How the 2026 Iron Viz champion built her winning viz — in twenty minutes

0:00 Ann Stolzman: Welcome everybody to Data and Women US West Coast. Uh, today is August 18th, it's Tuesday, uh, and we're here to talk with Ann Pregler.

0:13 Um, if you haven't had a chance, I believe that's our QR code for our icebreaker. Feel free to keep scanning. I'm Team Ghost.

0:22 No pressure.

0:23 Ann Pregler: Of course.

0:25 Ann Stolzman: I'm kidding.

0:26 Choose what you'd like. All right. So our agenda today, just kind of talking about who we are. We'll go over our resources that we created.

0:41 Um, and you can add to these resources. It's kind of like what we've used to kind of, um, keep us up to date on Tableau and learn how to be better with it.

0:52 Um, and then we're going to be speaking with Ann, who…

0:57 Ann Pregler, who is going to be talking about Iron Viz. We have two Annes here today. Do we have another Ann? I don't think so.

1:04 Um, and then our next session is going to be in October, um, with Celia. Um, so we'll be talking with her on October 20th.

1:21 So about this user group. We sort of came up with this while we were at Tableau 2023? No.

1:31 Ann Pregler: Five.

1:32 Ann Stolzman: Five. Wow, okay.

1:35 Tableau 2025, we were there. We were like, "Hey, there's no Data and Women on the West Coast of California. We should make this a thing." So we decided to do that, and our goal is we want to, um, promote women, and women speakers, and talk about data, dashboards, and all things related to data. Um, and we wanted it for a time that's convenient for people on the West Coast, because we have East Coast, I think we have other locations, but we don't have a West Coast, we wanted to be inclusive.

2:10 And of course, anybody who's interested is totally welcome to attend.

2:15 Okay. And so on to our speaker, Ann Pregler. Ann, did you wanna take this one away and introduce-

2:23 Ann Pregler: Sure.

2:24 Ann Stolzman: Ann?

2:26 Ann Pregler: Okay. Uh, yeah. Hi, I'm Ann Pregler. Um, shoot, I was not prepared to start talking yet. Um-

2:31 Ann Stolzman: Oh, that's okay.

2:32 Ann Pregler: But I can, uh-

2:33 Ann Stolzman: I… The, the point was I introduce you.

2:36 Ann Pregler: Oh, wrong Ann, I'm sorry.

2:37 Ann Stolzman: Yeah.

2:37 Ann Pregler: Two Annes. This is very confusing.

2:39 Ann Stolzman: It's very confusing, I know.

2:41 Ann Pregler: Okay.

2:41 Ann Stolzman: So Ann Pregler is a Tableau Ambassador and a 2026 Iron Viz Champion. She works as a senior data analyst at BECU, where she uses data to improve the experiences of colleagues and members.

2:54 She specializes in building Tableau visualizations that transform complex data and clear insights, and her favorite part of the job is untangling messy data sets and creating practical, intuitive tools that help people understand what's happening and make better decisions. She holds a bachelor degree in mathematics from Seattle University, along with a master's in technology and a master's in data analytics.

3:18 She began her career as a teacher and continues to draw on those skills to create compelling data stories, explain complex ideas clearly, and empower others to use Tableau with confidence.

3:29 Outside of work, Ann enjoys gardening and fiber arts, baking bread, and spending time with her husband, children, cats, and chickens.

3:37 So take it away, Ann.

3:41 Ann Pregler: Okay. This time it is me, Ann, right?

3:42 Ann Stolzman: Yes, it is you.

3:44 Ann Pregler: Okay, perfect.

3:45 Ann Stolzman: And I'll stop sharing my screen so you can take over.

3:47 Ann Pregler: All right. Well, I'm gonna go ahead and share, and hopefully this will work smoothly here. Fingers crossed.

3:54 Ah, it did. Okay, great.

3:56 Um, all righty.

3:57 So I'm excited to be here today to talk about Iron Viz.

4:01 Uh, quick intro, you just saw this, so I, I won't do a lot here. Um, but I used to be a math teacher, now I do data viz. Um, Tableau Ambassador, have some viz of the Days. I blog about data on my site, annepregler.com.

4:13 Um, and importantly, my whole career has pretty much been built through the online data community. My current job, my last job, all came from opportunities that I got by actually initially participating in Iron Viz, and then becoming more involved in the community from there.

4:29 Uh, here is an example of some of my different dashboards that I have on Tableau Public. Uh, I do a wide variety of things from business dashboards to more creative storytelling, um, and have built a decent collection of things over the last couple years on a lot of different topics.

4:47 So why am I here today? Iron Viz 26, uh, in… just happened in May, uh, where I became the Iron Viz Champion for this year. Got a cool trophy. Um, and today I'm gonna talk about that experience. I'm gonna talk about how I built my viz. And hopefully, along the way, share some tips and ideas that you can use to help make your vizzes better no matter what you're building for, um, even if you're not building them in 20 minutes.

