Episode 39: [Mini] 3 Design Tweaks that Make a Big Difference
 
alignment.png
 

Welcome to episode 39 of Data Viz Today. I’ve been on a mission to improve my design abilities, and there are three design tweaks that I’ve found to be really effective in making my visualizations look more professional. In this episode, I share these three tips that pack a punch!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:15 - Today’s topic is 3 design tweaks that I’ve learned this past year that instantly improve the design of my visualizations. I’ll explain those three things, and I actually took an old chart that I had made and implemented these three things so you could see the difference. There are still a lot of things I’d change about that chart, but it’s interesting to see the transformation with just three tweaks. See below!

  • 01:10 - First, alignment. I try to make sure everything that I put on a page is aligned with something else, or there’s a really specific reason why it’s not. Usually I find left alignment looks best, especially with bigger chunks of text. In Adobe Illustrator, I’m always setting up my graphic with straight guidelines that I can use to snap all my elements to. You could mimic this in powerpoint or tableau by drawing your own guidelines along the margins or between groups of information. In Tableau dashboards, you can toggle on a grid by pressing the G key, which is super helpful. So be aware of what each element is aligning with, and also an alignment that’s easy to overlook - the vertical and horizontal spacing between your elements. Make sure there’s consistent space between your elements as well.

  • 02:10 - Second, text hierarchy. We talked about annotation hierarchy in episode 7 “How to Annotate Like a Boss” where you have a title, lead-in text right below it, then maybe subheaders and explanatory text within your chart to call attention to the points of interest. And all those text elements have an order that’s ideal for your audience to read them in. So how do you convey that order? Usually I go straight to size of the text, which definitely works well - bigger things are more important. And another option is weight. Many fonts of a light, regular, italic or bold option so you can layer those to create a hierarchy effect. Another way is color. One technique that I’ve been experimenting with is using a slightly lighter black, which can be easier to read that straight black, for the title, and then smaller text that’s grey for the subheader. Or for a white text that’s on a darker background, you can make text look less important by giving it a little transparency so it takes on a bit of the background color so it pops less than the white title. I’ve used this technique well with labeling. Like if I have a treemap and I’m labeling each segment with the category and the percentage, I write the category white and then the percentage in white with added transparency. It’s legible and cohesive because I’m not changing much, but it still gives a little hierarchy.

  • 03:50 - Third, keep annotations simple. After doing episode 26 “How to Develop Your Design Eye” I realized how crazy I was with my annotations. You can see in the shownotes for that episode, I had created a viz for rainfall in DC and then I came across a similar viz by Jane Pong for rainfall in Hong Kong a few years earlier so I had the unique chance to directly compare what I created with something a pro created with a very similar dataset. And one big thing that stood out is that Jane had these really elegant and understated annotations calling out interesting days with short, slightly curved black lines and text within the chart, and by comparison, my annotations looks almost comical because the lines were really long and flowy leading out to text outside the chart and colored red! Then I also had these big clunky arrows along the axes as if people didn’t know which direction to read. So now, I keep my annotation lines as short as possible, with only one curve (not bending in and out and around), and in a consistent thickness. Like the line doesn’t start small and get bigger. I’ve noticed that when I use arrows or lines that do that, it looks more clip-arty.

  • 05:30 - One resource that really helped me as a beginner is Canva’s Design School. They have a bunch of interactive tutorials and courses that do a good job of teaching some basics.

  • 05:50 - My final takeaway is that there are a few design tweaks that can get a lot of mileage out of. And for me, those have been making sure each element is aligned with something else, make sure your text has hierarchy with size, weight or color, and lastly, keep the arrows and annotation lines simple.

  • 06:30 - You can sign for my weekly newsletter that I send out every Sunday with top tips from the episode. I love mailing it out every week and getting your replies!

  • 06:35 - I also have a resources page with my favorite books, blogs and podcasts.

  • 06:45 - And I also have two online courses - one for creating your first custom map using Mapbox, and the other is a shortcut to learning how to create charts in Adobe Illustrator.

Here is an example of those three tips applied to an old viz. There’s still a lot of changes I’d make now to this viz, but doesn’t the new one look a little more professional with better alignment, clear visual hierarchy, and simple annotations?

 

My old visualization BEFORE tweaks

AFTER - making a few tweaks

AFTER - making a few tweaks

 

Allison Torbanmini, design
Episode 38: How to Use Writing to Improve Your Vizzing - Featuring Tiziana Alocci & Piero Zagami

Welcome to episode 38 of Data Viz Today. Can you write your way to data viz success? It might be hard to see how writing could improve your visualizations but in this episode, I'll lay out 3 compelling reasons WHY you need to start writing today. Plus, I'll share easy 3 steps to get you started.

This episode is inspired by the creative passion project Market Cafe Magazine that's created and independently published by Tiziana Alocci and Piero Zagami. Listen to what it takes to self-publish a data viz magazine!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:23 - Today’s episode is all about using writing as a way to improve your data viz skills. The project that inspired this episode is the data viz magazine called Market Café Magazine that’s independently designed, written and published by Tiziana Alocci and Piero Zagami.

  • 01:10 - In this episode, we’ll find out why Tiziana and Piero started a magazine about data viz, how they pull it all together, and how it’s changed their lives…Then I’ll jump into the reasons why YOU need to start writing about data viz too, plus 3 tips to get you started.

  • 01:35 - Tiziana and Piero are both accomplished freelance information designers in London.

  • 01:40 - In 2017, they were super active in their local data viz community - constantly going to events and conferences, and couldn’t get enough of consuming and creating visualizations. They started to feel this itch that they wanted to dig into the process of how designers were going about their craft, and share those stories in a unique and artistic way. The decided that they’d figure out how to curate stories from interviews with designers that they admire and make a magazine for the community to enjoy.

  • 02:20 - But a little wrinkle…they didn’t have any experience in journalism or publishing. So they obsessively started building a database of practitioners that they wanted to interview, topics that interested them and that led to research on templates for the content, and they began briefing contributors, and working with sub-editors. It started to come together and they kept experimenting and trying…

  • 02:55 - Being freelance made the process of putting the zine together a little easier since their schedules are flexible, but it still required a lot of sacrifice and long nights and a ton of visits to the post office.

  • 03:05 - They have 3 issues under their belt now so they’re slowly getting processes in place and more streamlined. It’s been taking them a few months to put together each issue because they take a very artisanal approach to creating the zine: they do tons of print tests, color studies, and image manipulation to apply this cool and unique look of their zine where each issue features two new colors.

