Ep 258: Bringing Audit Data to Life with Visualizations w/ Valerie Ann Zappia (Victoria’s Secret)

Summary

The Audit Podcast – Ep 258: Bringing Audit Data to Life with Visualizations

Guest: Valerie Ann Zappia (Internal Audit Data Analytics Manager, Victoria’s Secret)
Host: Trent Russell
Date: October 7, 2025

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Episode Overview

In this episode, host Trent Russell talks with Valerie Ann Zappia, the internal audit data analytics manager at Victoria’s Secret. The conversation centers around practical strategies for integrating data analytics and visualization into audit, navigating common challenges—like data quality and tool selection—and fostering analytics skills among non-specialist auditors. Valerie shares personal techniques, success stories, and actionable insights for anyone looking to elevate their audit function using data, all peppered with real-world examples and her signature humor.

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Key Discussion Points & Insights

1. Using AI Tools in Professional and Personal Life

• Professional goal-setting with Copilot:
  Valerie uses Copilot to help define her annual professional goals, leveraging the AI to build upon past objectives and make the process more dynamic.
  Quote:

  “I put my job description into Copilot and said like, what are four or five solid goals for this role?... I kind of like went back and forth with Copilot to figure out what my goals were going to be for the year. And I thought it was super helpful.” – Valerie Zappia [01:47]

• Personal life hacks with ChatGPT:
  Beyond work, Valerie uses ChatGPT for house-hunting, letting the AI analyze Zillow listings, suggest bid strategies, and review home disclosures for smart home-buying decisions.
  Quote:

  “I use ChatGPT personally. So I'll put the Zillow link into ChatGPT and say, can you do a market analysis on this house?... It took less than a minute for it to give me that information.” – Valerie Zappia [02:44]

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2. Handling Data Availability and Quality Issues in Audit

• Building approachable relationships:
  Valerie finds that being approachable encourages openness in data sharing across departments.

• Requesting better data, not just fixing bad data:
  Instead of laboriously cleaning data, she asks data owners to walk through and even improve their reporting processes, resulting in cleaner data upfront.

• Learning over time:
  Earlier in her career she fixed whatever she was given; now she feels empowered to tactfully “push back” and get better quality raw information. Quote:

  “Sometimes going back to that person and saying, like, can you just walk me through how you actually pull this report?... I've gotten customized reports either created for me or had helped them create new reports to organize the data a little bit better to save me time on my end.” – Valerie Zappia [05:16]

• Video documentation for completeness & accuracy:
  The team sometimes asks data owners to record their process for data pulls, enhancing documentation and auditability. Tools like Loom and Teammate make this easy.
  Trent Russell: “Just record a video, show us how you are pulling this, and then you have the completeness and accuracy part of it documented.” [06:44]

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3. Tool Selection for Data Analytics

• Reality vs. ideal world:
  Valerie values Tableau’s customization (which she used in prior roles and formal classes), but at Victoria’s Secret, tool choice is driven by cost and existing licenses—thus, Power BI is standard thanks to Microsoft packages.

• Adaptability as a skill:
  Regardless of tool, the key tasks remain: summarizing data, joining datasets, and using functions (e.g., pivot tables, VLOOKUP). Quote:

  “If I could go into every job and they could give me Tableau, I would be so happy. But that’s not how the world works.” – Valerie Zappia [09:46]

• Maintaining relevance:
  Valerie regularly attends free conferences (e.g., Tableau, Google Looker), highlighting the need for constant learning in a shifting tool landscape.

• Host’s tip:
  “Whenever I get a new tool, I go, how can I join data? How do I summarize and put stuff in a pivot table? And then I’ll just use the Internet to figure out the functions.” – Trent Russell [10:24]

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4. Upskilling Audit Teams and Promoting Data Literacy

• Embedded analytics goals:
  Valerie’s team now requires every member to set a data analytics goal annually, using AI tools like Copilot or ChatGPT for support.

• Required training:
  Each auditor must complete annual CPE hours, with at least half devoted to specialized skills like analytics or IT.

• Instilling analytics thinking:
  Audit procedures now include prompts asking if data analytics was considered; Valerie actively encourages her team to seek her input and try new approaches. Quote:

  “I am trying to get my team to the point where they can recognize that I can help them with something... I just need them to be able to recognize like hey, this is an opportunity... reach out.” – Valerie Zappia [12:22]

• Challenge:
  People naturally tend to repeat what worked before. Valerie is working to turn that on its head.

