Transcript
This has been generated by AI and optimized by a human.
Jess Carter (00:04):
The power of data is undeniable and, unharnessed, it's nothing but chaos.
Show ID (00:09):
The amount of data was crazy.
(00:11):
Can I trust it?
(00:12):
You will waste money.
(00:13):
Held together with duct tape.
(00:15):
Doomed to failure.
Jess Carter (00:16):
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 Resultant.
Hey guys, welcome back to Data-Driven Leadership. Today we're diving into the kind of leadership that takes data from being just numbers on a screen to a true engine for information and impact. Data has evolved from a back office record keeper to the heartbeat of modern business strategy, and the leaders who know how to harness it are shaping the future. Our guest today is Veronika Durgin, VP of data at Saks, and a seasoned leader with more than 20 years of experience guiding teams and strategies at scale. Her career journey took her from mastering the technical foundations of data to leading enterprise-wide initiatives that connect people, processes, and technology in powerful ways.
(01:12):
When she first started her career, the data landscape looked nothing like it does today. Tools, techniques and even the way organizations thought about data have transformed dramatically. She has seen firsthand that to succeed in this field, you have to commit to constant learning. Now in her role as vice president of data, a position that did not exist when she began her career, she focuses on leading data initiatives and prioritizes encouraging conversations between data teams and stakeholders to ensure they're actively engaged in the process.
So whether you lead a data team, influence data strategy, or simply want to sharpen your leadership skills in a data-driven world, this conversation is full of insights you can put into action. Let's get into it.
Welcome back to Data-Driven Leadership. I'm your host, Jess Carter. Today we have Veronika Durgin, vice president of data at Saks. Let's get into it. Veronika, welcome.
Veronika Durgin (02:08):
Thank you for having me. Super excited.
Jess Carter (02:11):
I'm so excited to have you here. You have a unbelievable background. I'm very impressed with your career. Did you walk into one of your roles and it was like you need to build a data strategy?
Veronika Durgin (02:25):
Both yes and no.
Jess Carter (02:27):
Okay.
Veronika Durgin (02:27):
And lemme maybe it's personality, but I've spent many, many, many, many years being a doer, both because I love it, but also it was important to me to actually have experience doing stuff and learning and understanding how things work. And then eventually I got to a point where I was comfortable saying, “Hey, I know what I know. I also know what I don't know.” So I can actually kind of start thinking for the future based on the foundation, what I know, based on what I'm learning, making connections, and that's to me what a strategy is. Kind of like grounding in fundamentals, in knowledge, your systems thinking, but also building for the future. At some point I was just ready for that. So it was kind of like both. It's not like somebody just like, hey Veronika, you look like someone who can, you know, take us to the future. It wasn't like that. I was actually at work long and hard to get to a point to where I was even comfortable thinking about the future.
Jess Carter (03:30):
When you say that you were a doer or you've been a doer, what does that mean?
Veronika Durgin (03:36):
I learn by doing. So I read that, I want to experience it. I'm just naturally very, very curious. So I spent a big portion of my career actually being an engineer feel like, oh, my background's in engineering. I've actually spent years working with transactional databases, fine tuning, optimizing, understanding how things work, what it actually…experiencing in real life, what good decisions and good designs are versus what bad decisions and bad designs are and learning. I was lucky to spend quite a few years of my early career with the team that was very outside-of-box thinkers. So I observed what it really means to do something by the book or to do something that works in reality and where you bend the rules a little bit to make it work. I am not normally a rule breaker, but just seeing that balance of knowing what you do and understanding trade-offs and implications, I think it was such a pivotal fundamental moment for me in my career where calculated risks actually pay off.
Jess Carter (04:54):
Right. Well, and I think that that's really important. I do feel like I'm the same way, so my confidence comes from my experience not from really anywhere else. And so I'm really confident when I've done things a long time; I'm super not confident, I'm more risk averse if I'm trying something new for the first time. I find it really interesting even when you said you've lived through what great data strategy lived out looks like and maybe not so great, do you have either war stories of things where you're like, this is what I would not recommend, or hey, this is something we did somewhere in my journey and it was so cathartic to live it out and see it actualized.
