109: How to Become a Data Visualization Designer & a Creative Data Analyst w/ Alli Torban

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[00:00:29] My guest today is Allie Torbin.

[00:00:32] She's one of the leading data information designers in the world.

[00:00:35] She's taught DataViz to companies like Google, Moderna and created data visualizations for premier publications like The Washingtonian and Axios.

[00:00:44] But she didn't always start that way.

[00:00:46] You see, Allie started as just a normal data analyst for a company in Washington DC.

[00:00:50] I was running SQL queries.

[00:00:51] I was testing software and there was nothing wrong with it on paper,

[00:00:56] but it just felt like there was something more fulfilling for me somewhere.

[00:00:59] And that's when I started expanding into the world of data visualizations.

[00:01:03] And Allie was smart, like many of you listening, but didn't have all that much experience.

[00:01:08] And she was also looking for a flexible job as she was a new mom of two little ones.

[00:01:13] So she knew she had to network and that networking kind of came in a strange medium.

[00:01:19] I thought, well, maybe I can start my own podcast and I can learn about data visualization,

[00:01:25] make a portfolio, start applying to jobs, and it totally worked.

[00:01:29] So in this episode, you're going to hear Allie's full story as well as her advice on how to become a data Viz designer

[00:01:36] and how it can work for you too.

[00:01:38] Stay tuned.

[00:01:49] Allie, thank you so much for joining us on the data career podcast.

[00:01:52] We're so happy to have you.

[00:01:54] Oh, thank you so much, Avery.

[00:01:55] Happy to be here.

[00:01:56] Yes.

[00:01:57] And I'm so excited to be talking about something that I don't think gets enough credit because data visualization is so fascinating

[00:02:04] and so big in the data world.

[00:02:07] And a lot of people just think that especially when you're just getting started,

[00:02:10] which is totally fine to think that like, oh yeah, data Viz equals I make a chart in Excel or I make a dashboard in Tableau or Power BI.

[00:02:17] And I'm excited to just kind of take some of your insight on like, there's actually a lot more than that, right?

[00:02:24] And the other thing I'm excited to talk to you about one of the things that you guys that you talk about in your book,

[00:02:28] ChartSpark here is like, there's room for creativity in the data world, right?

[00:02:32] Is that true that there is?

[00:02:33] Yes, there is.

[00:02:34] Yes.

[00:02:35] We think that to be creative, that's an artist, right?

[00:02:38] That's always what I thought.

[00:02:39] Oh, I'm not are you creative?

[00:02:41] No, I'm not an artist.

[00:02:44] That's not really the definition of creativity.

[00:02:46] The way that I define it, it's the ability to generate new ideas or remix existing ideas and make sure that it's solving a problem.

[00:02:55] And you're solving an ethical problem.

[00:02:57] We don't need any more unethical problem solving, but mostly it's solving a problem and you don't have to be a genius either.

[00:03:04] You know, we're not just thinking, oh, I'm not creative because I'm not Einstein.

[00:03:08] You can do little creativity acts that build on each other like making a bar chart in Tableau.

[00:03:14] You have never created anything in Tableau before and you learned it.

[00:03:19] That's a creative act.

[00:03:20] And then you build on that, like maybe you create the bar chart and connect it to a map and now they filter together.

[00:03:26] That's what researchers Kaufman and Beguetto call little C creative acts.

[00:03:31] So learning something new.

[00:03:32] That's a mini C creative act.

[00:03:34] Little C creative acts is like experimenting.

[00:03:37] And then pro C creative acts is when you are bringing those creative acts together and for a professional purpose, you know, you're solving problems at work.

[00:03:47] It's useful to other people.

[00:03:49] And then you have the big C creative acts.

[00:03:52] And that's like long lasting impact on the world.

[00:03:55] Einstein, but that's what we have in our heads.

[00:03:58] But that is not what we should be aiming for.

[00:04:00] We should be thinking about mini C, little C pro C and bringing our creativity through our data visualization or just regular data analysis work and creating things that make us feel more fulfilled and make an impact on the world and make us stand out.

[00:04:15] So yes, you can be creative in the data field.

[00:04:18] I love that because it's often like you said oftentimes you kind of think I either am creative or I'm not creative.

[00:04:25] And one of the things that you kind of structured your book around was no, like actually there's certain things that you can kind of do to warm up your creativity and get started.