• Wish list from peers:
  As echoed by Trent’s conversation with another analyst, the ideal is for every auditor to spend even “two minutes” thinking about how analytics could enhance each audit.
  [14:18-15:36]

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5. Aligning Analytics with Broader Business Strategy

• Securing buy-in:
  Valerie’s leadership proactively champions her work, referring her to business contacts across the organization.

• Show, don’t just tell:
  To build credibility and spark ideas, she creates demo dashboards with public data (e.g., Google Trends for Victoria’s Secret, Kaggle datasets) to show stakeholders what’s possible. Quote:

  “One thing that I did when I first started was I created some dashboards just using dummy data that I found on the internet... If you can give people tangible examples, they will be more willing to work with you.” – Valerie Zappia [17:30]

• Connecting through relatable topics:
  Valerie likes using engaging data sets—like Taylor Swift’s song stats or Google Trends search history—to teach and engage both business partners and audit teams. Quote:

  “I made a presentation on data analytics and visualization using data that I pulled from that Kaggle website on every single song that Taylor Swift has ever put out... They were able to connect to it a little bit.” [21:06]

• Host’s tip:
  Using personal interests (Trent did fantasy football data) helps make learning tools and concepts fun and sticky, and the skills transfer seamlessly to business data. [24:20]

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Notable Quotes and Memorable Moments

• On pushing for better data:
  “It’s going to take me hours sometimes it’s going to take me days. But sometimes going back to that person and saying, like, can you just walk me through how you actually pull this report?” – Valerie Zappia [05:16]

• On tool pragmatism:
  “If you can do pivot table, VLOOKUP, and use the internet to figure out the function, you do that in every single tool.” – Trent Russell [10:24]

• On analytics culture:
  “I am trying to turn that on its head. I always try to go in team meetings, too, and if there's something little that I can, like, present to them... But I try for them to just remember, you know, to reach out.” – Valerie Zappia [13:38]

• On making learning fun:
  “I probably spent way too much time on it. But whenever I was giving the presentation and teaching people about all of the visualizations and things, they were able to connect to it a little bit. Because you would think at least everybody in the room, like, knew Taylor Swift, who she was, you know?” – Valerie Zappia [21:06]

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Timestamps for Key Sections

• 01:47 – Valerie’s AI usage for professional goal setting
• 02:44 – ChatGPT for house hunting and personal productivity
• 05:16 – Addressing data acquisition and quality issues with stakeholders
• 07:48 – Tool selection: Tableau vs. Power BI and learning new tools
• 10:24 – Universal analytics tasks and learning approaches
• 12:22 – Upskilling and embedding analytics into the audit team
• 14:18 – Auditors’ mindset shift: thinking differently about analytics
• 17:30 – Building buy-in with other business units, creating demo dashboards
• 21:06 – Using Taylor Swift and relatable data to teach analytics/visualizations
• 24:42 – Host’s advice: Use hobby data (e.g., fantasy football) to learn tools

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Closing Thoughts

Valerie wraps up with advice that bridges both career development and analytics: openly tell people about your goals. This transparency draws support and serendipitous opportunities—her own path to Victoria’s Secret came thanks to her mother remembering her childhood dream job.

“If I wouldn't have said all of those things out loud, she still remembered what my dream job was when I was 10 and how many, you know, 20 years later, she helps me by throwing that in my inbox.” – Valerie Zappia [25:46]

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Takeaways

• Be proactive and approachable in data relationships—don’t just accept the status quo.
• Choose tools pragmatically, but keep your skills adaptable and up-to-date.
• Infuse analytics into everyday audit thinking, even for non-specialists.
• Use relatable, fun data to deepen understanding and engagement.
• Share your career goals—you never know who might help open a door.

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For more tips and the latest in audit analytics, be sure to subscribe and check the show notes for additional resources.

Transcript

[A]

I think the thing that has been the most helpful for me, not only in my career, but personally, is telling the people around you what your goals are and what your interests are and what you hope to achieve in life. And, I mean, your friends, your family.