  • 03:30 - But the 4th issue, due out very soon, has been 6 months in the making…Tiziana and Piero say that’s because while the mechanics are getting easier for them, they’ve found that they like pouring lots of time into researching right people to interview. Part of their secret sauce is interviewing all their contributors in person to make sure they can capture the human-side of every story. It’s admittedly time consuming and exhausting but non-negotiable in their opinion.

  • 04:11 - They use Adobe InDesign to create the layout of the zine and then work with a third-party printer to do a limited run of each issue. They have a unique point of view in the way that they put it all together so it’s an experience for me.

  • 04:40 - So crafting this magazine is obviously a labor of love, so why do it? Why write about data visualization? Tiziana and Piero have found that every time they interview someone, they not only learn about someone else’s experience who is probably socially and culturally different from where they come from, but they also get a fresh injection of knowledge and enthusiasm from this person. I’m sure you’ve experienced this before – talking to someone who’s really passionate about subject is very energizing.

  • 05:30 - Ok so writing about data viz has been fulfilling for Tiziana and Piero, but why would you want to write? Here are three reasons why writing can help you improve:

  1. It’s a creative outlet, you have complete control over what you write about and how you do it. You don’t need to create in the same way that you’re used to consuming it. And that exercise in creativity is great exercise for creativity in your visualizations.

  2. When you write and share, you’re inviting others to engage with you. You make new connections and broaden your network. Especially if you do interviews or collaborations. You make some talented friends that you can learn from. Related to that, when you write about your experiences, you’re helping other people. Maybe you don’t want to write about something because you feel like other people have already written about it, but the one thing that no one has - is your experience. The events that have happened in your life and your reaction to them. Those are unique to you that can help someone else. And I believe that no matter where you’re at, you’re one step in front of someone else and you always need to be looking back to see who you can lift up.

  3. You learn more about yourself and the topic by writing about it.

  • I noticed a tweet by Alberto Cairo not long ago, saying that he asks his Intro to Dataviz students to create personal blogs and write about their
    weekly readings. And I asked him why he does this and he said that he’s learned
    that the best way to memorize something and understand it well is to force
    yourself to write publicly about it. He asks them to not just summarize the content of the book but to connect it to their own experience. Read more in this post.

  • I’ve definitely experienced the benefits of this. By researching a topic, like the episode on box plots, and then weave it into a story and add useful tips, I really have to dig deep. It takes between 10 to 20 hours of work for each episode. And I’ve learned so much more than if I just read someone else’s post or a Wikipedia article.

  • BONUS why: career builder, you’re creating a body of work that’s showing how you think and your expertise. That’s pretty valuable.

  • 07:50 - Ok so those are some great whys, but how…I know that writing your own content is daunting for many reasons. But here are three quick steps that you can do now in just a few minutes that will make writing about data viz feel closer…

  1. Announce a schedule and keep it. Tweet it, tell your friends, or just text your mom. Just someone who will ask you about it so that you keep moving forward with it.

    • Being consistent is really where you can see growth and reap the benefits. Two posts a year isn’t going to move the needle.

    • I remember Seth Godin, the famous marketing guru who has been writing a blog post every day for years, he said the fact that he lives every day thinking that I have to write something worth reading tomorrow completely changes the way he navigates through life. He notices the little the things, he’s more in tune with things that pique his interest. I thought that was an interesting take – just the fact that you have to say something worth listening to on a consistent basis can make you up your game. Change the way you think. So make a schedule and keep it. Consistently write about your data viz experience.

  2. Think about who you’re writing for – narrow down your audience. It can really help you narrow down the topics you’re going to write about. Just start with an audience of one – yourself. Draw a circle that’s you – draw 2 or 3 lines coming out of your circle and write a few things that you like. Cooking, sports, podcasts, parenting, running, legos, videos…. Then draw a bigger circle around all of that. That’s your audience right now. You plus anyone else who likes the couple of things you like too.

    • I like short bits of data viz tips and podcasts. So here we are. I got a lot of questions about why I’d put a visual topic into an audio format, but that’s what I was interested in, and it turns out that other people are interested in it too! It’s not for everyone, but it’s helpful to some people, and that makes be really happy.

    • Tiziana and Piero like boutique artsy magazines, and stories about data viz designers. They gave me really great advice for anyone looking to start writing about data viz. They said, “keep it tight, specific to the topic you love, no matter how niche or weird (in fact, the stranger the better). Be honest, insightful, and establish a regular flow of content, so your audience can learn what to expect and when to find it.”

  3. Create a template – this is a content creation secret that I’ve found that the best bloggers use. Have a few general themes that you rotate through. It’ll help keep you focused and feels less intimidating when you sit down to write.

    • Here’s an example: If you’re blogging once a week, first one is a new technique you learned, second week is the process behind a new viz, third one is your opinion on something you read or a piece of news, and fourth week is the best viz you came across that month and what you liked about it.

    • And within that kind of template, keep in mind that the best posts do three things: Inspire, educate, entertain. If you can do all three within a post, amazing. But I’ve found it easiest to focus on one to be the star and have the other two be co-stars. If you couldn’t tell, the star of this episode is inspire.

  • 11:55 - So you got your schedule, you narrowed your topics a bit by your interests, and then write your template or general themes that you’ll use within your schedule. Then you sit down to write your post, think about what’s the star of this post – am I tying to inspire, educate, or entertain?

  • 12:20 - My final takeaway is that if you consistently write about your experience in the visualization field, you can start reaping the rewards of having a creative outlet, making new connections and helping others, really learning and going deep on the details, and building your own body of work that can help your career.

  • 12:55 - You can keep up with everything Market Cafe Magazine on twitter @MarketCafeMag and snag an issue here: https://marketcafemag.bigcartel.com

  • 13:15 - Sign up for my newsletter right below… :)


BONUS: Setting Your 2019 Data Viz Goals + Data Stories 2018 Year in Review Rebroadcast
 
2019Goals_SQUARE.png
 

What a year… I was honored to join Enrico Bertini and Moritz Stefaner on their Data Stories podcast along with Cole Nussbaumer Knaflic and Jon Schwabish to discuss our highlights from the year and what we hope to see in 2019.