[00:04:34] So I love that in your book, but I want to kind of start with the beginning of your career where basically you were doing sequel queries.

[00:04:40] And I don't know.

[00:04:41] I wrote this down on my notes that you got bored, but you basically didn't feel 100% fulfilled doing these sequel queries.

[00:04:47] Is that fair to say?

[00:04:48] It is.

[00:04:49] Yes.

[00:04:49] And you know, it's one of those things where you feel like you are where you're supposed to be and where you have worked up through college and right after.

[00:05:00] Like you feel like you are technically on paper where you're supposed to be, but there's just something missing.

[00:05:05] So yeah, I was a business systems analyst.

[00:05:07] I was writing sequel queries.

[00:05:08] I was testing software and there was nothing wrong with it on paper, but it just felt like there was something more fulfilling for me somewhere.

[00:05:16] And that's when I started expanding into the world of data visualization.

[00:05:20] So I got a graduate certificate in geospatial intelligence because I thought, okay, I'll just add mapping to this current career and I'll be an Intel analyst and that will be like scratch whatever itch I have.

[00:05:31] But then I started applying for jobs for that.

[00:05:34] But then I found out that I was pregnant.

[00:05:36] And then, you know, here in the U.S., the whole like FMLA rules are you have to be at a particular job for at least a year for in order to get any kind of coverage.

[00:05:47] I mean, well, the company doesn't have to give you coverage.

[00:05:49] So you're kind of like relying on them.

[00:05:51] Like, okay, this is not a good time for me to change careers.

[00:05:54] So I stayed at that job and I was working part-time and then I had my second daughter and then I stopped working completely.

[00:06:00] I was like, okay, this is a great time for me to figure out what specifically was missing with direction I can go in my career.

[00:06:08] And I was listening to a lot of podcasts, like Googling, like work from home part-time data, you know, trying to find what jobs are out there.

[00:06:16] And as you know that there's just a lot of words, word titles that a data job could be hiding under.

[00:06:22] So I was really desperate to find something that I could just do at home that would be fulfilling for me.

[00:06:29] And I found data journalism.

[00:06:30] So you could be a freelance data journalist.

[00:06:33] I thought, okay, that's perfect.

[00:06:34] So I wrote a couple articles for a local magazines and it was using some data analysis like,

[00:06:41] I don't know if you've ever heard of isochrones, like an isochrone map where it's just kind of showing how far you can go somewhere in any direction,

[00:06:49] like within 10 minutes or within a mile or something.

[00:06:51] So I thought, well, what, how far or how expensive, how much more expensive is it to live within walking distance of a metro?

[00:07:00] So I did the data analysis and I did some maps and then the magazine ran the article, but they didn't include my maps because they were awful.

[00:07:08] But you know, like I was doing that kind of thing, but I realized, oh, I actually like the data visualization part.

[00:07:13] So I was, I started listening to a lot of podcasts about DataViz and I thought, well, maybe I can start my own podcast.

[00:07:20] And I can learn about data visualization, make a portfolio, start applying to jobs.

[00:07:26] And it totally worked.

[00:07:28] I mean, I know you talk a lot about networking in order to help you find a job and me starting the podcast was like networking on steroids

[00:07:35] because I am getting to go to people who have the job that I want and being like, how did you get there?

[00:07:41] Tell me the secrets of how you got there, how you make amazing work.

[00:07:46] And then I would take something specific that I learned from them in that episode and I would make my own visualization based on that.

[00:07:53] So within nine months, I had a portfolio that I could start applying to jobs with and I got a job very quickly,

[00:08:00] even though I needed to be part-time.

[00:08:01] So that takes, you know, all the job opportunities and brings it down to like decimates it.

[00:08:07] But I was able to network my way and work hard to create a portfolio that got me hired as an information designer.

[00:08:14] That's so cool.

[00:08:16] Yeah, I don't know if you've met Ken G before.

[00:08:18] He has a podcast called Ken's Nearest Neighbors where he does, you know, data science basically interviews and stuff like that.

[00:08:24] And one of the things that he's talked about before is he's like, I think everyone should have a podcast because it's just like,

[00:08:29] it's honestly like the most beneficial for the host just because the networking is crazy that comes from it.

[00:08:35] And the learning, you're just getting to sit down with people like yourself who are like field experts

[00:08:40] and basically just suck all the goodness out of their brain and put it into your own.

[00:08:45] So that's so interesting.