[B]

Hello, everybody. Welcome to another episode of the Audit Podcast. I'm your host, Trent Russell. Today on the show, we have Val Zappia. Val is the internal audit data analytics manager for Victoria's secretary. And Val spoke at the spring audit analytics and AI conference in 2025, and she had a ton of questions from folks, so much so that she didn't have time to get through them all. So we said, we'll just do an episode and we'll answer all these questions that people from the audience had. And a lot of these are the probably the most typical questions also that we still hear. And so that's why we want to get Val's perspective on it. Some of the topics that we hit on that we still hear most common from folks, the issues that they're having is data availability and quality issues, how to deal with that, how to go about selecting tools. People love talking tools. And so there's a lot of tools questions, what Val's doing to upskill the rest of the team. So the non DA folks on the team in internal audit, and then how to align the analytics and audit priorities with the priorities of the organization or aligning those to the strategy of the organization. All right, with that said, here we go. What's in your. Either your Internet browsing history, your chatgpt, your copilot history. Yeah, whatever. LLM preference. You have like a personal prompt or. And a. Like a professional one that other people can go, oh, that's cool. I should try one of those.

[A]

Yeah, I guess professionally, because I just did this. I have copilot, and I obviously, like, I never know what to put for my goals for the year. Like, that's always something I feel like, like, you know, I mean, like, people ask you, what are your goals for you? You're like, I have no idea. So when I initially started my role, I put my job description into copilot and said, like, what are, like, however many goals you need, like, what are four or five solid goals for this role? So then this year, because this will be my second year in my role at Victoria's Secret, I put my goals in from last year and I said, hey, these are my goals for last year. Like, what's a good idea to build upon these? So then I kind of like went back and forth with Copilot to figure out what my goals were going to be for the year. And I thought it was super helpful.

[B]

I like that. Goals are always super tough. Like I don't, I don't know. I mean, I don't know. Yeah.

[A]

So I always give you a number of, like you have the, you need four or five goals and sometimes you can only think of two or three. So that was incredibly helpful. And then personally, as I told you before this started, I'm looking for a house and I am a first time homeowner so I don't know what I don't know. So now that I've been at this for a couple of months, I will put the Zillow link into. I use Chat GPT personally. So I'll put the Zillow link into chatgpt and say, can you do a market analysis on this house? Do you think it's priced correctly and what do you think I should bid? And let me tell you, the last house that I bid on, the reason why I didn't get it wasn't because of the price. It was because somebody had waived inspections. And of course I want the inspections. But I thought it was super helpful and at least like that time with my bid, like I had like I was spot on with the price and it told me like, hey, this is what we think would be a good bid. So super helpful. Yeah, no, it's crazy. And they were like, these are all comparable houses that have just sold like down the street and all of those things. And like, yeah, that's stuff I could look up but I mean it took less than a minute for it to give me that information too.

[B]

Yeah. And the amount of stuff that you have to know like we were talking about before we hit the record button and buying a house is insane. We definitely made mistakes. We use, I mean, stupid us. We had to use Google 10 years ago, you know, 15 years ago. And it was just, it was not as complete, I think, as if we would have used any kind of LLM to go like, give me a checklist, I'm buying the house. What's all the stuff I need or.

[A]

Even the disclosures they send you the disclosures over. I put those directly into ChatGPT and I say, what question should I ask from this and what should I glean from it? So it's, it's been my buddy on the back end as I go through this process.

[B]

Those are solid. I like that. I think that that's going to help somebody.

[A]

I hope so. Yeah.

[B]

On the analytics front. So like I said in the intro, you spoke at the Auto analytics and AI Conference in the spring. You had, I think you had more questions than anybody else during any given session. And so we took all your questions and kind of picked them out and said, look, we still get asked these questions also all the time. And so that's really what this is about, is addressing the most common questions that we get in audit analytics or in an audit analytics role. So probably no surprise, I'm sure you've gotten data that is either it's tough to get, it's data acquisition's tough, or you get it and it's complete trash. How do you deal with that?

[A]

Yeah, I guess I always, first of all, I try to be really approachable in my role. I think in what I do, sometimes people are like, I don't understand what you're doing. So they're very hands off and, you know, they don't want to give me information. So first of all, I try to be really approachable. I think for me, like, yeah, I can clean up a data set. It's going to take me hours sometimes it's going to take me days. But sometimes going back to that person and saying, like, can you just walk me through how you actually pull this report? Where does it come from? I've gotten customized reports either created for me or had helped them create new reports to organize the data a little bit better to save me time on my end. Whereas, like, they're giving me a report that's a little bit cleaner. Like, maybe I'll have to add a column or two, but I'm not going to have to do anything crazy. And plus, I think having that auditor brain, I do like to see the process still of like, this is how they're pulling things, they're pulling it together, completeness and accuracy, all of those things. So. So I think that has what has helped me the most. I think earlier on in my career I would just accept the messy crap data that they were giving out to me and just spend hours and days fixing it. So I don't know if it's like, I've moved up and I'm a manager now, so I feel a little bit more confident, like pushing back a little bit and being like, hey, so like, how did you do that? But I found that super helpful, I.