Veronika Durgin (05:35):
Some of the old examples are probably not applicable anymore and maybe too detailed in your examples, I can't quite share. Another piece of wisdom of how my brain works is you can take whatever risk you want as long as you have plan B of how to get out of the situation or if you know cannot get out of situation. I actually recently wrote out to how do you have time and space to fix and make it work? And I'll tell you a funny story, and this is just when I worked at Vistaprint years ago, we had a real NOC, network operation center, with, it's a room with glass, it's like a fishbowl with gazillion TV screens, and there were people truly monitoring the health of the website and all the systems and we had this sledgehammer by the door that had a sign on its head “Plan B.” That to me, it's just so funny, but sometimes with brute force or times something elegant and in my mind I was like, as long as you have a way and you think through how to get out of your situation, you can make anything work, really. But that's like a visual. Every time I'm like, we need to have a plan B, and this is what I imagined this massive sledgehammer with a “Plan B” on it.
Jess Carter (07:01):
That is incredible. I want everyone to go get a sledgehammer.
Veronika Durgin (07:06):
I mean, don't be that violent, but sometimes it is okay to do brute force sometimes as long as you again understand the trade-offs.
Jess Carter (07:15):
So what I really appreciate about what you just said is there's a reality, too, to like, hey, you do have to think through what if this doesn't work? So there is a responsibility of a data-driven leader to think what if this doesn't work? But I also think that there's, for me, I have found that there's a lot of freedom in that exercise because I am extremely risk averse, extremely. I've gone through some things the last five years in my life where I've kind of had to realize, you know what? Nobody is probably dying because of the decisions that I make at work. Truly, that's just not probably going to happen. We're humans who have evolved all these years and my physiology is like, no, but something really bad could happen and it's really powerful to be like, well, let's name it, then. What could go wrong? Because it's actually not as big as my fear is. And then the fear can size itself and we can build a plan. So I completely understand and appreciate that wisdom from you.
Veronika Durgin (08:10):
And there's a little bit more to it, second-order thinking. So it's not necessarily what could go wrong, it's more of how do I fix things, how I think through solving things proactively, because it's like we want for things to succeed. So when we go through this mental exercise of if I go this way, step two, three, five,, et cetera, what can go wrong? How can I mitigate it? It's a wonderful mental exercise that when you go into a situation, you are prepared and that's where you want to be. You don't want to be shocked, you don't want to be freaked out. That's how you make things succeed ultimately. So I kind of like a mental exercise of being prepared and the risks are not actually scary anymore.
Jess Carter (08:54):
Yeah. So let me ask you this. So we've joked a bit about your wisdom. So you've spent, I dunno, how many years in data in the industry, what would you say?
Veronika Durgin (09:04):
Another funny story, I'm like, I've been here long enough where I have a funny story probably if every question you ask.
Jess Carter:
Awesome! That's a dream.
Veronika Durgin:
My very first full-time real job was in 1999 and why it's a fun story is because everybody around me was freaking out about Y2K and I was like, ah, what whatcha talking about? And then nothing happened. So I go way back.
Jess Carter (09:37):
I remember the concerns about Y2K and it is really funny because you either were here for that or you're like, what are you talking about?
Veronika Durgin (09:44):
Yeah, I was there for that and I still, I have no idea what you're talking about.
Jess Carter (09:47):
Yeah, that's fair. You're around and you're like, this doesn't make any sense. I imagine you've seen an enormous amount of evolution of database administration and systems management and processes and what effective data organizations look like. Do you have some thoughts on key qualities or skills you found that distinguish people who thrive and grow in this space over the last two decades?
Veronika Durgin (10:12):
Yeah, I think honestly curiosity and growth mindset, and I know everybody is freaking out, but I've always said, if you don't like learning, you should not be in data. It's probably applicable everywhere else, but I obviously, I mean I can't speak to other areas, but I've kind of always said it. Things change so much in such a short time period.
