answer. I think when I first joined, if I asked everybody, hey, what do we do in sales yesterday, I would have gotten like ten different answers based on how they configured their kind of report in NetSuite, there's only one right answer.

Jess Carter: That's awesome. So you have an aligned team that understands the metrics. You mentioned getting them some tableau. I mean, was there some data literacy training? Like, did you have to kind of help some people get their arms around that?

Matt Tischler: Yeah, I would say I'm big on data definitions and making sure that everybody knows, hey, when we say total net sales for our business, that includes product sales, that includes shipping revenue, that also includes I know we do embroidery for some products. And so it's like, hey, includes all those if I'm saying products, net sales, that is product sales after any discounts. So it's kind of educating people on, hey, here's how we're going to talk about the business, and then making sure tableau matches what everybody would say. And then a little bit of light tableau training. And so we have a couple of people who they picked it up very quickly to be able to just it's like, hey, instead of running this report NetSuite that takes 2 hours and downloading, it just go into this workbook and go into this dashboard and grab your numbers, or it literally has changed how we operate in a lot of ways.

Jess Carter: I mean, it's seriously so cool to hear a story where you're like, this is actually transformative in under a year. I mean, that's amazing to me. Well then, so let me ask you this. So elephant in the room, you hinted on this a little bit, but I really want to unpack it. If I had heard this story and I was guessing the role the person playing it was right, like, I wouldn't have guessed CFO, I would have guessed Chief Data Officer CIO. And so you really did talk about this, and I think that was a blind spot for me in the first ten years of my career, is understanding. Hey, a great CFO, especially in private equity, has to have their arms around the business and make sure that the business has their arms around the business, that we're all talking about the same things. And so when you think about a data driven CFO, what are some of the elements in your head where it's like, hey, these are the key components that made me successful. And it wasn't just understanding how to read a PNL right, it was more than that. If you were to encourage somebody maybe who isn't as comfortable playing around with data and isn't going to teach themselves, python, would you have any advice for somebody where it's like, here's just where you might lean in a little bit more to the rest of the business's data?

Matt Tischler: Yeah, I mean, honestly, I'll talk in generalities and I'll talk a little bit about how I got to that. So I would say the first advice I would give to others in the CFO chair are just understanding how the kind of data infrastructure works and how as simple as it sounds, relational databases work. And then drawing that into kind of what they do on a day to day basis will help you think about hey, yes, I can get to the answer to that question because I know the data is there somewhere. It has to be, right? And so, honestly, my eyes started to be open a little bit more. I think back when I was at Zoe's, I taught myself SQL because I needed to answer questions I could not with the current data that was in front of me. And so I remember talking to her DBA, I would see her do something in SQL. I literally wrote it down on a piece of paper, went back to my desk and started just messing around with it. And then in two years, I had basically taught myself sufficiently to where I could do some pretty complicated things and build my own tables and do this. But to me, it was out of pure necessity.

And this is kind of one of my other things, is curiosity. I think the good CFOs I've worked with are very curious. They want to understand what's driving something. And so usually if you have a desire to know what's going on there, you're going to try to learn all the tools to be able to get there. Most CFOs are not going to be able to do that or not need to do that, but at least understanding how their teams are going to get there will help them equip their teams in the best fashion and making sure that they're putting their teams in the best position to have the information. So where I've seen this work well is, hey, the less wall there is between it and finance, the better. I've been at organizations where there's a pretty big wall and finance may not have access to that kind of data, but the more I've seen SQL, Python and those things, more in the finance data set because finance folks are naturally curious. They want to understand what's going on. And so they tend to then say, hey, I want to go figure out the answer myself, versus kind of submitting a request for an answer, right. It's much more satisfying and I think efficient to go equip your teams to be able to do that.

Jess Carter: Sorry, I'm so fascinated and interested in the story because think about what would have happened if you wouldn't have dug to understand the business, right? You'd never been to an ecommerce site. You hadn't ran that business before. If you didn't dig to understand, you would have built a bunch of dashboards that said the wrong thing to the wrong I mean, it would have been total vote of no confidence on why do we need a warehouse if everything's broken, right?