I’ve posted a re-broadcast of the episode below. I also added an intro with some thoughts on my 2019 data viz goals and ideas on how you can tackle your own. I’d love to know what yours are! Here’s to another year filled with data viz projects, collaborations & learning…

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

2019DataVizGoals_PNG.png

What are your data viz goals for 2019?

Feel free to download the PDF that I use. I’d love to hear about your #goals!


Allison Torbannew year, 2018
Episode 37: How to Be Consistently Creative - A Journey to Find Info We Trust - Featuring RJ Andrews

Welcome to episode 37 of Data Viz Today. I recently caught wind of a forthcoming data viz book all about the craft of being a data storyteller called Info We Trust by RJ Andrews, and I wanted to know all about it. So I hopped on the phone with RJ and got the scoop on how he cleverly structures his days to be the most creative data storyteller and author that he can be, plus he shared his essential components to being consistently creative, even when faced with a limited toolset or under a deadline. I hope you enjoy our chat, and you can pre-order his book (due out January 2019) here!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

Alli: 00:00   Hey, you're listening to episode 37 of Data Viz Today today. I'm Alli Torban, and every week I bring you data viz inspiration and actionable tips by featuring a creative project. Thanks for joining me. I am super excited about today's episode because it's a little different. It's a little longer than usual, but also very special. I've been on this data viz book kick lately. I've been trying to gather as many as I can, read as many as I can because I'm trying to get away from the computer screen a bit so when I caught wind of a new data viz book all about the craft of being a data storyteller, I wanted to know all about it so I was able to hop on the phone with the author, RJ Andrews, and I got the scoop on how he cleverly structures his day to be the most creative data storyteller and author that he can be. Plus he shared his essential components to being consistently creative, even when faced with a limited tool set or under a deadline. I want you to hear every minute of that discussion that I had with RJ, so that's why this episode is a little bit longer than usual. So RJ Andrews is an independent data storyteller based in San Francisco who's recent clients include MIT and Microsoft and now he's working on a new book Info We Trust due January 2019. So here's my discussion with RJ packed with lots of wisdom. I hope you enjoy it and I'll hop back on at the end and share my main takeaways....

Alli: 01:20   I know that you're an independent storyteller and I've kind of always wondered, how do you structure your day? Do you kind of have every day looks different or do you try to keep like a general structure?

RJ:    01:36   So I think that there are some common structure elements that I aspire to. I think it's important when you think about a structure is that the structure has to last for the longterm. And so what that means is that you have to play not only within your structure, but you have to play with the structure itself. And if you don't, you know, always hit all the goals that you have. That's okay. It's okay if you didn't make it to the gym today, but you're gonna make, make it to the gym tomorrow, right? Because you can't be too hard on yourself or you're not just, you're not, you're not going to be able to do it for the long term. Alright? So with that sort of caveat said, you know, today is actually a pretty, pretty good representative day of this morning. I got up, I chauffeured, my wife to work.

RJ:    02:23   She works on the other side of the hill and I always drive her to work when she's working. I came back, I had coffee already brewed, I grabbed a coffee, I went for a walk on the bay. So I live about two blocks off the bay and dogpatch neighborhood of San Francisco. And today was, today was a lucky day. I saw a really young juvenile sea lion, not in the water but actually up on the shore. And so that was just, you know, the sun is rising, there's all kinds of shore birds that you're looking at and you see the sea lion and it's just, it's time to think about what am I going to do today, you know, what does the world need from me today, whenever you get to walk and walking is an incredibly powerful activity because it allows your body to move and kind of be busy doing something and kinda clears up a lot of space in a way that if you're just sitting in a room and like thinking and only thinking, you know, the thinking you have when you, when you go on a walk is very different.

RJ:    03:22   So whether it's a walker, a light jog, that's how I try to start a lot of days for my most productive days. I have two heavy creative times, time periods and they're usually in the morning. So during the time we're talking right now, I'm very high energy then. And usually late at night I can often fit in another sort of burst of really focused high energy, creative time.

Alli: 03:44   How long do those times usually last? Is it a couple hours?

RJ:    03:47   It really depends on the activity and this is something that I've heard from artists and engineers and all types of people who make things is that one of the reasons we like making things is because we can get into a flow state and just almost kind of become one with the work and have our whole identity honestly sort of just fade away and we just become completely consumed by the work and it's honestly addictive, you know, to get into those kinds of flow states in order to get into that flow state and stay in it though, you really have to carve out a lot of time or protect it.

RJ:    04:25   And so hopefully you can get into that state and then stay there for as long as you can either because you're not being distracted by emails or meetings or you know, other kind of normal responsibilities. It's usually not that you're exiting those states because you know, you're burned out. Like if you can get there, usually can stay there for awhile. That said at the end of the night, you know, sometimes you're just, you're just tired, so you make it a priority to protect your flow state.

Alli: 04:55   And so you said you have two times during the day that you do that. And then what about in between?

RJ:    05:00   So inbetween, which for me would be late morning or early afternoon, I often exercise then because my mind is kind of like turned to mush. I'm not super high energy mentally, but it's like, okay, this is a good time to slug some lead or you know, get some miles under the belt or whatever.

RJ:    05:19   I don't really play social sports, but I do like activities where I can keep thinking kind of in a different way. And so there's this idea of while you're exercising for athletic competition or something, but there's also that idea of like, you need to exercise for cognition. You only get one meat vehicle in this life and you have to, you have to really take care of it. And so you have to be strong in order to have high energy to do the work. Your body has to be healthy. So that's one side of it. But the other side is that in a similar way to walking, like when I'm swimming in a pool or a paddling on my kayak or running or even slugging lead, then part of my brain is occupied doing the activity and it. And it frees up the rest of it to kind of wander and have thoughts and have a little bit of a, some people call it empty time, I think of it as free association time, but it's, it's, it's time that you know, your mind can wander. And that's similar to when you're under a hot shower, you know, and you have like these mental wanderings, you know, you can achieve that without always jumping in the shower.

Alli: 06:26   Yeah. That's really interesting. So you have these creative times, then you have all these breaks where you let your mind kind of free associate all the ideas you've been. Yeah, that's really cool. But now you are writing a book called Info We Trust and it's due out in January 2019. So when did you first get the idea that you needed to get your message into a book?