Another funny story for you, when I started, well, besides the dial-up modems and stuff, the help was actually Ask Jeeves. Back then Google was not really a thing and Help—I worked with SQL Server—was a book, so that's all we had for SQL Server, this thick book, and then any error message or whatever you get, you had to read through the book and if you can’t find it, there was a phone number on the end. So just to put it in perspective, that was barely 25 years ago.
Jess Carter (11:10):
That's wild.
Veronika Durgin (11:11):
Look where we are now. So if somebody doesn't like to learn and stay curious and doesn't have growth mindset and doesn't feel comfortable being anxious all the time because you're like, what am I missing? What am I not learning, data is not the right place for you.
Jess Carter (11:29):
We've seen this huge change over time, and I imagine when you started, a lot of this was like you were in it's database administration, it's engineering force systems of records of some kind, and then data sort of stood up and started walking. It took its first steps and it was like, oh, data is a product. I guess one of the things I would ask you is, and I'm genuinely curious, I imagine there weren't a lot of people with the title VP of data in the early two thousands.
Veronika Durgin (12:01):
There were none that I can think of. That's actually another great point. The role I'm in right now didn't exist. Back then data wasn't a thing on its own. There were pockets in, I think it was early two thousands, data warehousing kind of started taking shape. And yes, there were teams, data warehousing, they generally rolled into some sort of tag. They were like your operational databases, you were analytical databases, but data was meant to support something. Data by itself wasn't a thing. Data by itself wasn't capability. It was just a tool to support some application.
Jess Carter (12:44):
Do you remember the first time a light bulb went off for you where, oh, this data is being monetized, productized. It's its own thing.
Veronika Durgin (12:55):
So I think that specific light bulb moment for me was when I read Doug Laney's book, Infonomics.
Jess Carter (13:02):
Okay. Yeah.
Veronika Durgin (13:03):
So I think to me that kind of pivoted perspective completely. So I spent my gosh, 15, 17 years on operational transactional side of data and then I was just done with it. I was like, all right, cool. I think I learned everything I kind of wanted to know. And then I was like, all right, let's switch to analytical data because this is new. So for me, it was still more of very I see, I want to understand the details of this, but then I've read his book and I was like, whoa, this is so cool. And I highly recommend for everyone to read it. It just puts things in just different light and different perspective. And this is how do you use data as a thing that unlocks new capabilities, new revenue streams, not just like a backend database for something?
Jess Carter (13:53):
Do you get surprised? I get surprised all the time at how many organizations still haven't had that light bulb go off.
Veronika Durgin (13:59):
So periodically I get on calls to organizations and they're like, oh, we're thinking about moving to the cloud. My first move to the cloud was like, I don't know, 10 years ago and I thought I was already late. And I think the point that I actually want to make is the gap between bleeding edge and others keeps widening. So for us as data professionals, sometimes I feel like I'm stretching one foot on one side, the other on the other, and you're going to get ripped in half. So it's like that cognitive load of there will still be companies that haven't made that leap, haven't had that light bulb moment or don't care, maybe it doesn't impact them. And then there are others running at the bleeding edge, and it's like the things that we have to know and understand, the pile is bigger and bigger and bigger.
Jess Carter (14:52):
If you're on the cloud, it can make a lot of other things easier, simpler, and cheaper, self-service sign-on, et cetera, single sign-on. And so you have all these different benefits of being there, but if you're not there, then we have to figure out other ways. So to have some empathy. To your point, there are businesses where it doesn't matter, the value proposition isn't there. So you're not silly if you look behind all the time. It depends on the context and what's right for your business. But to your point, I do think when I look at companies that are growing and building and data is a product, I think not everyone is treating it like that. When you realize that your data is a product, it means it needs different accessibility, different protection, et cetera, integrations, and now you're playing in a world where cloud certainly makes sense to at least consider, right?