5:14 Quick catch up if anybody has missed it. Uh, Iron what? What is Iron Viz? It is the world's biggest data visualization competition, depending on how you define competition. Uh, the way the process works, uh, is you start with qualifiers. Uh, these usually happen in the fall. Anybody at all have usually one month from when the topic is announced to when vizzes are due.

5:35 This year the theme for the qualifiers was food. From there, there's some initial judging, where the top 10 entries are selected. The top three get to go to the finals, uh, at Tableau Conference.

5:47 The finalists get the data set and have one month to plan every single aspect of their build.

5:52 Um, and then finally, at Tableau Conference, the three finalists get just 20 minutes to build the viz live on stage in front of thousands of people.

6:02 So how did I get here? Um, this was the third time that I had entered the Iron Viz qualifiers.

6:08 I entered for the first time in maybe 2023. I don't know. Um, I didn't make the top ten that year, uh, but I learned a lot from the experience. My second entry last year placed fifth, and then this year my qualifying entry, which was about food that could be grown in space on the way to Mars, um, got me a spot in the top three and led to, uh, comp– oh, I forgot that I put more screenshots here.

6:33 Sorry. And led to competing in front of about six thousand people on a giant stage. Uh, so not intimidating at all.

6:43 Good news, I wasn't on my own for this. Uh, I had a Vizier, Grant Quick, who helped me plan, and then during the live stage show, trash talked everybody else very effectively.

6:54 Uh, I had former champions, including Lisa, who I saw in the chat, uh, who gave me, you know, really great advice on the process, um, and mentoring as I went through the planning process. Uh, my work colleagues showed up in force. They had signs and T-shirts. And then, of course, the other finalists, Kevin and Brian, who did just such an incredible job, um, and were just amazing to get to spend time with.

7:19 How is Iron Viz judged? Uh, there's three criteria that apply to both the qualifiers and to the final competition.

7:27 Number one is design. Uh, the viz should be beautiful, accessible, and the design should help add to the story instead of distracting from it.

7:36 Number two is analysis. The viz uncovers new insights about the topic and makes new connections using the data, especially things that aren't immediately obvious.

7:46 Last one, my personal favorite, storytelling. The viz takes the reader on a journey and tells a clear story about the data and through the visuals that have been used.

7:57 Today, I'm gonna talk about all three of these components and how I leveraged them to build my viz.

8:05 Sometimes the judging gets a little tricky, and they've got to resort to rock, paper, scissors, like this picture here the photographer captured that I just really love.

8:16 All right, so Iron Viz this year. Starting, of course, with the data.

8:20 The data source for the competition was Goodreads, which is the world's biggest book review site, and the data was all about books, authors, reviews, aggregated from that source.

8:32 There was some good news and some bad news. Uh, the good news is the data was actually pretty clean. Um, from what I've heard, some years for Iron Viz, that's not the case. But this time, our data was pretty well cleaned up. There were some issues that still had to be addressed, but some things had already been taken care of. It was pretty easy to understand.

8:51 The bad news is there actually wasn't a ton to work with in terms of features. Um, we had author, title, publication date, a title summary, up to ten genres, more about that later, and some information about review counts, uh, like how many of each star rating there were, how many reviews there were in total. But that was kind of it. Um, we didn't really get any data about the reviews themselves or about the users on Goodreads.

9:17 It was pretty limited.

9:19 That meant that my biggest challenge with the data was just trying to figure out what an interesting story would be that one of my competitors probably didn't also pick. I wanted to make sure it wasn't super obvious, but still was interesting and made sense from the data.

9:37 Um, in the end, all three of us came up with different things, which genuinely surprised me. Um, and it was really cool to see how everyone interpreted the same data set very differently.

9:49 Before we go any deeper into those three areas, uh, let's pop out for just one second and go take a quick look at what I'm talking about, um, in case you haven't seen this or if it's been a while and it's not quite as ingrained in your brain as it is in mine.

10:03 This viz is about genre mashups, so books with more than one genre. It starts with a fun little opener, which you also saw in the icebreaker for this talk, where you get to tell your own story, kind of this Mad Libs style, fill in some questions, um, and see a little Venn diagram appear of how the genre is mashed together. Um, oh, I picked two of the same.

10:26 The sports. Here we go. Um, and so you can see, you know, you combine some genres here and, oh, horror, literary fiction and sports actually has a book. That kind of surprises me. If you want to read a sports literary fiction horror, there you go. There's the book for you.

10:41 Then we move on to some more information about genre mashups. We see some of the most common ones, and we see some mashups that have had interesting trends, uh, like paranormal romance, which saw a really big spike, um, in the mid-early 2000s.

10:56 Finally, on the last page, we dig a little bit deeper into the characteristics that make a popular genre mashup, looking for mashups that might be the next big thing.

11:07 Um, so I've got, you know, a little accordion here that lets you expand out, see some different criteria. And as I click through these, you'll notice the network chart over here starts to narrow down possible combinations, uh, until we wind up with only four leftover possibilities.

11:27 So that's the viz.

11:29 The big question is: how was this built?

11:34 Let's dig a little deeper.