RJ:    06:53   I think that Info We Trust probably has a bit more complex of an origin story. One of the key insights in the very beginning from my perspective was sort of this idea of information vs informing. And so I really love information. I love making information. I love maps, I love charts. I love diagrams ... I really, I really love it all. But what I love even more is uh, helping people become informed, you know, focusing on information. It's really easy to get lost in the machine side of things. Well, one of my observations was that quite a lot of the chatter, both in books, conference talks, blog posts, you know, twitter or whatever. A lot of it is all about sort of the, the technical, like how do you, how do you make the machine. And I was very, very interested in sort of like, all right, once we have the information, how does it, how does it actually inform? And so to take from design world and take a very human centered approach to the craft information on its own doesn't create any meaning, meaning only happens when the reader, the viewer, the audience interacts with the information and it's the connection between the information and how the information excite some something in that person's mind that that's where the meaning comes from. That's where all the value comes from. That was one of the really one of the big early things that I wanted to focus on with this book.

Alli: 08:28   Who do you think that this is the ideal book for and what do you hope that they will learn from it?

RJ:    08:35   So Info We Trust is for everyone who believes in pursuing new and wonderful ways of looking at the world. I wrote it for everyone. Not only people who want to understand and see things better, but also want to help all the other people understand and see things better too. You know, the first word in the title, Info - information - informed, informing, but then there's this last word in the title which is trust. Trust is a pretty interesting word and it's one that I address a lot in the book, but the big idea is that, you know, we all pretty much know nothing. We all know only a little bit and thank Gosh for that, if we all had to know everything, we would have never escaped the bronze age because we really depend on the knowledge, information and wisdom of others.

RJ:    09:27   Information is the way that we you know, it's kind of like this glue that kind of stitches us all together. People who are really excited about maps, charts and diagrams, but how maps charts and diagrams sort of help stitch, not only helps stitch society and civilization together, but how they all help actually improve and advance civilization together. It's very much a kind of like we're all in this together. So that's why I wrote it. I promise you that reading this book will show you new ways to bring meaning not only into your own life, but into everybody's lives.

Alli: 10:07   I'm really excited to read it. When did you first start writing? Was it earlier this year or in 2017?

RJ:    10:14   It was in 2017, so I signed the contract at the end of October and started writing or at least started the process of writing in early November. So, just about 12 months ago.

Alli: 10:30   Wow. That's a lot of work.

RJ:    10:32   It is a lot of work. But really it's a luxury. The ability to take a year off from normal work and completely focus myself. I mean, I'm really, really grateful that the universe shone down and gave me this opportunity to completely focus on this. I'm now at the end, at the end stages of a book production about to return to doing my more normal activities. But for a solid maybe 10 months or so, it was only the book every day for a very long stretch, seven days a week, many 12, 14, 15 hour days, you know, hammering on this, trying to make the most of the time I got to spend with it.

Alli: 11:21   Did your creative process change at all from your schedule when you were doing freelance work or do you kind of try to keep the same structure where you had some exercise time, you had two creative time periods and other times you were consuming information or did you change it up at all for the last 10 months when you were writing the book?

RJ:    11:41   So the nature of putting together a data story and putting together a book about data storytelling, they each have a little bit of nuance. I think that, to be honest, technically a book is not a impressive artifact, right? I mean we've been making books since Gutenberg in 14, 15 century or so. You know, we sort of figured out how to make books. That said, the complexity management process of actually putting together this book has been way more intimidating, more way, more challenging, way more satisfying than any data story I've ever put together. Either publicly or for a client, even though the technology isn't hard. The book's hand illustrated, so I'm using markers and papers and I'm typing it out in a variety of word processing programs. It was really challenging and so the routine sort of reflected my attempts to try to wrangle the whole book writing process.

RJ:    12:49   So my approach to writing the book was very strategic from the beginning. What I did was I identified that I didn't want the book to be too influenced by fashion, sorta like what's the hot topic on data viz twitter today. And I also knew that I wanted the book to be written in my own voice and so how I chased those goals was I first went and read everything about the craft of data visualization and data storytelling written before 1985. Alright. Why 1985? It was about the year when interactive computer graphics really started to take off and you started to see that in the literature. And so the first thing I did was I spent pretty much the month of November reading all the classics, so Turkey and Tufte and Nigel Holmes and William Cleveland. And so that's, you know, late sixties throughout 1985.

RJ:    13:49   And then, I went even deeper. And so I read everything from William Playfair through the early 19 hundreds and there's a couple of interesting things, you know, even in the mid 20th century, I read all of that and I took careful note of what people were talking about when they're talking about informing each other using data that still resonated with my experience as a creator. And so I wanted the whole book, there's sort of some kind of advice that's really useful for analysis, meaning that we can, we can kind of talk about whether or not this thing is working, you know, maybe the psychophysics community is more interested in that type of advice or information. But what I wanted this book to be was really about creating, not about critics but about producers, creators, you know, how to make because like, because that's what I am, there are plenty of critics in the world, you know, I really believe we need more more makers and so we have this one sort of input which is all of this pre interactive computer work.

RJ:    14:52   And then we have this second input to the process which is my own experience as a creator. And I assume that whatever overlapped from those two input sources was essentially timeless. You know, if it was true before interactive computers and I still find it true across my modern work. Then that thing has a really good shot at being a timeless thing. And that's the kind of material that I wanted to construct the skeleton of the book out of. So I did this first wave of research and I had these two inputs. I figured out where the overlap was. And then I wrote the first draft of the book, and that first draft of the book, you know, came together probably about mid January, 2018 and then once that happened I was able to open up my research process to the rest of the literature to help sort of polish and sharpen and focus the narrative journey.

Alli: 15:49   I think I and many of the other people in the data viz community see you as one of the most creative makers out there. And, but I think that a lot of people also think that creativity is kind of like a lightning strike, but you strategically schedule it into your day. So I was curious, what do you think are the essential components of being consistently creative in your work?

RJ:    16:14   So lightening strike sort of Aha moments. They certainly happen and it's wonderful when they do happen. I believe though, that you can construct an environment via your creative routines, your daily routines, but also sort of like your cognitive environment in terms of what you're thinking about. Right? Because what you think about is what you are, right, and so how do you do that? So there's a lot of different theories of kinda like what creativity is. One of them centers on this idea of intersectional creativity where creativity it's very similar to metaphor. We're making a new thing is about connecting to existing things in a new way. And so we do that with language when we try to use existing concepts to describe new things. I'm looking at my computer screen right now.