Veronika Durgin (15:43):
Yeah, for sure. I actually maybe a little bit controversial, but I think you'll love that. So data is a product, we're still debating how to define it. The way I think about it is using data with the product mindset data to me is actually capability. It's an enabler, it's a tool. And then product mindset to me means that you build once, but you use multiple times.
Jess Carter (16:07):
I like that perspective because I do have clients where the data is an actual monetized, straight-up product, and where I really started playing with data was in state government. So it was like they were used to their systems and then we kind of blew things up and we were like, Hey, there is no system for opioid overdoses. There's no system of record for COVID or for infant mortality. These are human problems that span multiple systems at the state. And so we have this moment to say, hey, do you guys realize outside of your source systems, we could create a pipeline of a data product around these human problems where we can collaborate more fully? So that was more literally where I was sort of trying to open everyone's eyes to realize they own part of a product or they're federating part of a product. But I'm with you. I think nine times out of ten, more broadly speaking, treating data with a product mindset is exactly the right language. I totally agree with you.
The other things I was going to ask you is we talked about a comprehensive data strategy a little bit. When, if you were to come into a new firm, and I guess I'm talking in scenarios, but what I really want to know is what do you consider a good data strategy?
Veronika Durgin (17:27):
I think at first you kind of need to understand problems of the business in general. And then obviously kind of overlay which of these problems can be solved by using data, creating data products, using data as a capability to unlock some value. So depending, and we talked about different maturities, different organizations. For some organizations just getting data in a shape where it's well understood, easily accessible, like traditional FAIR—findable, accessible, interoperable, reusable—is enough. So that's like that data strategy. So it unlocks analytics, everybody's looking at the same dataset, they understand it in the same way. So that's like one. In other situations, and I think gen AI is a great example, how can data now be used to solve problems that couldn't be solved before or how to solve problems faster? And that's based on that and you kind of figure out where data fits in and what you need to build the foundation to solve those problems.
Jess Carter (18:33):
I love your definition. What bothers me about some of this is the amount of noise data that companies collect that doesn't serve them, isn't accurate or trustworthy. I tend to be like, either clean it up or stop collecting it. It kills me how much data exists and how little when we get in to help them with a lift-and-shift, or a system modernization, or an integration, or we're building out the mapping of the data model and where it goes. And it is the amount of data that is low fidelity, but there's such high volume blows my mind. So is it just a problem? How do you feel like we keep ending up there?
Veronika Durgin (19:16):
Oh my gosh. Well, first of all, I will admit I'm a data hoarder. I will tell you the other side to just collecting data that's sitting on the shelf collecting dust is, it directly impacts our environment, right? Data centers, CO2, et cetera. So it's bad and it's expensive. It's easy to collect data. It's a lot harder to make sense of it, to model it into something useful. A lot of times like data management, MDMs, they're like cleaning your house. You don't enjoy it. You do it because you have to. You're like maybe you clean some rooms, but not that closet, nobody can see it. So I think it's the same thing, unless there is, you are a very strong data leader where you actually dedicate time to this sort of stuff and you push your team to work on it, it doesn't get prioritized. It's not glamorous. You're not going to get a pat on the back. Collecting this data doesn't really, I mean, impact you quite yet.
Jess Carter (20:18):
Right? That's fair.
Veronika Durgin (20:20):
When our cloud providers will start charging us a lot of money, then it become a necessity. But right now there isn't the right incentive other than it's the right thing to do. And the right thing to do often falls below the line of, this is a project that's going to make us X amount of dollars. I think it's just hard to prioritize, but you have to make it like, I'm going to give you more knowledge, brushing your teeth, you just have to do it.
Jess Carter (20:48):
There's no value proposition for a lot of people. They're like, what's the point of expending X energy in that? If it's not a revenue-generating model or if it doesn't help us in some of those ways. Now to your point, yeah, there's no dollars behind it.
Veronika Durgin (21:01):
But it's interesting though, there are future potential dollars if you're sitting on valuable data, you're not using, understanding that it's valuable, putting it in the shape. This is like your monetization strategy, but it's hard to predict what the future value of this will be. So we haven't figured this out. I think if we figure out how to monetize and show value, then we'll start working on it.