11:36 The first thing that I focused on is design.

11:39 Um, I am a very design-focused viz maker. Is there a word for that? There should be.

11:45 Anyway, design matters a lot to me because it's where I get a lot of inspiration. Um, until I can kind of see in my head roughly what the viz is going to look like or feel like, I have a hard time making a lot of progress on it. I wanna know what the vibe is going to be before I get too deep.

12:03 So for Iron Viz, uh, I locked in fairly early on a very journal, uh, bullet journal, um, doodle journal-inspired theme.

12:11 You can see here one of my inspiration images that you can probably tell a lot of these elements, you know, the orange colors, the dot background, some of the handwritten styles, the Post-It notes made it into the final viz.

12:26 I always make one of these mood boards, uh, for any bigger viz that I'm doing. This is part of my mood board, uh, for this viz.

12:34 And again, you can see those elements coming through, right? We've got, like, these sort of sketchy boxes. We've got the highlights, uh, dot paper, some of the colors, the orange and the teal.

12:44 Um, and just again, really that feel. Um, I wanted my viz to feel the same way that looking at these images feels.

12:54 Um, I do this, like I mentioned, for every viz. Just kind of for fun here, this is for that qualifier Packing for Mars entry you saw earlier. Um, this is again, that inspiration board for that same kind of deal.

13:09 So once I had locked in on how I wanted my viz to feel, I had to figure out how to make my dashboard feel the same way. Um, 'cause obviously I couldn't just get out some journal paper and start doodling. That doesn't really work, uh, when I'm trying to make this as an actual Tableau dashboard.

13:26 Um, and dashboarding has more limitations than just straight up designing in Figma or something like that. Um, I had to work with the actual chart types. I had to make sure that my data was legible and wasn't getting hidden by those design pieces.

13:42 I used three main strategies to try to translate that design feel into the actual Tableau viz.

13:51 Background images, custom shapes, and chart choices.

13:56 Background images probably had the biggest single impact of all of these strategies.

14:02 What you can see here is a fairly early version, um, of my background image that I used for the whole dashboard.

14:10 This is created just in Figma, um, and it's actually just a bunch of rectangles with drop shadows on them, kinda tilted over, got a line with a drop shadow in the middle, some dots, and then a little bit of text.

14:22 Um, this was actually probably the first thing that I built for this dashboard, um, before I even really honed in on what the story was gonna look like, I had locked in on this design concept.

14:36 Even though I like to have that design feel first, for Iron Viz, I had to leave some of the details of these background images till last.

14:45 And that was just because I was working very hard on getting my build time down, and I knew that changing the background images wouldn't affect how long it took me to build.

14:55 And so it could wait until everything else was done and planned out.

14:59 Towards the end of the design process, I expanded on these background images, and once the graphs were added, we ended up with what you actually saw there just a second ago. I added all of this doodle art to try to evoke the feel of lots of genres. You can see you've got the romance and the cozy and the sci-fi and the horror and the fantasy and all those pieces.

15:22 I added these little banners that are coming from that, again, sort of bullet journal doodle style.

15:28 I added cute little arrows, little font tips, all of that.

15:32 So the background images went a long way to conveying that journal feel that I was going for, while still letting the data pieces be the star of the show.

15:45 Speaking of the data pieces, even though my backgrounds were giving me the right feel, I had to make sure that the charts didn't oppose that. 'Cause if I just stuck a default Tableau chart in here, it would just all look wrong. I wanted to make sure that the charts still felt like they could have been drawn in somebody's journal.

16:04 So to do that, one of the things that I leveraged was custom shapes.

16:09 Um, and you can see those most dramatically on the intro Venn diagram and then later on the network chart.

16:18 All of the circles here, um, are custom shapes just done in a drawing program with a highlighter brush. The cool thing about custom shapes is they can have transparency, um, and so that's why here these look like they have been colored in by hand with a highlighter, um, when they actually haven't been.

16:35 Some of them I even intentionally put slightly off center, like this pink one for romance, just to kind of help again convey that this could have been somebody just drawing in a notebook.

16:47 Last thing that I used was just chart choice.

16:50 So on this page you can see we have a little bit of background image, just sort of this guy up here, and then that one highlight. Oh, and I guess this, this card that's kind of important. Um, but for the most part, the charts here actually don't have anything fancy. These are just out-of-the-box Tableau charts. Um, they don't have any custom shapes.

17:09 But I use just some little tweaks to make them fit the feel better. Like here, making a lollipop chart instead of just a bar chart, again, felt just a little bit more hand-drawn.

17:19 Making this chart with a variety of line thicknesses, and kind of that light, thin gray line helped it feel a little bit more penciled in.

17:29 I originally had, like, custom shapes for these little circles, and I had to drop it because I just did not have time, um, during my build. Um, and but even these fairly simple charts can still help keep the feel of the overall design.

17:45 The big questions that I was constantly asking myself is, Is the design clean? Does it add, not distract?

17:54 Is it accessible? And can I build it fast?