[B]

Think, as a process. And we started doing this really just before even pandemic and work from home was rather than, hey, we're going to come find you in your office and sit with you and Watch you pull it because that. You got to schedule that, you know, and it can be a pain. Just, hey, when you're good, like, just record a video, show us how you are pulling this, and then you have the completeness and accuracy a part of it documented. And if you want to take screenshots, pause the video, take a screenshot. And even now with tools like Loom or I think Teammate does this where you can record like a video that's a link and send that way way easier. And then you again, you just pause it or you just drop the link straight in. We do that all the time. Just drop the link straight in. Here's my procedures. Okay, I like that. That's good.

[A]

All right.

[B]

What about on tool selection? How do you go about selecting tools? Or. Because tools is always like, the hottest topic on any conference we go to, when it comes to AI and analytics, it's always about tools. My guess is, and you can elaborate, the tools that you have are basically whatever you were given.

[A]

I was going to say, this is such a hard question for me to ask her, because what I want and what I actually get are two different things. So I have my master's in data analytics, and I've taken classes on tableau. So, like, when I was in school, that was their preferred software, and that's what I was taught. And. And in one of my previous roles, they had tableau licenses that were very easy for people to get. That's what we used. And I made my best dashboards in that job in tableau. Since then, the companies that I've worked for have had Microsoft licenses. And I find that when they have those micro big Microsoft packages, you know, everything's included. So, like, they don't want to go out of their way. I get it. And pay extra for you. I think it's $70 a month for only your license, and then you have to pay $15 extra per person. Whereas with Microsoft, you know, if you're just using Power Bi, everything's included. So since then, I've had to use Power bi. Have I figured it out? Yes. You know, I've obviously made, like, great dashboards in Power Bi, but I think that Power Bi is really easy if you know all of the Microsoft products. I mean, I taught myself how to use Power BI by watching YouTube videos. Nobody taught me how to use it. You know, I think with tableau, it took a little bit more time. Like I said, I took a class on it, and that was super helpful. But tableau is more customizable, and I Think that's why I like it, because I love this stuff, you know, and I'm in the weeds. But if you are someone that's kind of doing this on the side, or people tell me that data analytics is their side hustle and internal audit and Power BI is super easy for you to teach yourself. So it's so hard for me to answer this question because if I could go into every job and they could give me tableau, I would be so happy. But that's not how the world works.

[B]

Yeah, I forget the name of it even. But when I was in school, we had something. It was like this tool and tableau, and those were the two that were competing. And it was like, whoever wins is going to win the market. And so we had the license for the other one. And then within like two years, I was like. It was when I was in public accounting, we had that thing, and then all of a sudden it went away and we had tableau. And I was like, okay, well, I guess we're using this now. So I think when it comes to tools, what I always try to tell people is regardless of the tool analytics, you're going to summarize data, you're going to join data and you're going to use the various functions within the tool.

[A]

And.

[B]

And so if you can do those three things, I pivot table, vlookup, and using the Internet to figure out the function, you do that in every single tool. And so whenever I get a new tool, I go, how can I join data? How do I summarize and put stuff in a pivot table? And then I'll just use the Internet to figure out the functions. And that's like it. So it doesn't really matter. The tool doesn't really matter. But yeah, for sure. There's certain. Everybody's comfortable with.

[A]

Everybody has their preferences. I think too, I try to stay relevant in all of them. Like, Tableau has a free conference every year. I always attend it, even though I don't use tableau every day. Google Looker is what people use if they have Google products. You never know when they're going to switch things on you. So you, you have to be flexible. You have to be agile. So I try to stay updated in all of the tools because you're going to learn something. Everybody has some sort of free conference or whatever, but for you to attend. So I try to keep updated in the whole space.