Jess Carter (21:26):
I imagine in your career the number of data products, solutions, integration systems, you've worked on a routine challenge. Most people are familiar with stakeholder management. So one of the things I wanted to get your perspective on is how do you keep stakeholders engaged and aligned to your role, to your strategy? I imagine there are probably some short-term wins and some long-term wins, and I'm curious if you have approaches you found effective in maintaining collaboration and buy-in with those stakeholders.
Veronika Durgin (21:59):
I think the most important thing is to always have a conversation. So some conversations will continue falling flat because people on the other side of the table either don't have any incentive to work with you or they're not interested, or they simply feel that whatever is in their head is a better solution, better, et cetera. There are other stakeholders that are interested and engaged. I think the conversations just have to keep happening.
(22:26):
And the other thing that I'm philosophically pretty passionate about is I feel that data people need to talk directly to others. I don't believe there should be anybody in the middle. To me, it feels like that broken phone game where somebody talks and then they interpret it and then somebody else hears something and they interpret it. And I think the issue is where we build something that nobody asks for or they literally ask for something else. It just got lost in interpretation.
So I think we just have to be comfortable that some conversations will be successful, some conversations will not be. Some might take a little bit of time, but I'll also say that if we deliver something that is easy to use, this is how you get buy-in. It's just that first thing you need to give someone something that they're like, wow, this is great. We as engineers tend to give people something that is very complicated, and then we get in this kind of interesting thing of I don't like it. I want to stay with whatever I know already it works.
Jess Carter (23:33):
Yeah, yeah, you're absolutely right. And so I just had this conversation with someone the other day where we were delivering these dashboards to a team, and it was so interesting, Veronika. We got on the call, we were talking to the client and the client was like, hey, I'm super excited. This is great. But they were like, I want to remind you that the reason we began this engagement is because we needed a lakehouse. And so the dashboards were a way to convey that we have a lakehouse and it's productive and generating value. I went back and looked and I was like, this is such an interesting note about how well we continue listening to clients or to the needs of stakeholders because they said that in the sales cycle. We then drafted a contract where there was no mention the word “data lakehouse” did not exist in a contract.
(24:21):
We got really laser focused on the scope of the contract and delivering dashboards, and they were like, do we have a lakehouse? And we're like, you have an amazing lakehouse. We built such a pretty lakehouse for you. Do you want to see the lakehouse? And they're like, yeah, we want to see the lakehouse. And so to your point, making it super easy to access, super easy to use, and then also making sure we understand what they wanted in the first place. Stakeholders are asking for things. If anyone gets anything wrong, it's not the person asking whoever's hearing and fulfilling. And so making sure we ask the right questions. You said at the beginning, stay curious, ask all the, why does that matter to you? Why is it so important? What exactly do you want? You know what I'm saying? It's just exciting to me.
Veronika Durgin (24:58):
It's funny, like you say this, so they came to you and they said how they gave you a solution. You're like, no, tell me, what problem are you trying to solve? Why is it important to solve now? The how, we'll figure out the how, because we talked about the product mindset. Datalake house was the right product to build your data store for many whats and to support multiple whys, right?
Jess Carter (25:25):
Yeah.
Veronika Durgin (25:26):
It's another constant, a consistent thing we see, and we actually do it ourselves too. We show up with solutions, which is okay if you want to have a debate. When you start with a solution, it's easy to debate pros and cons of it. But when you have conversation with the stakeholders, people who are going to use whatever it is you're building, understand what they're trying to accomplish, not how they think you should accomplish it. Because ultimately, we're the data experts. To your point, data lakehouse was the how, and it wasn't really important. It was what problem you helped them solve.