RJ:    17:07   So the computer screen is full of iconic metaphors, right? So we have a trashcan to throw files out and sort of depending on this old concept, you know, the save icon still looks like a floppy disk even though a lot of people who use the save icon don't know what a floppy disk is. And so that's sort of what I mean. That's honestly kind of what creativity is. It's like you take something that's existing, that's old, maybe you smash it with something else. Most famously kind of George Lucas does this with star wars where he takes a lot of Japanese films in World War II films and kind of rips favorite scenes from one of the other layers that I had Joseph Campbell myth structure and we have star wars. And so if you can accept this idea of intersectionality, then what you need first in order for the Aha moments to fire is that you need a really good warehouse in your head of content to connect.

RJ:    18:02   And that's actually like really, really fun. There's just so much knowledge that is at your fingertips all the time that it's very easy to become almost like an overnight expert. That's kinda what it feels like, you know, to study up on something and not actually become an expert, but if you can become functional in something like pretty quickly. And so the first step to being creative is to immerse yourself in and fill up a warehouse of knowledge. And again, maybe it's not in one category or one topic, but maybe it's in multiple. And of course you always have just the regular sort of information that's in your longterm memory. Nobody's kicking around in your head. I see that as step one. I think that step two is, you know, kind of dropping everything and walking away for a little while.

RJ:    18:49   You need some of this time that we talked about earlier, which was this, you know, whether you're exercising or sleeping, but you need time for some of that knowledge to be filtered from your short term memory into your longterm memory and let those concepts either, settle in with how you think about the world or maybe challenge how you think about the world because a lot of the concepts won't really be in harmony with the ways that you thought about things. And then hopefully if you give not too much time because you don't want to forget your whole warehouse of thoughts. Then you start making sort of connections between the thoughts that you just suggested, but also between the new thoughts and the thoughts you've been carrying with you for a long time. And that's where I believe that the Aha moments sort of come from is when you start making those connections.

Alli: 19:37   That's fascinating. So warehouse information and then step away and give yourself time to make connections. So once you do make those connections, so you get some really great ideas and you have to start executing them. I think it's hard sometimes to be creative and then come back and you're limited by your tool set. So how, how do you handle when you get ideas and then you're not sure how to execute.

RJ:    20:05   I think one of the best things you can do is step away from the computer. You know, I'm constantly moving from the screen to analog sort of paper and pencil kind of tools. I want to give a really heavy endorsement for working by hand for many reasons. One is that whatever you output, it's not going to be, it's not going to be content driven, right? It's very hard to do data driven work by hand. Obviously, you know, some people are doing it right now, whether it's Georgia Lupi or Amy Cesal, it's possible to actually do data driven stuff by hand, but that's not, that's not what I'm talking about. I'm talking about not exploring content driven work, but actually exploring the form of the work so it's like this is what it could look like. This is what it might be because then you can jump back onto the screen and actually implement it.

RJ:    20:53   In a way that I'm not a confident enough or talented enough coder, like some people are. Some people are able to do all of this work purely with code. For me, I'm not fast enough. That becomes a little bit limiting and constricting for me that I just don't have. I don't feel like I have the same amount of freedom as when I'm working, you know, with hand drawn sketches for example. But hand drawn sketches are even more powerful than that. Say you are very talented with code drawing by hand. It'll still give you a different way of looking at things. And then, you know, this isn't, I mean, a lot of the work that we do is solitary, you know, you're by yourself in a room, headphones on, dialed in, focused. But ultimately this is a people facing art and we're always working with teammates and colleagues.

RJ:    21:41   And the thing about showing someone something that was built with a computer is that it looks polished. I mean, it's one of the sort of the things we really haven't figured out, but you know, everything looks legitimate, right? Like even like even fake news on facebook. Right? And so one of the nice things about showing people sketches is that it doesn't look polished. It's like it's obvious that this isn't finished. And it's very easy when you show somebody a sketch to direct their attention to the aspect that you actually want to talk about because you know, they're in a design. There's maybe 20 things you could critique if you show something made by the computer, it's easy to get distracted by the 19 other things. Not the one thing you want to focus on during a particular conversation.

Alli: 22:32   I never thought about that sketching has the benefit of, you know, looking unfinished. That's actually something that could work in your favor.

RJ:    22:41   Yeah. And this is something that Elijah Meeks has talked about a little bit and because he's, I think, gone to some great effort to include some sketchy styling and semiotic and I think he's written on it from this perspective as well.

Alli: 22:59   Yeah, I love that.

RJ:    23:01   Info We Trust the book is very technology agnostic. That it doesn't really address any particular tool or programming library that was also very strategic. The whole book is hand illustrated. I obviously use quite a lot of tools to make the charts and diagrams and views that eventually became hand illustrated, but there's honestly just too many tools that I use too many tools and there's even more tools out there that I don't use. Right. And I'm certainly not, I'm not an expert in any tool and that's sort of a nature because of the, because of the multidisciplinary aspect of the craft. It's so hard to become an expert.

Alli: 23:42   Well, I think one of the struggles that I have with creativity in my work is that a lot of times I'm under a time crunch, like sometimes at work I'm given a data set and a goal and I have to send up some prototypes within a day or two. So I'm wondering how would you approach a situation like this where you can maximize your creativity if you're under a time crunch?

RJ:    24:08   Yeah, sure. So this theory of creativity... It's very demanding of your time, right? Because I'm saying that, you know, do a lot of work then step away, that's more time and then come back to it, that's really intensive. So first I'll say, I'll applaud you and say good job prototyping. Prototyping is very, very important. Because the worst thing you can do is go off into a cave for three months and make the perfect solution and then come back and have it be wrong because you've just wasted all that time. So yeah. So the first thing is like, you know, you go off and put your headphones on and make something but have regular interactions. You know, before I make an interactive project, I always first sketch, like a static version and have a discussion with the partner or client or whoever, so this is working, does it work static, you know, what are we going to get by adding more data, what are we going to get by adding interactivity and sort of almost baby step things towards the grand vision.

RJ:    25:17   So prototyping is really useful and I think a really great way to rapidly improve a project. And so in order to prototype on a tight timeframe, what you need is heavy engagement. And this is one of, one of the things, I sort of have this list of things I talked to with new clients and I said, look, the more you lean into this process, the more information that flows between us, the better the project's going to be. At the end, and so in a tight timeline, what that means is that you need really tight engagement. You need people who, whoever the stakeholders are who are either the recipient of your work or somebody who is informing your work. You need them to really be on board with your process and really be available and accessible and just as enthusiastic to make that new vision that you're trying to achieve.