Matt Tischler: That's exactly right. And honestly, our journey still almost a year in is just beginning when it comes to data. But even having just that granular data of hey, how much did I sell by day, by customer, by order, what did I sell? That is the most basic you can get. We now have that in a very clean data warehouse with a tableau data source a lot of people have access to. And they can slice that any which way they want if they want to look at. There's so many different answers or so many different questions that come up in the business that can be answered by this data that we're able to do now that we never were able to before.

Jess Carter: So when I listened to you explain this, one of my curiosities is you hadn't done this before. Were you nervous at all or were you very self assured? Like, this is going to be fine, I got this.

Matt Tischler: I was probably overly confident, but I was very confident. And it's interesting because I think when I first pitched the data warehouse project to the board and got board approval for a budget, it was part of our 22 budget. I remember one of our board members, she's pretty high up in a company that is very good at Data. And so I spent extra time with her and actually one of her data engineers just going over the stack because she's like, hey, we've gotten this wrong before and it cost us two years. So she's like, hey, can we spend a little bit more time? I want to make sure that you're good on the stack, right? And so I was like, yes, let's do it. I'm very confident. And so I reviewed everything with her and also had worked with a couple of other folks that I knew in my network to validate, hey, this is the stack going to build. Is this the right bill? It feels right to me based on everything I know and based on kind of what I'm seeing, but had it validated by multiple sources and then had a meeting with our board member to walk through everything and then she gave the thumbs up like, yeah, this looks really good. And it was probably two quarters later, we were way done with the project and we're kind of showing things off and she was in a board meeting. She was very complimentary of, hey, I was nervous before we started this, but she was like, it's very clear that you did this the right way. So I do get nervous about things, I'm not going to lie, but I was never nervous about this because I've seen how bad it can be in other companies. I'm like, it's going to be really good here. And so far it's been fantastic.

Jess Carter: That's so cool. Well, also you used even in that way, you knew you were going to be able to figure it out, but leveraging your network and having an amazing board member where people around you saying, yeah, let's lean into it, and you weren't like, no, it's mine, I got it. You're like, sure, let's talk about it. Beat it up, right? And so I think you had every reason to be confident in where you were headed because you beat it up yourself. You didn't just let other people beat it up when you thought you were self assured.

Matt Tischler: Exactly. Exactly.

Jess Carter: Man, that is so cool. Okay. And then my other kind of curiosity about the experience of the project is, did you have a moment when you kind of saw your first dashboard or you had your first data set for a board meeting where you were like, this is awesome, I am getting the value. Do you remember any of those moments?

Matt Tischler: Oh, yeah, I remember the first time I was able to there's always going to be ad hoc analysis, right, that you've got to do it's like, hey, what's driving X, Y and Z? I remember the first time I was able to use Tableau to do that. And then the first time I was able to use SQL to do something a little bit more complicated to understand, like, hey, it was some kind of basket analysis like, hey, do people who buy this typically buy this as well. I was like, Man, I remember telling our CEO at the time, I would have told you no, if we didn't have this. I would have said, hey, I'm not going to be able to get to it because it's going to take me a couple of years to download the data. But it took me ten minutes. And so those were good feelings. And it was honestly a little bit of the validation of about a year's worth of wandering through the desert without a data warehouse and then being able to implement it and then be on the other side and have that solution. It was validating in the sense that, number one, I was able to answer questions I couldn't before. Much better speed. And then selfishly. I remember we published our first dashboard right before I was kind of set up the automation for the reporting right before I was going to be on vacation for like a week because I was like, I don't want to do this report every day while I'm on vacation. And so we got it live, and then I was like, I was out for a week and it went off flawlessly. Everyone got their information. Nobody bugging me for that week. So that was a selfish one, but it was still also, I think, how do I say this differently? If it's only me, I'm going to be the bottleneck every time. And so we have to both liberate the data and then democratize it, right?

Jess Carter: That's such a beautiful phrase.

Matt Tischler: That's one of my favorite phrases. Because if everybody comes to me for answers, then they're going to be disappointed because people joke about taking a number before they get to my office because I just get bombarded with things. But I want to be able to give people the tools and the data they need to run the business. And that's honestly what makes me happy is when I see people go solve an issue or really come together with an analysis to make a recommendation and I was not involved in it, that makes me happy because I know that they wouldn't have been able to do it beforehand.