[00:08:46] So when you ended that job, I guess I should also mention that now you run your own like information design consultancy company.

[00:08:53] How much later did you start that company?

[00:08:55] You start doing like freelancing stuff.

[00:08:57] I was dabbling in freelance pretty quickly after I got that job just occasionally here and there as people would approach me.

[00:09:05] But I jumped into full time freelance maybe three years after I was working there because I was able to acquire more skills while I was working at that job

[00:09:17] because it's really hard to believe that you can do it until someone gives you that title.

[00:09:24] I just remember looking at my business card and it saying data visualization designer.

[00:09:29] And I was like, I can't believe someone put my put that on a card that has my name on it.

[00:09:34] Like that's so amazing.

[00:09:36] So just really just having that business card gave me the confidence to be like, yes, I am a data visualization designer.

[00:09:42] I can do this.

[00:09:44] And so that really helped me learn more tools, learn more skills.

[00:09:48] Like how do you manage a project?

[00:09:51] What questions do you ask people at the beginning of a project so that you can make sure that it's successful?

[00:09:57] And that helped me open up my consultancy and be like, yes, I can do this on my own.

[00:10:01] That's amazing.

[00:10:02] And congratulations.

[00:10:04] It's just really so cool that basically starting a podcast, which is basically in all honesty,

[00:10:09] like kind of an uncomfortable way of networking.

[00:10:11] I mean, it's hard.

[00:10:12] You have to ask people.

[00:10:13] And I think I read in your book that one of your first guests was Naughty a Bremer.

[00:10:16] Is that right?

[00:10:17] Yeah.

[00:10:18] And most people probably don't know who that is, but they're basically they make such cool visualizations

[00:10:23] and amazing.

[00:10:24] Yeah.

[00:10:24] And in my opinion would be not super easy to reach out to.

[00:10:28] So that's really cool that you did that.

[00:10:29] And I feel like it made a huge difference for you.

[00:10:32] But Avery, I have to make the point that up until episode maybe 45, I was not talking live to anybody.

[00:10:40] I was way too scared to do that because I thought I don't know what questions to ask.

[00:10:45] Am I going to sound like a babbling idiot?

[00:10:47] Like, but what I did was I sent them a questionnaire with all these questions about their visualization

[00:10:53] and then I would read through their answers and kind of like make a script for myself

[00:10:57] that walked through it in more of a narrative format and that episodes are about 10, 15 minutes long.

[00:11:02] So I was too scared to even interview people.

[00:11:05] And so anybody listening to this thinking, well, I only think of podcasts in one way.

[00:11:10] I only think that their interview podcast, you can get around that.

[00:11:14] Just think about your strengths and your weaknesses and lean into it.

[00:11:17] Have that part of your premise.

[00:11:19] You know, like I don't interview people live.

[00:11:21] I summarize everything so that it's nice and quick for you to pull out little nuggets.

[00:11:25] You know, I made that as part of the premise because I was too scared to interview anyone live.

[00:11:30] So it doesn't have to be the typical interview pop podcast.

[00:11:33] So even if you're scared, you can make it work.

[00:11:35] I love that.

[00:11:37] I feel like there's nothing I've really done in my career where I haven't been scared.

[00:11:40] You just got to do scared stuff in your career over and over.

[00:11:42] Yeah.

[00:11:42] Yeah, you're not going to you're not going to improve if you're not a little bit scared.

[00:11:46] I agree.

[00:11:47] Okay.

[00:11:47] What is a data visualization designer anyways?

[00:11:50] Like what for someone who's maybe never heard of that title?

[00:11:53] Once again, we talked about how titles are kind of bogus in the world.

[00:11:55] But what is this bogus title?

[00:11:57] What does it actually equivulate to?

[00:11:59] Yeah, I think the listener of your podcast is going to be familiar with data analysis.

[00:12:04] So you have just a data set and then you are exploring it and maybe you're making charts for exploratory analysis.

[00:12:12] You know, what was the, how is this data distributed?

[00:12:15] What are the max?

[00:12:15] What are the men?

[00:12:16] That kind of thing.

[00:12:17] But for me specifically, as a data visualization designer, I focus more on the explanatory data visualizations.

[00:12:25] So how can I polish this and choose a particular chart type where it's going to be explanatory for the final thing, the goal of the chart?

[00:12:35] What's the thing that I want to communicate?