17:58 Um, and I would say that when you're thinking about design for your work, this last question, can you build it fast, maybe not quite so important. But these first three questions are always critical for taking into consideration as you're building anything.

18:12 It's easy to get carried away by design and to make something that's so fancy that it actually becomes hard to use.

18:19 Um, and so trying to find that balance between something that looks beautiful and something that is enhancing your story, um, can be tricky and takes a lot of thought and patience and iterations.

18:34 Moving on. Step two, story.

18:39 I found that finding a good story was actually the hardest part of this build. Like I talked about earlier, the data set didn't have a lot of different features to work with, and so trying to find something that was interesting and that had enough depth to really dig into took me a while. Um, I cried multiple times. Uh, but I got there. I ended up choosing to explore genre mashups and really loved the, the direction that I ended up in here.

19:06 So what actually makes a good story?

19:08 The first thing, and this is something that I learned throughout multiple qualifier entries, is that it's got to be more than just a list of facts.

19:17 Um, this is something that I've seen as I've gone back and, like, looked at a lot of entries to Iron Viz and kinda tried to figure out, like, what separates some of the beautiful vizes that look so good but didn't make the top 10 or didn't make the top three from the ones that did. And it's not always consistent, and there's lots of reasons. But one trend that I noticed is sometimes you see a viz that shares a lot of information but doesn't really tell a story.

19:43 There isn't a new finding or something surprising that isn't just there at face value.

19:49 And of course, vizes that are sharing information are really important, and we use them a lot. But for Iron Viz specifically, having that story really, really matters.

19:59 I like to think about this story with that classic high school three-paragraph essay structure, that beginning, middle, end.

20:05 It's simple, but it has a nice flow to it. And thinking about that gives you the whole structure of a dashboard all the way through.

20:14 Last thing, for the most interesting stories, I think they have to come to a novel, the book puns aren't done yet, sorry, uh, novel conclusion. They come to some kind of realization from the data that wasn't immediately obvious, that you couldn't just answer right away, and they take you along for the whole journey.

20:32 Um, so for example, for my viz, the conclusion at the end was about what genre mashups might be popular next.

20:40 You really need to come along for that whole data journey to know the answer to that question. You can't just answer it from the title.

20:50 The story flow that I ended up going with, uh, as you saw earlier, we started with a hook, that Mad Libs file intro with the Venn diagram.

20:58 Then an introduction. That was the second page. There was some basics about mashups, and it introduced the big question of what will be popular next.

21:07 Then the core analysis. This was all one page, um, in that little accordion that expanded, but it was actually several pages worth of content. Uh, I just squished it down to one page because Grant, my supervisor, said, "You do not have time to build six pages." And I looked at it and said, "Yes, you're probably right. I will just fit it all into one page instead." Um, I don't think that was what he intended me to do, but it worked out fine in the end.

21:30 Um, anyway, this is where we hit that three-paragraph essay bit. We're giving three reasons or three pieces to the story, um, three different factors in this case that might create a popular mashup.

21:43 And then finally, we end with the conclusion. What is that next big mashup going to be?

21:49 Of course, I also had to figure out how to clearly convey this story in Tableau, and to keep… how to keep it interesting.

21:56 The three big things that I used to make this happen were a three dashboard layout, lots of dynamic zone visibility, and parameter actions. So the three dashboard layout we've already looked at and talked about a little bit. But by using these arrows to navigate between my different sections, and then by using the accordion on the final page to fit in a whole bunch more information in a smaller space, um, I kept that story flow really clear.

22:23 There's a beginning, you move forward, and then by the end, you are actually at the end. The pages have turned.

22:30 Dynamic zone visibility. You can see this happening right here on this accordion. Um, this is one of the biggest examples of this.

22:38 As we're clicking through this, uh, different parts of the visual are becoming apparent.

22:44 Um, and so this is, again, I mean, A, it's fitting more stuff in in a smaller space, but B, it's also making sure that people are flowing through the story in the way that I want them to. They're seeing one of these factors at a time and going through one, two, and then three, um, to really see one piece and come along on that whole journey with me.

23:09 Last one, parameter actions.

23:11 Uh, lots of stuff in this dashboard is being controlled by a couple very simple parameters.

23:18 You can see that here. Um, this entire page has just one, one parameter controlling it. Um, it has four different states, one, two, three, four.

23:29 Um, oh, sorry, I'm making hand signals at my camera and it's, it's recognizing it. It's gonna zoom.

23:34 Ooh, whatever. We'll keep going.

23:36 Um, and those number states are both controlling the accordion, and then they're also controlling what's being filtered on the Venn diagram, and they're also controlling dynamic zones like this one with the top four picks. Like, you can see if I go back to one here, this looks different now. It's not showing the four picks anymore.

23:59 Likewise, the dynamic zones, this is a little more subtle, but as well as controlling the visuals you're seeing here, notice that as I click through, the backgrounds are actually changing too, right? Like, this top 10 pairings frame is part of a background.