RJ:    26:13   So that's our first thing - having engaged stakeholders. The second piece is that you need to limit your scope, so you need to be very specific as specific as you can about this is what we can achieve in this timeline. And if we had more time, this is what else we could achieve. And that's a nice way of putting it because it's a little positive. It's not saying, well I can't, I'm not going to do these things. You know, it's like, if we had more time we could accomplish these other things. But it helps because again, it's the people facing art. It helps everybody who's around the table understand what's possible given the time that's being afforded for this particular part of the project.

Alli: 27:02   Yeah, that is really great advice.

RJ:    27:04   Maybe we could end on quoting Elijah Meeks because I think that so much of what he says is so smart, but he was talking about color specifically, but I think this quote extends to the whole craft... "Recognize that it's hard and that it's going to take time and effort. Point that out to your stakeholders. Schedule some time for it. Don't just brush it off. One major reason why people are so bad at color and data visualization is because they don't budget any time for it." And I think that's true not only for color but across all dimensions of the craft.

Alli: 27:39   That's really smart. Well, thank you so much for being with me, RJ.

RJ:    27:43   My pleasure. This has been really fun.

Alli: 27:48   Thanks again to RJ for sharing so much wisdom. I learned a lot during our discussion and as always I wanted to highlight my key takeaways first. There is no one right way to do things. There is no one best creative routine, but RJ has found that the essential components for a creative routine are

1. periods of gathering lots of information and then 2. move into a period of light exercise or rest where you can kind of free your mind to free associate the ideas that you just collected and then 3. carve out time periods in your day where you can enter a creative flow state. RJ said he schedules to have those time periods in his date where he's high energy.

Alli: 28:30   Then when you do have a creative idea, try sketching it out. First, the benefits are that you can explore the form of your idea rather than the technical aspect of what you can and can't do in a certain tool and your idea doesn't get influenced by your tool knowledge. It has the benefit of also looking unfinished so you can more easily focus someone's attention like your client on your specific idea and when you're trying to maximize your creativity under a deadline. Know that this calls for heavy engagement from your client so that you can rapidly prototype and make sure to limit your scope, start working to train your clients to understand that data is as hard and they need to budget time for it.

Alli: 29:10   Finally, the thing that RJ said that really stuck with me was that we need more makers and fewer critics, and I'd love to know what part of my chat with RJ really resonated with you and if you share it on twitter, make sure to tag me @DataVizToday and RJ who is @InfoWeTrust

Alli: 29:27   I'm really looking forward to reading his book Info We Trust, which I know is going to be a timeless look at the craft of data storytelling. You can preorder it on Amazon now!



Allison TorbanAndrews, book, creative
Episode 36: How to Experiment With Visualization & Handle Critiques - Featured Data Visualization by Richie Lionell

Welcome to episode 36 of Data Viz Today. Have you ever pushed the boundaries of visualization? Did you receive any push-back? Do you want to experiment more with new chart types, but you’re not sure where to start or maybe you’re worried about people’s reactions? In this episode, we’ll hear how Richie Lionell created his thought-provoking data viz, how he handled criticism gracefully, and how you can get started creating something new in spite of potential negative feedback.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:23 - Today’s episode is all about experimenting with visualization and in turn receiving feedback and critiques on it.

  • 01:20 - Today’s featured data viz is called “Senator Voting Patterns” by Richie Lionell. Richie heads the Story Labs at Gramener, a Data Science company based in India.

  • 01:30 - He first came across senate voting data on the senate.gov website and was super excited to find it in a clean xml format, so he scraped the xml files and converted it to CSV.

  • 01:42 - His very first thought was to find out how each senator’s voting pattern compared to the other senators. So he wrote a simple python script to compute the simple similarity score for every senator with every other senator. He did this by taking a sentor’s vote (yea or nay) for each issue and comparing it to every other sentor’s vote.

  • 02:25 - Initially he tried conveying this story through a network layout, but it turned out to be really cluttered and hard to read. So he had start making some tough choices. He knew he couldn’t show all the information because it was just too complex, but it still needed to be interesting for people to explore.

  • 02:51 - His big breakthrough came when he decided not to show a many-to-many network relationship and ditch the traditional network layout, and instead try a radial representation where one chosen senator would be placed at the center, and the other senators would be placed around him or her based on how similar their voting records were.

  • 03:25 - So in his final visualization, Richie used Gramex to handle data & the user interface, and, D3.js for the visual representations.

  • 04:28 - Like I mentioned, some people seemed to really like this viz and some people really did not like it. The biggest complaint seemed to be that the position of the senators around the circle was random - Richie just randomly positioned them around the circle so they wouldn’t overlap.

  • 05:10 - Some people were a bit more harsh and found it difficult to comprehend the radial layout and thought that it was completely useless since the position around the circle didn’t mean anything.

  • 05:20 - But Richie took it all in stride and was glad that it sparked debate, and actually found it really insightful to hear how different people perceived the viz - some people found it really hard to read and some people found it really intuitive, which gives us insight into how people understand visualizations.

  • 05:33 - In the end, Richie was happy to have tried something new and achieved his goal of visualizing one interesting theme… he wasn’t trying to answer all the questions. But he did wrestle with creating a rich graphic that was still readable. It’s a tough balancing act that all data visualization designers have to contend with.

  • 05:55 - So how can you experiment with new visualization techniques and how can you prepare yourself for the inevitable critique?

  • 06:01 - I really loved watching Maarten Lambrecht’s OpenViz talk about Xenographics -  he created an entire website where he compiles new and strange chart types, and in his talk he gave some tips on how you can create your own xenographics. One tip was take your chart and just flip the axes, another tip is to crossbreed two different chart types. Pick two that show the info you want to convey and try to integrate them into one chart.

  • 06:35 - While I was on his xenographics site, I came across the chart type called the solar correlation map, which reminded me a lot of Richie’s viz. The idea is to use the solar system as a metaphor for a chart where you place a variable in the center and then place other variables at varying distances from the center determined by how correlated the variable is to the center variable. In the article introducing the solar correlation map by By Stefan Zapf and Christopher Kraushaar, they offer some tips on how to create a new visualization

      • Identify a problem in data analysis

      • Find an analytical tool that solves this problem

      • Use a visual metaphor to explore and communicate your results

  • 07:25 - I dive more into visual metaphors in episode 14 if you’re interested in hearing more about that.