[B]

Hey, everybody, we're going to take a quick break from our guests, and if you need to get analytics or AI actually working in your internal audit department or if you already have some of it, you feel like you're not really getting exactly what you need out of it. You know there's more you're not getting that. Go to the show Notes, look for the Green Skies analytics link. Click it on the website. There'll be other links that you can click that'll take you directly to a calendar to schedule time. It's literally three clicks to get the time scheduled to get it figured out. All right, back to the show. Nice. What about in like other people? Maybe they're, they're side hustling like you said, or you are trying to increase the analytics competency within your team of the non auditors. That's a classic question. Everybody gets that. What kind of steps have you taken, what kind of programs have you implemented to go, hey, you're a two different data literacy wise, this is how we're going to get you to a 6, 8, 10 right.

[A]

So I guess the approach that I've taken with my team is I am the only data analyst. I always say I am the team. It's just me right now. So I am trying to get my team to the point where they can recognize that I can help them with something. I don't need them to be able to do the analytics or crunch the numbers or anything like that. I just need them to be able to recognize like hey, this is an opportunity develop to help me with a project. So the steps that we have taken is everyone on our team is now required to have a data analytics goal. So circling back to the beginning with, you know, using copilot, I encourage them all, hey, try to use a copilot or a chatgpt. Figure out a data analytics goal that you would be comfortable with for the year. So everybody has that they're supposed to complete in 2025. Ask me again in a year how that goes. But that's a start that we've had. We do have required training hours for everyone as well. Everybody on the team. Most of us have certifications but if somebody doesn't, they are still required. I think we get, everybody has to get 20 CPE a year and 10 of that has to be some sort of like specialized more knowledge, whether it's like you're getting like it specific training, data analytics, specific training, that sort of a thing. So we've incorporated both of those and then we use Audit Board. So. So we actually do have a question in Audit Board now that says was data analytics considered for this? So I'm trying to hit them at all points and just remind them to think a little bit differently. And I think that's the hardest part, is everybody just wants to keep repeating what they did last year. And I'm trying to turn that on its head. I always try to go in team meetings, too, and if there's something little that I can, like, present to them or I'm always sending them emails, they're probably sick of me sending them emails of different things that I see that I think are really cool. But I try for them to just remember, you know, to reach out.

[B]

And I don't even think it. You said think differently. I was talking to another audit analyst solo, similar to you. Was the only one, super, super good. Very, very good at what they do. And I asked him, I was like, what would. Like, if you could have anything in your role, what would it be? And I was, you always expect, like, I want this tool, or, you know, something. Basically, tools is what I always expect. And he's like, if every auditor would just take, like, two minutes and think, how could we use analytics here? Or even just do this audit differently, as opposed to, all right, roll it forward. Tested the same. Check the box. You know, hey, we're done with this. Let's go. He said that was like. That was his number one thing on his wish list, was that because once you have that and they send the projects to you and there's. It's not super fun when the auditors don't communicate to the analyst and say, hey, I have this idea. Can you build this thing for me? And especially if it's like, this is what we think the ROI is going to be or the value it's going to bring. That is typically one of the toughest things that we hear from, regardless if it's a solo analyst or. I mean, there's a team of 12 hardcore data scientists on that team. No audit background, and auditors and the analytics team, they might as well be on different planets. It's like nothing.

[A]

And that's the thing. Like, so right now I'm working for a retailer, and I come from financial institutions, you know, so my background is heavy finance. Like, I was a TA for my dad. My dad taught a. My dad taught a derivatives class at the University of Pittsburgh, and I was his ta, and I learned all of that stuff. So I worked for CME Group. So you would think that I would, you know, as an audit analyst at CME Group, be able to recognize that, like, these are the different opportunities that I would be able to help with audit analytics and all of these like derivative instruments and things like that. But even in that situation, I still wasn't an expert. Like I still needed the people on the team to be like, hey, Val, this is a situation where I really think that you would be helpful. So I think that that is kind of number one. You have to get your team to understand that, that you're not going to be able to read their minds and know ex what every single person on the team is working on. So I, like you said, I totally echo that. I just want people to think a little bit differently for one or two minutes of their job.

[B]

So that's what the new process and Audit Board is going to be. It's not. Did you consider.

[A]

That's what I'm hoping. That's what I'm hoping. I just need the Y. Yeah. Yes or no.

[B]

Yeah.

[A]

So hopefully they look at it beforehand and they're not looking at it after and they're like, I forgot to reach out to Val.

[B]

Yeah, we'll have to talk to Audit Board, see if we can get a timer functionality added to Audit Board. Just say, click the timer. Two minutes.