24:15 When I click to the next one, that one's gone, and now our background has Post-It notes and little hearts. Um, same thing over here. Some of these background elements are changing as I go through. This is all still being controlled by one parameter and one set of dynamic zones.

24:35 Those parameter options also show up on the first page. Uh, these ones, I guess, are maybe just parameters, not parameter actions.

24:43 Um, but as you are changing these three parameters, the Venn diagram is responding, and then also, again, background elements are responding. Like this banner here isn't showing up originally. When a parameter changes, it appears. So do some of the notes about what you're doing.

25:01 This is all just dynamic zone visibility, in this case, being controlled by these three parameters, um, and a couple of calculations.

25:10 All right.

25:11 Third criteria, analysis.

25:14 I had a design I was happy with, um, I had the story that I wanted to tell, but of course, this isn't any good if the data doesn't actually support that story. If I'm just making it up or just telling a story with words, that's not what we need here. That's not the point of Iron Viz or of data storytelling.

25:35 I'll just note that, of course, in reality, a lot of this analysis happened at the same time as the story. I didn't, like, make up a whole story and then go analyze it. There was a lot of back and forth.

25:46 Um, I'm just putting it after for ease of explanation.

25:51 This analysis involved a lot of Tableau Prep.

25:55 For Iron Viz, you get to do as much prep as you want to in Tableau Prep beforehand, so from the original data source to whatever sources you're gonna use in the dashboard. Um, and to help build faster, it's really important to do a lot of the preparation ahead of time so that as little as possible has to be done in the dashboard itself.

26:17 I used Tableau Prep for a bunch of different stuff. Um, the first one was genre cleanup.

26:22 This was really important, because the original data source actually listed over a thousand different genres.

26:29 Some of those were, like, super niche subgenres, like, uh, ghost romance, man loves woman. Like, very, very specific.

26:39 Some of them were not actually genres. Um, shout-out to the ten books that listed Google as a genre.

26:47 Not a genre. Agatha Christie, also not a genre. Um, and some of them just weren't relevant to what I was looking for, which were fiction genres. So I mapped all of those thousand genres down to seventeen. I did that by hand, and it took a long time.

27:03 Second one was limiting the data. Um, I reduced it to just what was relevant. So in this case, that was fiction books, that was books that had at least a couple ratings and reviews, um, and I got rid of any columns that I wasn't going to need.

27:17 Third thing, calculating metrics. So all of the calculations that actually were part of my analysis were done beforehand in Tableau Prep, um, just to save time on the build.

27:29 And then the last one is separating the sources.

27:31 Uh, so that final dashboard, even though it all comes from one original data source, the final actually uses six different data sources, because each one is optimized for a specific chart, um, or a specific set of charts in some cases.

27:48 Here is a part of my Tableau Prep flow. This is not even all of it. You can imagine about this same length over to the left here before even getting to this section.

27:58 Uh, there was a lot of Tableau Prep involved.

28:02 The final challenge for Iron Viz specifically was to make it fast. Um, I had my design, I had my story, I had my analysis.

28:10 The final question was, how the heck do I build this in twenty minutes?

28:14 Uh, Lisa had given me some great advice to start with building what I wanted, and then figure out how to make it fast later. Uh, I really, really leaned into that. Uh, my first time building it took me about fifty-five minutes, uh, which, not okay. Uh, this has to be built in twenty minutes, so I had a lot of paring down to do.

28:36 To get faster, I focused on three main things.

28:40 First one was optimizing. I wanted to make sure I was maximizing efficiency for my build.

28:46 So that's things like for minor pieces of text, I just put them in the background. Um, I made sure anything critical stayed in the actual text so it would be screen reader accessible. But for stuff that was more just adding flair or flavor, that became part of the background.

29:03 Each data source got set up to be exactly ready for its own chart. Um, so we can go– I have this one up here. We'll see if it shows up for me correctly.

29:12 Oop, there we go. Um, so for example, you can see this line chart here.

29:18 It's using a data source that is built specifically for it.

29:21 And you can actually see every single field here. There are only five fields, and they are all used in this specific chart. Um, so it's very targeted. I don't have to do any filtering or really make any changes, because it is ready to go for this chart. Uh, all calculations were in a text doc ready to be copied in, and then I reused elements wherever possible.

29:47 Um, I don't know why I just made that bigger, 'cause I'm about to go back to Tableau and show you that one again.

29:52 Uh, example of that here, you can see this lollipop chart, um, on the second page gets reused and copied on the third page. This one just has a top ten filter added to it, but it is otherwise the exact same chart. I originally had a different chart here, and had to cut it just for the sake of time.

30:14 Speaking of cutting, I had to cut a lot of stuff.

30:18 Um, there was a lot of content I wanted to include here that I just couldn't.

30:22 Uh, for example, a dedicated conclusion portion, more details about Catalyst books, tooltip formatting, some chart styling. I had to just let a lot of stuff go because 20 minutes is not very long at all.

30:37 Third section or third tactic was to just get faster, um, just by practicing and learning how to do this as quickly as I possibly could.

30:47 One thing here was keyboard shortcuts. Um, I'll show you this just because it's kind of fun.