  • 07:33 - So say you try out a new chart type and the critiques start rolling in… first, be prepared for it. There’s a reason why Maarten calls new charts xenographics - some people are scared of new charts and will automatically dislike it because it’s different. Second, keep in mind that creating something new is hard and most people completely underestimate the creativity and effort involved in it.

  • 08:00 - I think following Richie’s mindset is the best way forward following critiques - know that some people will find it difficult to understand because it’s new, but their critiques are still valuable even if they don’t seem to understand what your goal was, because it’ll give you insight into how people are making sense of your visualization, which will help you in the future.

  • 08:20 - My final takeaway is that we need people to experiment with new visualization techniques and chart types, and it’s tough being a pioneer - it takes creativity and effort, but it’s important to keep the data viz field moving forward. Just be ready to hear feedback on it, and try to take it as insight for your next viz. If you’re giving feedback, remember to critique respectfully.

  • 08:52 - Listen for Richie’s advice to designers just starting out!

  • 09:40 - You can follow him on twitter @richielionell and keep up with his work on Gramener's Story Labs

  • 09:45 - Did you know? You can sign up for my newsletter that I send out every Sunday with a quick recap of the top tips from the last episode to help commit it all to memory, or to give you the highlights in case you missed the episode. :)


Episode 35: [Mini] 3 Techniques to Handle Overplotting
 
Example of data that suffers from overplotting

Example of data that suffers from overplotting

 

Welcome to episode 35 of Data Viz Today. What should you do when you plot your data points and realize they're all on top of each other?? I recently learned that this is called "overplotting" and in this episode, I'll offer 3 techniques to help you handle this problem so you can get back to analyzing & visualizing!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

Example of overplotting, jitter plot, and gather plot from Gather Plots research paper

  • Welcome! I'm Alli Torban.

  • 00:30 - Today’s episode is about how to deal with overplotting. Overplotting is when you have a lot of data that overlaps each other in your chart. It’s difficult to see how much data there is and where it’s the most concentrated, which really hinders your analysis and obviously conveying your message visually.

  • 01:15 - When I finally figured out that this was called overplotting, I was able to find a lot of great resources, specifically this article by Stephen Few with lots of ideas.

  • 01:40 - So let’s talk a little more about what overplotting looks like and 3 solutions that you can test out next time you run up against this in your practice.

  • 01:46 - Overplotting is pretty common in scatter plots and line charts when you have a large dataset and/or many points are plotted on the same or similar values, or when you’re plotting the values of some points and your x-axis is plotting a discrete variable (like something where there’s a finite number of possible categories), so you’ll end up with a lot of points in the same place.

  • 02:36 - There are a couple of solutions that you’d probably think of immediately. Make the points or lines slightly transparent or decrease them in size. Try these as well:

  • 03:00 - First, you can try aggregating the data. Maybe you don’t need to see every point or line, so consider whether showing something like an average or median would work for your goal. Similarly, you can filter your data in certain ways and create a series of small multiples.

  • 03:35 - Second, you can try to convey where the density of your data is by adding a distribution chart on the margin of your scatter plot. So the actual data in the scatter plot stays the same, but there’s a distribution line on the side of the chart to convey where the points are the most dense. Similarly, you can create a contour plot which draws these kind of concentric circles underneath your data points and the circle centers around the densest areas and radiates out as it becomes less dense.

  • 04:22 - Third, you can add some jitter to your points. That’s when you slightly alter the value of points that are close together so they don’t overlap, or overlap less. The points end up kind of huddled together rather than obscuring each other. A similar solution that I found is called the gatherplot. I stumbled across a research paper by Niklas Elmqvist and others that introduced the gatherplot, and it’s kind of like adding jitter to your points in a scatter plot, but then ordering the points in a more meaningful way. Think of like you have all your gridlines on your scatterplot, and whichever points fall within one cell are then lined up in an orderly way rather than jittered all around or overlapping. So you get the benefit of jittering because the points aren’t overlapping, but it’s a little more organized so you can compare the size of the grouped points more easily. Plus if you’re coloring the points by some other variable, it makes it easier to compare the number of points of each color when they’re lined up and ordered within the cell, rather than jittered randomly.

  • 05:45 - My final takeaway is that the next time you have an overplotting problem, where there’s a lot of overlapping points in your chart, you can try

    • playing with transparency,

    • decrease the size of the points,

    • aggregate the data,

    • create small multiples with filtered data,

    • use a contour plot,

    • try adding jitter, or

    • using a gather plot.

  • 06:15 - And if you’ve been wanting to try creating data viz in Adobe Illustrator, they offer a 7 day free trial with no credit card required, and you can get going designing and editing charts quickly with my new course → Design Your First Visualization in Adobe Illustrator in Under 30 Minutes


Allison Torbanmini, overplotting
Episode 34: How to Harness the Power & Beauty of a Box Plot - Featured Data Visualization by Eric William Lin

Welcome to episode 34 of Data Viz Today. When's the last time you saw a box plot? How about the last time you created one?! It's been a long time for me, but this week's featured data visualization by Eric William Lin has convinced me to reconsider using this often clinical chart type as a beautiful and powerful way to tell a story. In this episode, we'll hear how Eric built his Kantar IIB Shortlisted viz, plus a few suggestions for how and when you could try a box plot!

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is all about the classic chart type - the box plot! Also known as the box and whisker plot. We’ll talk about the visualization that inspired me to reconsider the beauty and functionality of a box plot, how it was built, plus a few suggestions how and when YOU could try a box plot!

  • 01:02 - Today’s featured data viz is called Casting Shakespeare: How age, gender, and race affect casting by Eric William Lin

  • 01:10 - Eric is a musician-turned-software engineer based in New York City. He occasionally teaches classes in programming and has recently become obsessed with designing data visualization, which has led to this featured visualization showing up on the shortlist for the Kantar Information is Beautiful Awards! Public voting is open til the 19th so vote for this viz!

  • 01:40 - The spark that led to this shortlisted viz was actually from Shirley Wu - she was featured in episode 4 How to Find Answers in Survey Results. And last year, she gave a talk at a Javascript meetup in Brooklyn about her beautiful visualization of all the words in Hamilton the musical for The Pudding. Turns out that Eric was in the audience and having been a high school theater kid and music major, the thought of visualizing theater seemed like a really exciting way to combine his two loves - coding and theater.