[00:12:37] The one thing that I found during data analysis, what is the best chart type that I can create in order to convey that?

[00:12:45] Maybe it's a data story where I'm sequencing through a few different charts.

[00:12:49] So for me, that's typically what my role is.

[00:12:54] I come in at the end, someone says, okay, we've done this analysis.

[00:12:57] We want to communicate this to this audience and then I help them figure out how to visually display that.

[00:13:02] So it could be with a chart.

[00:13:04] It often is with a chart, but it could be with more an infographic type thing.

[00:13:08] It could be with a comic.

[00:13:09] I've done data comics explaining the difference between percent change and percentage points.

[00:13:15] So it could be a comic kind of a thing, a diagram, an infographic, a database, maybe a mix of all of those, maybe just an illustration.

[00:13:24] So for me, that's why I call myself an information designer more than a data visualization designer because I started out as a database designer,

[00:13:33] but I've kind of expanded out to more just other ways of displaying information.

[00:13:37] Oh, I see.

[00:13:38] So yeah, technically, and we'll pop up if you're watching on YouTube or Spotify, we'll pop up one of your pie chart cartoons.

[00:13:44] I guess that's the key point.

[00:13:45] The information designer versus data viz designer would be like a database designer wouldn't necessarily make make cartoons necessarily.

[00:13:53] Probably not.

[00:13:54] No.

[00:13:55] Okay.

[00:13:55] That makes sense.

[00:13:56] But you make cartoons so you're like, you're like a more playful.

[00:13:59] Yeah.

[00:14:00] Yeah.

[00:14:00] Yeah.

[00:14:00] Yeah.

[00:14:00] Just going outside the lines, I guess.

[00:14:02] Yes.

[00:14:03] But by the way, we'll talk about that here in a bit about why that's a good thing.

[00:14:07] I did want to just kind of give my stab at it as well.

[00:14:11] So like when I think about like a database designer or information designer and you kind of talked about how you're like giving off like almost like the final, you know, information.

[00:14:20] It's like a lot of the information you're giving is like almost like a snapshot.

[00:14:23] It's not like it's not like a dashboard that's continuously updated.

[00:14:27] It's like this is the story in this time.

[00:14:29] Like this is what it looks like.

[00:14:31] And it's also being shown to a lot of audiences and maybe like a lot of broad audiences.

[00:14:36] Like for instance, I know you've had a lot of your work published in big publishing companies.

[00:14:41] It's like this is going to be seen by, you know, potentially tens of thousands hundreds of thousands of people to make the story known.

[00:14:47] That's kind of like one of the things that you do in your position.

[00:14:51] Yeah, I would say so.

[00:14:51] And I have designed dashboards before and it is more of okay.

[00:14:57] We want to be able to show this to our analysts or to our CEO or something.

[00:15:02] And then I help them figure out what chart types are the best and where to put them and then they further test it.

[00:15:07] But you're right that it's more what I do is more of the snapshot type visualization.

[00:15:12] But someone who builds those dashboards might call themselves a data visualization designer too.

[00:15:16] They might lean more into maybe data viz engineer, depending on how many, you know, calculated fields and custom queries that they're creating.

[00:15:25] They might push more into the engine data viz engineer arena.

[00:15:29] But someone doing dashboards might call themselves a database designer too easily.

[00:15:33] Okay, that's fair.

[00:15:34] I think that's fair.

[00:15:35] So there's kind of a gray area there.

[00:15:36] Yeah.

[00:15:37] Oh, for sure.

[00:15:38] And one of the things I think that makes you guys unique compared to other data viz positions in my opinion is some of the tools that you guys use.

[00:15:45] So would you kind of maybe mention some of the tools that you use on a day to day basis?

[00:15:49] Yes.

[00:15:49] So my main stack now is Tableau because I started out learning Tableau because when I was looking for a database job, I was just looking at what are the tools that most people are listing and I saw Tableau a lot.

[00:16:03] So I learned how to do Tableau.

[00:16:05] Not that it's particularly better than Power BI or anything like that.

[00:16:08] That's just the one that I learned.

[00:16:10] And what I like about Tableau is that I can export my visualization as a PDF and then I can bring it into Adobe Illustrator or Figma.

[00:16:19] You have to do, you have to bring it into Illustrator first and then bring it into Figma because you can't bring a PDF into Figma.

[00:16:25] But the benefit of this is that I can have a lot more control over every single one of the elements in the chart.