30:53 Uh, we'll see if I can still remember these correctly. Uh, but for this chart, um, this can be built in only three clicks and the rest keyboard.

31:04 Um, so let's see here.

31:05 So we can do… If we start at the top, we can do Option, or I think it's Alt on a, on a Windows computer, and Shift-O to get color.

31:15 Go down one with the arrow keys. Option-Shift-I to get size.

31:22 Down one more. Option-Shift-C for columns.

31:27 Genre parent, go Option-Shift-S for details.

31:31 And the last percentage goes Option-Shift-Rows.

31:34 So I've not touched the mouse at this point, and then all we have to do is make this one continuous and dial down the size a little bit, and that's it.

31:46 Uh, so I learned a lot of keyboard shortcuts, a lot of ways to build things very, very quickly.

31:52 Uh, I made sure every step was done in the best order for minimum movement. I had the mouse ready in the next spot while something else was loading, uh, all of that kind of efficiency, and also drunk lots of coffee. Uh, this sounds like a joke, but it's not. I discovered I could actually go seven minutes faster if I'd had at least two cups of coffee.

32:11 So there you go. The more you learn.

32:15 All of that work and preparation paid off. Um, I ended up, uh, being named the Iron Viz Champion, which was an amazing moment, along with all of these wonderful people, uh, who also were a part of this journey. A really amazing, amazing experience.

32:31 Iron Viz's slogan is, "Win or learn, you can't lose." And so before I wrap up, I just want to say you should enter Iron Viz, you specifically. Um, I guess not Lisa, but everybody else should enter Iron Viz. Uh, qualifiers for 2027 will open in the fall. They usually get announced around late September, although it varies. It is open to everybody, no matter how much experience you have.

32:55 And winning, while very, very cool, really isn't the point. Um, even if you feel like you won't be competitive, enter anyway.

33:03 It builds connections, it's a great learning experience, and it will help you grow your skills and meet people you might not have met otherwise.

33:10 Um, and it is just very, very, very worthwhile.

33:15 I think that's it. That's my last slide. Um, I have no idea how I did on time there, so hopefully that was all right. Uh, but we'll open it up to questions if we have time.

33:24 Michelle Maraj: Thank you so much, Ann. It's, like, so cool just to see how… It's, like, how people prepare and then also, like, what all goes into these. 'Cause, I mean, even sitting in the audience, like, watch- it's like we watched you do it, and I didn't even realize, like, the shortcuts. I didn't realize what is… I mean, like you said, like, some things are shapes versus some things are backgrounds.

33:43 It's like there's so much going on that you don't really get that level of detail going or, like, even watching in the audience. So super, super cool to see the walkthrough. Thank you.

33:54 And then I did have, it's kind of tied. So Iris in the comments she said, asked if there's a shortcut library.

33:59 I don't know. Ann, Ann P., um, when you were learning the shortcuts, did you, like, look up…

34:05 I guess, how did that work? Did you look up specific shortcuts, or did you have… What resources did you use to, like, find the shortcuts that you would use to make your entry a little bit faster?

34:16 Ann Pregler: There is a reference which I will paste in the chat right now. No, I won't, 'cause I used the wrong keyboard shortcuts to copy. Sorry, I'm switching between a Mac keyboard and a Windows keyboard, and it's, it's not going great. Um, there we go. Now I'll paste it in the chat. That lists all of the keyboard shortcuts. Pretty much what I did is once I had finished planning my build, I then went through that list and checked every one and went, "Hmm, could I use this?

34:38 Could I use this? Could I use this?" Um, and found some that, that are very helpful. Um, they are probably mixed helpfulness. If you're just building dashboards for work, you probably don't need to have all of them, but you can save a little time with a couple big ones.

34:52 Michelle Maraj: And then I don't see any other questions yet. But something else that I was kind of wondering is, 'cause yeah, I was surprised for your shapes, 'cause, okay, thinking about the Venn diagram, 'cause was that, like, are those three different shapes that are just, like, sized differently, or how did that one, how's that one structured?

35:17 Ann Pregler: Yeah, that's a great question. And sorry if you can hear my cat trying to knock down the door and get into my office. Um, he's being very loud.

35:24 Uh, yeah, so let me actually, let me share my screen for this one. That might be easier just to show you.

35:32 Uh, so for the Venn diagram specifically, this was an interesting one, um, because it was custom. The Venn diagram is not a default chart type, of course.

35:42 Um, oh, sorry, I need to actually put some of these up here so we can see it.

35:48 There it is.

35:49 Uh, so this is one custom shape, which is doing the orange circle without a border. Um, it is a dual axis chart, because the top layer is just providing the black circle. Um, and then the shape is, is the circle inside.

36:04 Um, and you can kind of see here, like I could…

36:07 I think this is the right layer. I could just make this, you know, a solid circle. Um, and it, it does the same Venn diagram effect. Um, actually I think that was the wrong one, but whatever.