Jess Carter: So you actually just answered maybe my last question I had for you, which was, how are you seeing the business mature in light of having some of this and the whole solution that you have? And it's like people are coming to you with some level of analysis or asking you questions, but it was like, I imagine you're able to tackle a next set of business problems because of this that you couldn't quite get to before. That is pretty neat.

Matt Tischler: The questions that we have have evolved. So when I first joined, it was, what is sales? And then number two, it was like, hey, okay, what was sales yesterday? And then did we hit forecast? Are we above or below last year that then. Leads to, okay, well, what drove that variance? Okay, let's figure that out. And then you get into and this is the journey we're on now, which is then the customer level insights of one of the things we've done, I would say this year is kind of joined in a lot of our Google Analytics data to the transactional data so we can see hey, I know that Jess bought some stuff yesterday and she went directly to our website. She didn't go to an ad or anything. And then I can see kind of, hey, what was her journey before that? And then how do we then build a propensity model to say, hey, if I see Jesses in the future, do I think they're going to order or not? The level of sophistication of the questions that we can ask and then potentially answer is just going to keep going up and up as we, number one, add more data sources and join that all together, and then also just as we get more comfortable with understanding the day to day business.

Jess Carter: Absolutely. Very cool. Well, is there any part of that story that I haven't asked you about and interrogated you enough around that you wanted to share?

Matt Tischler: No, I think we've covered it quite a bit. It was a pretty seamless process. Honestly, from my mind, it went smoother than I expected. The more you dig into the data and the more you figure out stuff and there's gremlins that pop up that you're like, wow, I didn't know that was going to pop up. That you just kind of solve in the moment. But I think one of the things that I don't think I've talked about is the reliability and scalability of the solution. This solution worked very well for where we are, and it's going to continue to scale because we started off with adding NetSuite data and then also we're piping in kind of our forecast data, which is just in a spreadsheet that we just get into snowflake and kind of marry up. But this is going to scale two, three, four, X kind of where we are now. And as we pipe in more data sources and it's been very reliable. I mean, the number of times that we've had issues in the last year with the report or something like that, it's very few times. And it's usually traced to something going on in NetSuite or time zone issues. For some reason, daylight Savings time throws a few things off and I have to go figure it out and correct it. But those have been very rare. I mean, everything runs it runs very well and nobody's looking at it every day, which is the thing that makes me happy is we do not have to hire an army of DBAs because we can't afford that. We can't afford a data engineer, a DBA in this just to watch it, to make sure it works. I just trust the software that's going to work and it does.

Jess Carter: I'm so glad you mentioned the Scalability, because yeah, I think a lot of places wait until they kind of have to do this. And I feel like you had the foresight to know it was needed, and now it's a little bit hands off, ask great questions, get out of the way. But it can hang with you for so long. And you have the whole engaged while it's lean. Which means when you have a new hire they're coming in and getting engaged with it. Not having half the sort of like all over cowboying their data wherever they need to. Right. So you got alignment and buy in so early that you've got a little runway here that'll just be fun.

Matt Tischler: Yeah, absolutely. Well, I mentioned scalability but one thing which is very closely related which I think is important is flexibility. And I know this because kind of an analyst at heart is I always want more information which to a lot of the data folks on the engineering side it's like oh, he's going to want another column in this report. He's going to want another field. Right. And in a previous kind of organization I've been to that's an It ticket that takes like three weeks to resolve if then if it works. But with the solution we put in place I'm like, hey, I forgot to map this in whenever I needed it and actually I do need it. That actually came up a couple of months ago. I needed this one field mapped in from NetSuite and I was able to through Coalesce I was able to get that mapped in in about 30 minutes and then just refresh the database then it's good. And all the historicals back to 2013 are updated. So the fact that I was able to do that myself was pretty powerful. So I would say that a lot of this journey kind of culminated in we had our PE firm, Summit Park. We had a couple of folks from there in on back in January to kind of review the business and everything at a pretty deep level. And I had a bunch of stuff prepared, but also prepared a bunch of tableau just kind of workbooks just to show a couple of things. And they were all blown away with the fact that I could pivot how I'm looking at the business so quickly and look at all the different metrics that we need to. They were truly astonished that that was possible and that that's what we built because I think they had seen some of it and they were like this looks really good. But when you're able to see it in action and be able to see hey, okay, well can we look at it this way? Sure. Here you go. They had never seen it that flexible before.