[A]

Yeah. How long did you do it?

[B]

So, so we just talked about how to get internal audit to work with you, but there's still the rest of the company that has to work with you to some degree. And so how, how have you gone about getting the buy in from those outside of internal audit when you have to get data and rely on them for their business expertise?

[A]

So I guess I, I'm very fortunate in my role that my boss and his boss, I always joke that they're passing out my business card. They will always give me scenarios where they said, oh, Val, I talked to this person and they think that maybe you can do this. They're always throwing different ideas my way. So I'm very fortunate in that. But in other scenarios or even when I first started this job and I was trying to get my feet wet and you know, get people around you to trust you, because I think that this role can be a little bit creative in that people don't know what kind of visualizations that you can create in these sorts of jobs. It's not like you have some sort of portfolio that you're submitting for these roles and saying, I'm very good at like creating these videos and this sort of, you know, all, all of this stuff. So one thing that I did when I first started was I created some dashboards just using dummy data that I found on the Internet. So I went to Google Trends, and in Google Trends, you can see how many times something was Googled. So I put Victoria's Secret into Google Trends to just see over the last 20 years, what did the Google searches on Victoria's Secret look like in every state. And so I kind of made this dashboard to show to not only the people above me, but my team. Like, this is an example of something that I can do. So I think that if you can give people tangible examples, they will be more willing to work with you. And I think that ideas will spark in their head. So I've used Google Trends data. I've also used the website Kaggle before to pull data sets and just play around with it and create a dashboard and just show people, not just explain what. How I can help them. Essentially.

[B]

I looked up Google Trends as you're saying that, because I used it once to scope an audit to see when the timeframe. So we're doing this opioid audit, and I wanted to know when opioids really took off as a term and became, like, publicly known, because that's where there's, like, intervention from regulators on opioids. So I want to be like, all right, well, if we look prior to that, it's probably where some fishy stuff might be happening, happening. And so we did that. There was two points. I'll ask you this. On April or April of 2016 was when the first spike of people googling opioids hit. Okay, what do you think the event was?

[A]

2016? April 2016, you said.

[B]

And all the trainings I've done where I used to ask this all the time, only one person ever got it.

[A]

Right has gotten the event right. See, I think this is hard because this is when I was in college, so I was not watching the news. I was not paying attention to anything that was happening outside of that bubble.

[B]

Got it. The artist formerly known as Prince OD'd on opioids, and that's when it, like, hit the news. So that was like, the event that woke up.

[A]

My mom would be so upset that I did not know the answer to that because she loves Princess.

[B]

Tell her the story and then ask.

[A]

I will. And I'm going to ask her the question.

[B]

Ask her what April 2016 nopioids are. And then the other one, I think, was in November of 2017 or October. And that's when, like, the national emergency of like, hey, this is a huge issue, and we're going to fund all the regulations and all that kind of stuff around it. So.

[A]

Oh, that's so funny. But it's interesting how I got down the rabbit hole of even finding that website because I was doing a presentation on data analytics and visualization basics. So I. It was for the iia and I had to go in person in somewhere in Columbus and give them a presentation. And the first thing that I thought was, I do not want to be boring. I want to make sure that everybody comes away learning something. So how am I going to do that? And another thing that I love other than data and visualizations is Taylor Swift. I'm a huge Swiftie. I went to the Aerostore three times. So I was trying to think of what other thing am I an expert in that I can speak to, because I like to be able to speak to my data. And I obviously couldn't give Victoria's Secret data out. So I made a presentation on data analytics and visualization using data that I pulled from that Kaggle website on every single song that Taylor Swift has ever put out. And there were different metrics on there about they had dance ability and how long the song was and all of these different things. So I did this whole analysis. I probably spent way too much time on it. But whenever I was giving the presentation and teaching people about all of the visualizations and things, they were able to connect to it a little bit. Because you would think at least everybody in the room, like, knew Taylor Swift, who she was, you know? So I think that if you can kind of find that piece of connection to teach people what you're doing in a way where they can at least remember it, I think that I've found that really useful. And so many people have reached out to me about that presentation because I made a LinkedIn post when I was done about, like, some of my findings, just because I thought that it was interesting. I've had so many people reach out to me and say, that was really cool. Or I remembered how to do this visual because you taught it this way. Yeah.

[B]

All right, I'm going to try to wrap up the Taylor Swift conversation.