36:18 Nope, doesn't matter. Uh, does the same effect. Um, but basically the sizing here is all being calculated in Tableau Prep. Um, what- There's, there's a lot of detail to the calculation, but the short version is that it's checking how big the bubble is, and then it's creating a radius away from zero zero, which is right here, and then subtracting a little bit to give it an overlap.

36:41 Um, that's how, like, the positions of these are being calculated, so that when I bring it into Tableau, my X and my Y are ready to go and are all pre-calculated.

36:52 Michelle Maraj: Hm. No, very cool, and thank you for sharing. Yeah, 'cause, I mean…

36:57 I forget where I was going with this, but, um, no, very cool, 'cause, yeah, I haven't created Venn diagrams before, so it's really neat. And I see, so Lisa asked, um, "What do you use to make your mood boards?"

37:07 Ann Pregler: Figma. Uh, you could use anything that lets you copy in pictures and drag them around. Uh, but because I'm usually designing in Figma anyway, I typically put it all on the same page so that then when I'm making backgrounds, I can color pick, um, from the images and grab colors that I wanted, uh, or grab specific elements. Uh, but you could use anything that lets you paste in images.

37:28 It's not too important.

37:30 Michelle Maraj: Mm-hmm.

37:31 Yeah, awesome. And then Jennifer, she says, "What are some other stories you considered, and were any of them used by the other two competitors?"

37:38 Ann Pregler: Yeah, that's a gr- really good question.

37:41 I did not consider any other stories after I'd actually worked with the data set. Like, when I first heard that it was Goodreads before I got the data set, I had a ton of ideas, but they were all based around reviewer characteristics. Like, I was thinking I could do something about what are teenagers reading or, you know, why do people like some books and hate other books.

38:02 And then I got the data set and realized that there wasn't anything in it about reviewers, which kind of makes sense, because it was already several million rows, and if then there had been reviewer data too, that would've been impossibly large. Um, but I was disappointed, because all of my ideas had been about who's reading what books.

38:20 Um, and so this was the only story that I liked enough to follow for any amount of time. And then I had a very frustrating period where it wasn't working for me, like, I wasn't getting the analysis to support what I wanted to tell. Um, at which point I just had a couple mental breakdowns and panicked a lot, um, and then I figured out how to make it work.

38:38 And that was, that was the journey there. There was a lot of panic involved in this process.

38:43 Michelle Maraj: Oh, no. And can-

38:44 Ann Stolzman: How, how much time between knowing what, that it was a Goodreads data set versus getting the data passed?

38:52 Ann Pregler: Uh, about a week. Um, I kind of cheated. I didn't actually cheat, but I got a preview 'cause I read the terms and conditions they sent us, and it mentioned that you're not allowed to use the Goodreads logo, and I was like, "Huh, that's an odd term," and went, " It must be a Goodreads data set." Um, so that was my little, my little preview. But it was in the official materials, and anybody could've found it, but-

39:12 Ann Stolzman: See? It, it pays to read the terms and conditions. Gotta read all the terms and conditions. Yeah.

39:18 Ann Pregler: Um, no, so about a week. Yeah.

39:21 Michelle Maraj: And Ann, are you a reader b- b- pre-Iron Viz or…

39:26 Ann Pregler: Yes. Always.

39:27 Michelle Maraj: Well, 'cause I was gonna say, do you have, like, a favorite mashup that you personally are driven towards?

39:31 Ann Pregler: Oh, it's gotta be a romantasy, doesn't it? I mean, it's just… You know, it's, it's not always high-quality reading, but that's okay. Nothing wrong with that.

39:40 Michelle Maraj: Mm-hmm.

39:41 Ann Stolzman: Brain candy is good.

39:43 Ann Pregler: Exactly. You need it.

39:45 But no, anything combined with murder mystery is also good. Um, animals solving murder mysteries, big, big hit there.

39:52 Michelle Maraj: Hm.

39:53 I know, I was really surprised to see that mashup 'cause it's like, oh, I don't know if I've read any books with, uh, animals solving crimes, but

40:00 Ann Pregler: Well, The Sheep Detectives, which just became a movie. That's a good one. Um, there's one with, uh, these beautiful creatures, an octopus solves crimes.

40:07 Michelle Maraj: Hm.

40:08 Ann Pregler: Uh, there's a lot of books with cats solving crimes. If you like cats, that's a wide, wide genre.

40:14 Lots of cats.

40:15 Michelle Maraj: Oh, no. I'm a cat person, so.

40:16 Ann Pregler: Well, good. Yeah.

40:16 Ann Stolzman: Don't forget Remarkably Bright Creatures.

40:19 Ann Pregler: Hm? Yeah.

40:20 Michelle Maraj: That's-

40:20 Ann Pregler: Throw that on the book club.

40:21 Ann Stolzman: That's a little bit, there's a little bit of a mystery tinge in there with the octopus, so.

40:25 Ann Pregler: Yeah, that one's considered animal mystery.

40:27 Michelle Maraj: Mm-hmm.

40:28 I think I'm gonna be using your dashboard for recommending books at my book clubs now.