Jess Carter: That is awesome. Well and I mean when I think about some of the flexibility that is also forethought from you as a data driven leader, right, of like, okay, I'm a curious person too, and so most of the time I'm awful when it comes to someone. If I was in a SQL world all day, every day, I'm always going to look at a report and think I want one more thing on it. Always, I will always be curious about a thing and then maybe depending on the data, it can go away. I don't care, but I care until I don't. So your ability to be like this solution has to please this customer because I'm either going to be your best or your worst customer.

Matt Tischler: I think it was also a really good fit with resultants in terms of because Michael Tantrum, who, you know, we see very eye to eye, he talks about he leaves Lego pieces all over the floor, but he gets them done pretty quickly. And I was like, well, I resonate with that. Because he also understood it was an Iterative process because I'm like, hey, I told him, hey, I can give you 90% of the requirements. The other 10% I don't even know yet and we're going to figure those out together. And so the solution needs to be if it's hard coded to that, 90% people downstream are going to be frustrated and it's going to take more time. So that's where the flexibility honestly, to me is more important than scalability. Although it does both. But business needs change now that we're asking more sophisticated questions like, hey, we've now got to track things differently. How do we get that into the data warehouse and how do we make sure that the orders are attributed a certain way? You don't know that whenever you start the project and you're trying to figure out what happened yesterday, right. So that's where that flexibility comes into play.

Jess Carter: Yeah, I mean, your data sharpens your people and your people sharpen your data. Right. And so if they get better with your solution, they're going to ask better questions of your solution and it's got to be able to hang, right? And so good for you. That's awesome. Thanks for listening, you guys. I'm your host, Jess Carter, and don't forget to follow the data driven leadership wherever you get your podcasts and rate and review letting us know how these data topics are transforming your business. We can't wait for you to join us on the next episode.

So I just got off the phone with Matt Tischler, and I think that I was just so impressed with if I step back for a second, some of the data driven leadership I observed in his experience at RuffleButts. When he showed up two years ago, he had no experience in an ecommerce environment, but he did have his consulting at Deloitte. He had his background and some experience with private equity. He knew some of the from yum. Some of his experience with the restaurant industry, and he had his finance information and his economics degree. He had so much at his disposal between his skill sets and his capabilities and his experience, that when he showed up, he got to decide how to take in the environment around him and apply himself and his skill sets in a way that helped anticipate and resolve a problem before it existed.

His ability to understand their need for data, their current consumption of it and use of it, and how to bring those together to generate better data that they consume in a more effective way to feed board meetings and to make better data driven decisions and to better understand their customers. I just think it shows a lot of foresight, and as you can tell, he's like a super energetic guy, right? Highly engaging.

I think his ability to understand how to leverage all of those skill sets in his environment is a really good moment of reflection for data driven leaders out there. Like, what are the things that make Jess uniquely Jess and whoever's listening, uniquely you? And how do you apply those things at the right amount to the problems your company is facing or your family is facing to make sure that you are showing up and applying the right skill sets. I don't know if you've ever had that experience where you're just trying to muscle through something like a SQL query and then someone shows you that there's a warehouse where you can just sort of dig and you're like, oh, okay, I could have done this faster, better, stronger. I think that's the application I get from Matt is you have all these really amazing skill sets, but how you apply them matters.

So I'm going to go back and reflect on whether I'm applying the right muscle to the right problems, and I hope you do, too. If you have specific topics that you want to hear about more, please rate and review the podcast and let us know how we can work to incorporate those into future episodes. Thank you for listening. I'm your host, Jess Carter, and don't forget to follow the Data driven leadership wherever you get your podcast and rate and review, letting us know how these data topics are transforming your business. We can't wait for you to join us on our next episode.