[00:16:31] So you have a bar chart.

[00:16:32] I can make sure that the axes are darker or lighter.

[00:16:36] You know, it takes a thousand clicks to do that in Tableau, but I can do it more easily in Figma.

[00:16:41] I can change the colors more easily.

[00:16:43] I can bring in other elements like texture or illustrations into the chart.

[00:16:47] So Tableau, Figma, Excel, you know, because everybody uses Excel.

[00:16:53] And then I have an app on my iPad called Procreate, which is a drawing app.

[00:16:57] What artists use it, but I use it for sketches when I am presenting ideas to a client so that the sketches are a little bit more refined than, you know, hand drawing on a piece of paper.

[00:17:07] Sometimes that can look a little too sketchy.

[00:17:10] So it's like slightly nice.

[00:17:11] It's like one step above.

[00:17:12] And also when I'm doing the comics or illustrations, I can do it in Procreate app and then bring it into Figma and then arrange the elements better.

[00:17:21] And then another tool I use is rawgraphs.io.

[00:17:25] It's a web based free web based tool and it helps you makes creating the more custom charts a little bit easier.

[00:17:34] So something like a Sankey diagram, you know, it's really hard to do that in Tableau.

[00:17:38] You have to have a bunch of calculations, but in all that.

[00:17:41] So there is it has a lot more of the bespoke visualizations that you can easily create and then export it as an SVG and bring it into Figma and, you know, style it as you want.

[00:17:51] So those are my main tools I use.

[00:17:54] See, I think that's so unique because using, I know it's probably standard for you, but using Adobe Illustrator for data viz seems crazy.

[00:18:01] But that's kind of the customization and the cleanliness that you guys as information designers are able to do.

[00:18:09] Let's say let's say that someone listening to this podcast is like, wow, I'm creative.

[00:18:13] I want to get into this.

[00:18:15] Like this sounds really fun to mix data with art.

[00:18:17] I'm in let's say that they're maybe a teacher or like an Uber driver.

[00:18:22] Like how realistic is it for them to kind of lands one of these data viz designer or information designer roles?

[00:18:28] What would they need to do?

[00:18:29] Yeah. Okay.

[00:18:30] So what do you need to do?

[00:18:31] So I think first assess the skills that you have.

[00:18:35] So you do need some basic data data skills and people are like, well, what what skills do I need?

[00:18:41] You know, so I would say taking some courses if you don't have any kind of data background, making sure some data literacy fundamentals type courses so that you know the difference between mean, median and mode and how to do basic data cleaning because you need to make sure that the data that people are giving you

[00:19:01] is accurately representing what you're supposed to be visualizing because there's been a lot of times where someone's like, oh, we want the data.

[00:19:07] We want you to visualize this particular thing.

[00:19:10] And then I look at the data like, well, your data doesn't really show that or I'm noticing a lot of nulls in here.

[00:19:16] They're like, oh no, just put zero.

[00:19:18] Like, well, does that actually mean zero?

[00:19:20] You know, you need to know you need to know those things so you don't step in a bunch of data pitfalls and then they run into trouble later because you created something for them that wasn't actually accurate.

[00:19:30] So make sure you have some data literacy fundamentals under your belt and then focus on a few tools.

[00:19:36] So if you do want to do more illustration type things in your more information design than just charts, do learn some basic tools.

[00:19:46] Even now I would say Tableau and Figma would be your best friends right now and learning some basic skills on that.

[00:19:54] Watch some YouTube tutorials.

[00:19:56] Then do what you recommended on my podcast, which is find something that you're passionate about, a problem that you are running up against in your daily life and create a project around that.

[00:20:07] And make it look really nice in your portfolio because an amazing thing about this field is that if you develop a portfolio and you can speak to it in an interview, like this was my question, this was my problem.

[00:20:19] This is how I got the data.

[00:20:20] These are the different ways I visualized it and why I tweaked it here, why I tweaked it there.

[00:20:25] And this is where I landed.

[00:20:26] And this is the problem it solved.

[00:20:28] And it saved me an hour a week because of this visualization or something like that.

[00:20:33] Like talk about how it actually changed your decision making.

[00:20:37] That's an amazing portfolio piece.

[00:20:39] Like nobody has that.

[00:20:41] It's like everyone's using the Titanic data set and then just running it through Tableau and then exporting it.

[00:20:47] You know, don't do that.