40:33 Ann Pregler: Yeah, it's a good, it's a good option.

40:35 Um, and yeah, you can find some really crazy things. People mash up all kinds of stuff with sports.

40:41 I wasn't expecting it, but they do.

40:43 Michelle Maraj: Okay.

40:44 All right. Well, if there's… Looks like there's no other questions, so thank you so much, Ann, again for presenting and joining us today. Really, really appreciate it.

40:52 Ann Stolzman: So we're gonna talk resources. I'm gonna throw a bunch of links in the chat right now. So we have, our other Tableau resources are in there, which is the Tableau Learning Hub, which is the last link that I have posted in there. Plus there's the Slack community that, uh, is out there for folks, uh, to join and, uh, talk with other Slack users, including a lot of community projects that are out there, like Workout Wednesday, Makeover Monday, Back to viz Basics.

41:21 And then we have our own resource, uh, library that we've put together here, uh, for the Data+ Women West Coast TUG.

41:28 Uh, you can view the library and submit items. We just ask if you have new items to submit, just check the list before you submit them. So the link to the form and the doc, uh, both Google, uh, setups are in the chat.

41:47 And do we have any open discussion for today?

41:52 Michelle Maraj: Looks like we're just all in awe.

41:55 Ann Stolzman: We are all in awe. It was great discussion today.

41:59 Michelle Maraj: It was pretty cool.

42:01 Ann Stolzman: I have to agree. I have to agree. So moving on then, um, does anybody have any other questions about, to us, about conference, resources,

42:16 our favorite drinks?

42:18 Michelle Maraj: Things you wanna see volunteers to present, you know.

42:23 Ann Stolzman: We take volunteers too.

42:26 Michelle Maraj: Okay, yeah, 'cause quick pitch. Um, if you are interested in presenting at our TUG, you can reach out to any one of us. Um-

42:34 Ann Stolzman: Yeah

42:34 Michelle Maraj: … and we are happy to have you and set you up with that.

42:38 Ann Stolzman: Yeah, absolutely. And we are coming up on-

42:41 Michelle Maraj: Or if you know anybody

42:41 Ann Stolzman: … or if you know anybody, absolutely.

42:43 And we are coming up on one year of this TUG, amazingly enough.

42:50 Michelle Maraj: Yay.

42:53 Ann Stolzman: So thank you all who have been attending these meetings.

42:56 Um,

42:59 now let's get to the fun. Again, if you have not already done so, please create your Visibly live badge. I know that Michelle put the link further up in the chat, and I'm gonna go find that and drop that back in.

43:15 Michelle Maraj: I just copied it back in-

43:17 Ann Stolzman: Oh

43:17 Michelle Maraj: … so.

43:17 Ann Stolzman: Thank you, ma'am.

43:18 Michelle Maraj: Yeah.

43:19 Ann Stolzman: So go, uh, do that, and we'll throw you up on the wall.

43:25 And next time we are gonna have Celia Fryer. Celia, um, will be in our October meeting. Uh, she… We are still nailing down her topic because she has a lot of different irons in the fire and things that she presents on.

43:43 Uh, she hosts the San Francisco TUG, TUG and an education TUG, if I'm not mistaken.

43:51 Um, and she is a great speaker and a great person, so if you're not already connected with her, you should be.

43:58 And I think that's a wrap for today. So, um, thank you all for joining. If you need to get in contact with any of us, our emails are right there on the screen, so take a quick screenshot if you need it. You can also find all of us on LinkedIn, uh, under our various names, and we will be posting, um, our badges. I actually already posted mine at the top of this meeting, so you should be able to see it, uh, on LinkedIn.

44:26 Um, and I think that's everything.

44:30 Michelle Maraj: Thank you all for joining, and we'll see you next time.

44:33 Ann Stolzman: Okay.

44:33 Michelle Maraj: Thank you.

44:34 Ann Stolzman: Take care, everybody.

44:35 Michelle Maraj: Can we get that Visibly, see what the end result is?

44:38 Ann Stolzman: Our end result?

44:39 Michelle Maraj: Yeah.

44:40 Ann Stolzman: Oh, yeah. Let's see that.

44:41 Michelle Maraj: Throw it up.

44:42 Ann Stolzman: Oh, we already have some people leaving, but I wanna see.

44:47 Michelle Maraj: If anybody was-

44:47 Ann Stolzman: Thanks for the recording

44:49 Michelle Maraj: … you wanted to see if ghosts are still in there, right?

44:53 Ann Stolzman: Well, we still have some ghosts.

44:58 Okay.

44:58 Michelle Maraj: Two fairy princesses and-

45:00 Ann Stolzman: A couple of spies

45:01 Michelle Maraj: …

45:02 taking a picture. Yeah.

45:05 So it's just kind of cool to see what everybody came up with. Yeah, a lot of people watched it live, which is pretty cool.

45:10 Ann Stolzman: Yeah.

45:11 Michelle Maraj: Yeah.

45:12 Ann Stolzman: I love that. Okay. I'm gonna stop the recording.