Jess Carter (26:00):
And I think the other thing, too, is we're building some operational maturity. And it's been so interesting to take what you just said. There's real wisdom and a lot of people, you talk about knowledge management? A lot of people are trying to solve a very complex problem about where do you put the right information for people to come up to speed on anything quickly. With AI, I really think that will solve itself in a matter of time. What I've been fascinated by is behaviorally, back to your point, we're just trying to make the right things easy. So instead of creating this giant library, we're building automation so the right things pop right up in front of a user who needs to use them at the point in which they should be using them.
It's a totally different project. Instead of building this giant library with a whole bunch of tools, we're just building the very right tools, but building automation in our systems. And the difference is unbelievable when it comes to the adoption rates. And so I think to your point, as a data team, our job is to listen, understand the business, understand what it's trying to accomplish, super close to the strategy, and then help actualize it with the data we have at our disposal, which I just think is super exciting. This is fun. Is there anything else that we need to talk about that we haven't?
Veronika Durgin (27:10):
I was actually going to flip that question back to you. What are your strategies for managing stakeholders? I'm like, maybe I'll learn something. I need to learn something.
Jess Carter (27:19):
So I tend to joke, I am a Chicagoan at heart, so I live in Indiana, but I'm raised by a Chicago businessman. So I embody that. I'm very blunt, I'm very straightforward. So my gift to the universe if I have one is sensemaking. I can tell in a room if two people think they're on the same page, and I know that they're not. So a lot of what I try to do is I amp up my listening skills to be like, okay, the stakeholders said they want a lakehouse, but I have seven more questions about what exactly they want. And so it's like they think they've sent enough of a message. My job is to say, let's talk about how you want it, or let's talk about what your tech stack is, or let's talk about your budget and your costs and let's, hey, what's your strategy?
(28:06):
What are we building? What will be enough for you at the end of the year based on if your strategy is actualized? And just to poke holes at it and be like, I think we're done. And yes, I have an ability to exhaust my stakeholders. I have to be careful about my energy. But I think my job as a consultant is to make sure I'm not walking out of the room worried they've made the wrong call. So there is a moment where I'm like, hey, stakeholders, this is what you've said. This is why I'm worried about what you're asking about. Let's react to that. Walk me off a ledge. And then the hardest part for me, Veronika? Stakeholders change their minds all the time. They forget they made a decision. So on every call, I have a decision log and I'll have a little script that says DIS for discussion, DEC for decision, and INF for information.
(28:57):
And if whatever we talk about gets logged and we either had an informational conversation, a rowdy discussion, or we made a decision about X that it would behave like Y. And when they bring it back up, I'm like, hey guys, maybe that's the right call, but I want to remind you that a week ago you said X.
So for me, the other thing that's really important is being the thread that pulls through time that reminds stakeholders of what they decided and why. That doesn't mean that things haven't changed and we shouldn't change it. I want to be their consciousness of exactly what they've already invested in and why to make sure we're still heading it. So alignment, constantly talking to my devs, making sure they understand my engineers, what are we doing and why are we doing it? Because they're going to get into the thick of it and you know this, and they're going to realize that whatever we thought we were building isn't going to serve the client in some way. So I think just asking all the right questions and that's why we joke. That's why I call it interrogation, Veronika.
Veronika Durgin (29:54):
I know for the audience, before we started, Jess said, I'm going to interrogate you today, which I absolutely loved, but you also described a little bit master data management, but in a more understandable way of you are trying to align on definitions and how things connect with each other and just document. Is it natural to you? If so, I am like, do you use specific patterns or methodologies for the sort of conversations? It's like workshops and they can often be pretty hard if you don't prompt people in the right way with the right questions.
Jess Carter (30:30):
Yes.
Veronika Durgin (30:31):
So I'm just curious if you can recommend any books to read or any things to research.
Jess Carter (30:38):
So I am trained in Six Sigma. I have a black belt in Six Sigma, so I have a black belt in thinking about business processes. So Pareto charts, understanding a problem, my flares go off immediately when I hear anyone present to me about how they've figured out what's going on. And I'm like, that's a hypothesis unless you have data that proves it. And so I'm really big on we're in business for a reason. We can make educated guesses, but we need to label them as educated guesses. Do not sound overconfident about something if you don't have the data to back it up. So I tend to just think in systems. So if I hear that we're going to come in and there's going to be a problem we're going to discuss technologically, usually I want a pretty basic, it's really pretty simple. It is a pre-read, it is back to who, what, where, when, why. What do we know?