[00:20:48] Go the extra step to polish it.

[00:20:50] What else could you add to it?

[00:20:52] So yeah, so I would say the data literacy fundamentals, learn a couple tools and then just start creating because there's you're just going to be disappointed by your work.

[00:21:00] That's just part of the journey.

[00:21:02] I looked back at some of the garbage that I created and it's like, well, that's just part of the journey.

[00:21:07] The more you create, the more you learn and then it'll get better.

[00:21:11] And your portfolio doesn't have to knock their socks off, you know, like Pulitzer Prize, Washington Post, New York Times Worthy.

[00:21:19] You know, it's less than you probably think that you need such a high end portfolio.

[00:21:26] I think the bar is a little bit lower than you expect.

[00:21:28] And then from there, improve your skills.

[00:21:31] I love that.

[00:21:32] And I agree that the bar for portfolios I think is quite low just because I don't think a lot of people have one.

[00:21:38] So even having one.

[00:21:39] Yes.

[00:21:40] And being able to speak to it too.

[00:21:42] People can't speak to it and people want to know your process.

[00:21:45] And they want to know that you are asking good questions and that every time we have a new project internally on our team, you know what you're going to be doing.

[00:21:54] You have a defined process that you're not just going to be willing to leave creating stuff in Tableau and just spitting it out.

[00:22:01] No, I have a process and I know how to think about things.

[00:22:05] I know what questions to ask.

[00:22:06] I know how to clean the data.

[00:22:08] That's being able to talk to it is super important.

[00:22:11] I love that.

[00:22:12] I did want to talk about one of the things that you talked about in your book.

[00:22:14] So once again, your books right here, ChartSpark, everyone should go check it out.

[00:22:17] We'll have a link to it in the description down below.

[00:22:19] But in this book, you give a lot of prompts to be more creative as a data professional.

[00:22:23] And one of the ones that I think you kind of just mentioned as well is like, you know, making good charts.

[00:22:28] How do you make good charts?

[00:22:29] You're not necessarily like just like born with good chart ideas.

[00:22:32] They don't just like pop into your brain type of a thing.

[00:22:35] One of the things that you talk about is if you want to make a good chart, you should be inspired.

[00:22:38] You should have what you call practical inspiration and you give this prompt to kind of help you come up with good ideas for creating charts.

[00:22:46] It's called x-ray.

[00:22:47] And I was hoping you could just kind of explain that to our listeners.

[00:22:50] Yes.

[00:22:51] Yes.

[00:22:51] So you shouldn't sit down on your project and be like, where's the inspiration?

[00:22:56] I'm waiting for my bolt of lightning.

[00:22:58] No, you have to be collecting it all the time.

[00:23:01] So what I do is whenever I see a visualization where I'm like, oh, that's catching my attention or that's particularly clever.

[00:23:10] What I will do is x-ray it.

[00:23:12] So x-ray is an acronym.

[00:23:14] X stands for excited.

[00:23:15] What initially got me excited about this visualization?

[00:23:18] So it could have been like, oh, this has a nice color palette or the annotations are really nice.

[00:23:23] Something like that.

[00:23:24] No wrong answers here.

[00:23:26] And then R is rules.

[00:23:28] How is it following the rules?

[00:23:30] Data visualization best practices.

[00:23:32] So this could be like the axes are really clearly labeled or you could tell the circles are really proportional.

[00:23:39] You know, everything was in the right order, things like that.

[00:23:43] And then A is anarchy.

[00:23:45] How did the person break the rules?

[00:23:47] So those are the things they're like, oh, maybe they used gratuitous 3D.

[00:23:51] There wasn't actually a third dimension, but they used 3D here.

[00:23:54] And that really changed the visual in some kind of a way.

[00:23:59] And you don't have to agree with how they implemented anarchy.

[00:24:04] What's really important is taking note of it and just thinking about what effect did this anarchy have?

[00:24:10] Because it might be something that you can use later in your work.

[00:24:14] And that leads to you, the why, the you, which is how could you use any of the things that you listed in your work?

[00:24:21] And the key is to be really specific.

[00:24:23] Like next time I have to show the proportion changing over time, I might use XYZ technique that I saw in this visualization.

[00:24:31] And just going through this practice of being really specific about what you're seeing, it's going to make you a better designer.

[00:24:38] But then later when you are creating something and you're like, oh, I remember there was something I saw about the proportion.

[00:24:45] And I want to try something like that again.