[A]

There was probably go on too long. Yeah.

[B]

Or I mean, like years and years. And at least four years. I feel pretty confident in saying four years. When I would go to Google Trends, the homepage was always, it says Taylor Swift.

[A]

Yeah.

[B]

It was a map of the US and it was color coded on which states Googled Taylor Swift versus which states Google Googled Beyonce. Those were the two. And it was. I think that was who. I know it's Taylor Swift. I think it was Beyonce for like Years. That was the forever. The other thing was I used to use this Taylor Swift visualization and one of the very first like trainings that I did, sessions that I did and it was, I got it off of Reddit, but it was a bar graph of. It had all of her albums on the x axis, so the bottom. And then on the Y it put the number of references to drinks and.

[A]

So there'd be hilarious.

[B]

Bourbon, tequila, champagne, whatever. And you could see over. Because of what she started when she was like 12 or something.

[A]

Yeah, she was super young and it.

[B]

Just goes up, you could see over time. And then she hit like 21.

[A]

It was just like just skyrocketing in. That's hilarious.

[B]

Let me bring this around to make it to close the loop on this. So. Because this is what I always recommend to people also when it comes to analytics. You did it with Taylor Swift data. That's something you're interested in. It's a hobby. It's interesting. You see an insight, it clicks for you a little bit more than it would if it was. I don't, I don't. Whatever. I don't know. The growth rate.

[A]

Like financial data. Yeah, that's so boring. Yeah, yeah.

[B]

Mine was fantasy football data. There's a ton of it out there and so much I was still like, I'd was a decent analyst but I had this new tool like I'd upgraded to this pretty slick tool and I had to learn it. And so instead of doing yeah like audit stuff in it, I did like my whole fantasy football draft pre draft rankings and stuff using this tool. And then by the time I was done with it, I was like, oh well, yeah, I'm pretty comfortable with this. So I always tell people like, pick a topic that you're interested in, a hobby, something like that. The same can go for AI and using LLMs. And that's where you're really going to have like the most fun and most interest. And then that translate pre translates pretty easily to what you do at work.

[A]

Exactly.

[B]

All right, so I think for sure those that are listening, especially if they are in like an analyst role, will go, yep, I've heard that question a thousand times and that one and that one and that one. So hopefully we've helped a lot of folks in answering some of these very, very common questions and kind of your approach to them and a bit of my approach to it also. So thank you a ton for that. With that said, what else do you want to leave the audience with? We'll let you close out the Show.

[A]

I think the thing that has been the most helpful for me, not only in my career, but personally, is telling the people around you what your goals are and what your interests are and what you hope to achieve in life. And I mean, your friends, your family. And my best example is when I was growing up, I used to tell everybody that my dream job was I wanted to be the CFO of Tory Burch. And that kind of evolved to. I wanted to work in the finance department, a big brand. And when I went to college, one of the first things that I did was every single person that went to Wake Forest and worked for a brand that I recognize, I sent them a LinkedIn message just to talk to them about their career. And I think at the beginning of my career, like most people, you get overwhelmed, you get sucked into it, you know. And I was working in various fields, financial institution, jobs. I liked it. Yes. But was that what I was passionate about and what I really loved? No. But I kind of lost sight of that. So I was sitting in my office and I was working in Chicago at the time. I was working late one night and I get an email from my mom. And the title of the email is, if you don't apply to this job, I have your resume and I'm going to submit it for you. And it was my current role in Victoria's Secret. So if I wouldn't have said all of those things out loud. She still remembered what my dream job was when I was 10 and how many, you know, 20 years later, she helps me by throwing that in my inbox. Just. I don't even know how she. I don't know how she finds things, but she does. So she found that. And I just find that with other things in life too. It's really nice to have that support of family and friends around you. So.

[B]

Hey everyone, thank you very much for listening to this episode of the Audit podcast. Whatever platform you're listening on right now, I'm sure there's a subscribe button somewhere, so please hit the subscribe button there. If you're listening through itunes or Spotify, feel free to go give us that five star rating. It only took me about 16 seconds to give myself a five star review and it really helps to get future guests to come on the show, so we'd really appreciate that. Lastly, be sure to check out the show notes and follow us on all our social media channels, on Instagram, on LinkedIn, and on TikTok. Also, if you're interested, please sign up for our weekly newsletter from the Audit podcast. Thank you all. Have a great one.