(31:27):
And I also want to know what do we not know? What is not understood yet about this problem? Is it a really simple fix? We've already identified it, we just need someone to author sign it? Or is this uh-oh? We have a big scary problem and we do not know what's causing it. I'm going to approach those things very differently. I also immediately jump to what is the business impact? When is it realized? Do we have a quarter to figure this out? Do we have two days to figure this out? So I'm constantly quickly trying to figure out how big is the problem, how severe is the problem, and how much time do I have to figure out what I'm going to do about that problem? If I can figure those three things out before the first meeting, I feel really good. Usually I get like 60% of that.
(32:14):
And then we go to the first meeting and we start, we uncover the rest. But I would tell you my approach generally reflects Six Sigma or IDEO "start with empathy". So I care deeply about is a customer experiencing something or is this a, hey, our environments are going to cost more in a quarter if we don't think about our cloud storage, whatever. So that's where I do think about customers first. I do think about how big is that problem? How urgent is it? How much time can I buy? And then I get into the detail. And I will say, I am also a doer. The detail does not scare me. I think that every leader should be able to roll up their sleeves and get into the detail. I always had a rule working with developers where I said, if I can't understand what you're estimating or what your technical solutions and challenges are, that is not my problem because I'm super good at hanging with you. I need you to actually help me understand it. So a lot of the people who've worked with me will say, they're used to hearing me say, I'm not there yet. Help me get there. Let's try it again. And I am unashamed. I will ask that six times if I don't get it. That's why I take home a paycheck and I take it seriously. So if I don't understand it, I need to. So that's the other element is I think there's just some grit of once we're on the bus, we're going somewhere.
Veronika Durgin (33:33):
I love it. Thank you for sharing. And it sounds like your mind works that way, but one of my favorite, and you touched upon this phrases as a leader, we don't have to be in the weeds, but we need to know who lives there. And I'm also very similar to you where I'm like, I am not going to apologize because I don't feel sorry, but I'm going to keep asking you because it doesn't make sense to me quite yet. It will at some point or at some point I'll get to a point where I know it will never make sense to me and I'll just lean on the experts. I can get annoying like that, too. So I'm with you on that.
Jess Carter (34:08):
I love that you just said annoying, right? I don't know that everyone loves it, but it's part of the job. Right? Okay. This has been a delight. Thank you so much for the conversation, Veronika.
Veronika Durgin (34:18):
Thank you for having me. It is been the most enjoyable interrogation. I've never been interrogated before. So if all of them are like this, it's wonderful.
Jess Carter (34:29):
No torture in this interrogation, I hope.
Veronika Durgin (34:31):
No, no, not at all.
Jess Carter (34:33):
Okay. Well thank you for being on here. If people want to follow along on your journey and your career, what's the best way to stay in touch with you?
Veronika Durgin (34:40):
So I think LinkedIn. I am old fashioned that way, so LinkedIn is probably the best way to kind of keep up with me. I also started blogging, talking about things that I am not comfortable writing at all with the help of wonderful neighborly, ChatGPT. I'm barely a mediocre writer, but I love telling stories, so...
Jess Carter (34:59):
Awesome.
Veronika Durgin (35:00):
If you want to read my whatever I produce, you can find me on Medium or Substack. I joined the cool kids very recently.
Jess Carter (35:08):
Awesome.
Veronika Durgin (35:09):
But LinkedIn is probably the best place.
Jess Carter (35:11):
Okay. We will get those links. And I want to get a link to the book you mentioned earlier. We'll put it in the show notes so people can access all of that very, very easily. So thanks again for being here.
Veronika Durgin (35:19):
Well, thanks so much for having me.
Jess Carter (35:21):
Thank you for listening. 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.
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