[00:24:47] You've already done this, this exercise.

[00:24:50] And what I like to do is just creep a spreadsheet of it.

[00:24:52] You could write it down on a piece of paper if you want like a journal, but you can go back and remember what exactly they did that was so special that you can use in your work.

[00:25:01] And that's creative.

[00:25:02] Yeah, I love that.

[00:25:04] I don't know if data is people use this term of like a swipe file.

[00:25:09] Oh, I don't think I've heard it from Austin Cleon's book.

[00:25:14] Yeah, the swipe file.

[00:25:16] Yeah, but the idea is basically whenever you see something is to put stash it away and I like yours do a small little exercise on it to write down what you liked and, you know, keep all the links and stuff like that.

[00:25:26] But I was actually going to go to the Austin Cleon book, which is called Steel like an artist.

[00:25:30] And I think you quote it in your book.

[00:25:32] And one of the things that he quotes in his book is Picasso and Picasso says all art is theft.

[00:25:39] And I think it's really true.

[00:25:41] You never want to plagiarize, but really getting inspired from other people with data visualization, I think is always a good idea.

[00:25:47] Yeah, and I actually mentioned that in the book about not crossing the plagiarism line because being inspired by people, you know, I'm inspired by people they're inspired by me like it's just a natural give and take.

[00:25:57] I like to think about a visualization as the data, the chart type and the design like the art direction in it. So if you are copying all three aspects which I have seen people do before it's like, that's going to look like the exact same thing it's the same chart type

[00:26:12] it's the same data is the same styling like of course like your plagiarizing here, but you know, nobody owns a chart type.

[00:26:18] So it's that's when that's why it turns gray for a lot of people the plagiarism but that's why I like to think about it in these, these three different sections the chart type, the data that you're using in the styling.

[00:26:31] So I if you're going to be inspired make sure you're grabbing inspiration from three different areas and then you put those three things together and then it doesn't look like you aren't plagiarizing but don't grab all three from the same chart.

[00:26:43] That's a good rule of thumb to at least switch up one of those things.

[00:26:47] The other thing I wanted to ask you about in your book that you mentioned, once again, you have like all these different prompts for just being creatively and thinking about how to communicate effectively.

[00:26:56] And one of them is kind of this CTR prompts, and you tell a story about how when you are data journalist, you're just a mom returning to the workforce, and you're pitching these stories to different, you know, magazines and publications, and you pitched this one and you were really excited about it.

[00:27:12] And they were going to pay you not millions of dollars but they're going to pay you. You're excited about getting paid returning to the workforce and they it was this story about how you were going to see what this city particularly Googled versus another city and how it differs I guess from from norm.

[00:27:27] And then they eventually turned that story down, and it kind of led to the creation of the CTR prompts can you kind of explain what that is.

[00:27:35] Yeah, so the point of that story was that I pitched a story and they accepted it. And then she called back and said that her editor said it just wasn't it, you know, what's the point of seeing Oh this city Googles this more like who really cares.

[00:27:54] And I felt so bad because I knew that the story was missing something but I didn't know how to make it better. So I could have been like, Oh, okay let me tweak it like this so that it is interesting to people but I just didn't know how to do that.

[00:28:10] And I think that a lot of times we are in that position where you know you have to create a chart with some sort of data set someone's like oh find a story in it and you're just like, Okay.

[00:28:22] What am I supposed to be doing you just feel terrified and frozen. So I developed the CTR prompt by studying other editors, and just news outlets where they are constantly writing for people and trying to grab their attention and conveying information to them in a way that they

[00:28:41] can't do. So I developed the CTR method which is conflict timeliness and resolution. So you start with some sort of observation that you've seen in your data set, maybe with your exploratory analysis you found like oh there's some sort of outlier here.

[00:28:56] And then you think about the conflict. If this outlier is true, then what does that mean what does that mean for other people or in something in the future what what impact does it have.

[00:29:07] And then think about timeliness. Why does this matter now, you know, things that are evergreen, evergreen information is fine, but you're much more likely to grab people's attention if there's some sort of timeliness aspect.

[00:29:20] Is there something you can compare it to that is happening now like maybe it's something about the Olympics and the Olympics are right around the corner you know like there's something you.

[00:29:28] It's really helpful to have some sort of timeliness aspect. And then resolution, what could make this better, what where can people find more information. And if you can answer those questions.

[00:29:39] It'll help you focus your visualization so that it catches your readers attention and they actually want to read it. And I have done this a lot of times where I answer those questions and then it gives me a better question to ask and then I can start again like okay this is my

[00:29:54] question to ask. What's the timeliness, what's the timeliness, what's the resolution. So you can think about it as an iterative process where it can really help you define why is this data meaningful to other people because that's ultimately we want to do.

[00:30:09] We want to create meaningful data visualizations for other people. So the CTR prompt will really help you do that.

[00:30:17] It's so funny because I mean I read this chapter in your book. And I'm sure this is 100% my fault, but I didn't get what's what the CTR was and I haven't baked in my brain that CTR is click through rate.

[00:30:29] Yeah, because yeah so who makes like YouTube videos you're always like looking at what your CTR is it's like how many people actually clicked on this video that saw it. And and so I was thinking that like the CTR was click through rate because it's just break into my brain that's what CTR stands for.

[00:30:44] But I kind of like it that way too because it's like if you're if you're if there's no conflict if it's not timely and there's no resolution why would anyone want to click on it so.

[00:30:53] That's right. Yeah, if you have CTR you will get CTR.

[00:30:56] Yes, it works both ways so anyways I think that's awesome and I love your point that it's like in the book you have a really good example of you take make over Monday project where the avocado is like avocado organic sales versus regular and how you kind of just basically

[00:31:12] change. I mean you change the chart chart type a little bit but not even a ton but even just changing the title can be can can really make the story resonate a lot more.

[00:31:20] You're right. Yeah, you don't have to again you don't have to do anything wild in order for something to be creative and creative meaning like you went a little bit outside the norm you remixed something and it was more useful to people.

[00:31:33] So by that standard, you don't have to do a lot to be more creative so you run to the CTR prompt and you realize oh this is kind of the part that's the most interesting to people not avocado prices over time like who who cares you know.

[00:31:47] But oh this is the time to make your make your guacamole for the big game because avocado prices are the lowest they've been in the last few years. Oh, okay that is helpful to me. Thank you. I'm going to now take an action.

[00:31:59] So just a small little tweak like a title can really be helpful. It becomes a story at that point which I think is what we care about as humans. Now you mentioned something just barely that when the editor shut that project down that you didn't know why like

[00:32:12] oh this was good and I just probably didn't present it the right way. And you said that you studied like other publications. What are some like go to resources for you when you're trying to get inspired or learn like where do you go to like kind of get inspiration?

[00:32:25] Yeah, the data visualization society took over the information is beautiful awards and every year data visualization designers independent at companies at news outlets they submit visualizations and then they're judged and then people win for a second and third but then you can also just look through the entire submissions all the submissions so if you are ever not sure where to find inspiration for amazing data visualizations

[00:32:53] they have all these different categories like business dashboards illustrations data stories news like all these types of categories that you can search through going back years so you can go to the information is beautiful awards website and find unlimited inspiration there.

[00:33:10] I love it. That's a great suggestion. Where can people go to learn more about data is designing and information design and and chart spark your book.

[00:33:19] Yeah, you can go to chart spark book.com and if you go to chart spark book.com bonus I have a bonus chapter that I wrote about all about communicating your design decisions so like what do you say when you are presenting an idea so if you want a bonus chapter of the book you can go there that's free.

[00:33:36] And then of course you can buy the book I have it in paperback and ebook PDF and audio book so if you're listening to a podcast you obviously like audio I haven't an audio book and it does work as an audio because as you know from reading the book it's not really graphics heavy it's more about the ideas and the prompt so it totally works as an audio book.

[00:33:55] And I of course have the data is today podcast so if you like podcasts then go there and learn more about data visualization.

[00:34:03] I love it. Yeah, you guys can check out the podcast. Did you just hit 100 episodes or is that coming up? Yeah, I think it is it is just about 100. You know some of them are bonus episodes. So the number episode number isn't up to 100 but yeah I have been over 100 episodes.

[00:34:16] Yeah, so congrats on all that definitely check out the book you guys will have a link to it in the show notes down below. It's all about how to be creative as a data professional and this book honestly is is unlike any other book I've really read to be honest it was really fun to read through and kind of go through these prompts of being more creative.

[00:34:31] I don't really think about being creative too much so now you will.

[00:34:34] I will and I have the skills the prompts to do so so Ali thanks again for coming up. Thanks Avery.