  • 02:15 - He began brainstorming about which plays to focus on and what would be an interesting angle, which can be a big struggle like we talked about in the last episode.

  • 02:25 - But the first piece in the puzzle for Eric was that he remembered that the New York Philharmonic had open-sourced their performance history data, so while looking through the dataset, he decided to focus on Shakespeare plays, but with a twist - instead of focusing on the lines of text, he would focus on the characteristics of actors who have acted in those Shakespeare plays at over time. Like the age, gender and race of the actors.

  • 02:55 - So he began gathering data for that, and said this turned out to be the most difficult part of the project. All that information was scattered around on different sites, in different formats, or not available at all. He had to scrape a lot of data from production websites using python, and deduce some actors ages from an old article that referenced their age and compare it to the production date of the play.

  • 03:20 - But once he got everything that he needed, Eric was able to move onto the fun, creative part - visualizing the data. His first instinct was to create 2-dimensional scatter plot. The x-axis would the year of the production, and the y-axis would be the age of the actor at the time of production. Then do this same scatter plot for each character, and present it as a series of small multiples. But he quickly realized that showing the actors like this would make it hard to visually tell a story or a narrative about interesting patterns in the data…

  • 04:00 - His breakthrough moment was realizing that he could frame the story around the actor’s perspective. What if instead of looking at each character one-at-a-time and looking at how they were cast historically year-by-year, he could ask: As an actor, what roles are available to me at my current age? What roles should I audition for, and what characters would directors cast me in based on past data.

  • 04:40 - This led him to the box plot - he could show the distribution of ages for each character side-by-side, and another innovative benefit of using the boxplot - he could slowly reveal the boxplot to tell a story of an aging actor - like now you’re 30 years old, you’re probably not going to be cast as Romeo or Juliet because 75% of actors who played those roles were under 30.

  • 05:15 -Final visualization was built with JavaScript, D3.js, Aliza Aufrichtig’s Coordinator, and Susie Lu’s d3-annotations - you can hear more about that in episode 7 How to Annotate Like a Boss with Susie Lu!

  • 05:25 - Experience the viz here!

  • 07:40 - Eric showed us that box plots can be beautiful and aid in storytelling, but let’s get a quick refresher on what a box plot is, and then we’ll talk about some pros/cons, and some variations.

  • 07:53 - A boxplot is a standardized way of showing the distribution of data. It gives you a quick way to see how your data points are spread out. If someone told you the median of a dataset, you don’t know if most of the points are clumped around that value, or if they’re spread out.

  • 08:13 - In a box plot, there’s a specific mark to show the median, the lower and upper quartiles, the upper and lower fences, and any outliers. Listen for a more detailed description of how to build one. Check out Nathan Yau’s extremely helpful blog post about how to read a box plot:

  • 09:55 - Pros: you can garner a lot of information about the distribution by these couple of marks, and they don’t take up a lot of room, so if you try show distribution with a histogram or a density plot, then it’s harder to put them all side by side and compare. But it’s easy to stack up box plots into one chart and compare distributions among various groups.

  • 10:25 - Cons: The benefit of something like a histogram, is that you can see more detail. The box plot is using summary statistics, so you don’t have any control over the granularity, like you would with a histogram by varying the bin size. It also hides the sample size, so you might compare groups with separate box plots, but it could be a little misleading if your sample size for each group varies widely. You could annotate it, or I like what Eric did by actually showing the points with slightly transparent dots behind the box plot. The box plot is also less intuitive for some people, but you could mitigate that by doing what Eric did and show a How to Read chart beforehand.

  • 11:25 - Check out box plot variations from Data Viz Catalogue!

  • 12:04 - Box plots in the wild:

    • New York Times - they showed projected career earnings for college graduates, and they had a box plot for each major and you could see the median and the spread of the projected earnings in dollars for each one.

    • FiveThirtyEight showed the median and spread of yelp reviews for restaurants with different Michelin Stars. I liked that they have an inset box that explains what the box plot shows.

  • 12:50 - Tools that make box plots: Tableau, RAWGraphs, Excel...

  • 13:20 - My final takeaway is that next time you’re visualizing the distribution of points and also want to compare distributions across many groups, consider using a box plot. It’s a clean way to show distributions, and you can experiment with different variations to show more detail, and even use it as a storytelling tool like Eric did! Just make sure your audience understands how to read it because it could’ve been a minute since they learned about box plots in math class.

  • 13:30 - Listen for Eric’s amazing advice to designers just starting out!

  • 14:55 - You can follow him on Twitter, and check out his website.

  • 15:05 - I'm sharing my essential Adobe Illustrator tips in my new course! Check if it's right for you HERE!


Allison Torbanlin, boxplot
Episode 33: [Mini] How to Discover Relevant Stories in Your Data by Taking an Editor’s Perspective
 
 

Welcome to episode 33 of Data Viz Today. How can you consistently generate interesting visual story ideas from your data set? I’ve been on a quest to find a process for this, and I recently found guidance in a book for authors who are trying to get their non-fiction short stories published by editors. I used what I learned to create a worksheet that brings me from a basic stat to eight story ideas! In this episode, you’ll learn about the reasoning behind it and hear it in action. DOWNLOAD THE WORKSHEET

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 01:34 - In this episode, I’ll share the 3 things I learned that editors look for when they publish non-fiction short stories, how that led me to creating a data viz workflow diagram that takes me from one basic stat to eight relevant story angles, and of course I’ll show it in action with an example!

  • 01:53 - If you’re ready to create fully customizable charts in Adobe Illustrator, check out my new course → Design Your First Visualization in Adobe Illustrator in Under 30 Minutes

  • 02:43 - The book I was reading is called The Byline Bible by the writing professor Susan Shapiro. It’s a guide for authors of non-fiction short stories on how to get published in magazines and newspapers. I saw so many parallels between her advice to writing relevant short stories that will get an editor to publish you, to creating a visualization out of a data set that’s going to mean something to someone.

  • 03:20 - So from the book, there were three pieces of advice for getting published that I thought were super relevant to finding a story in data.

1. Avoid the obvious. You want to focus on drama, conflict, and tension. Susan Shapiro says in her book: “confront unresolved emotional issues about something that’s bothering you.” What’s the use in visualizing something that everyone already knows?

2. Make it timely. You need to compel your reader with a fresh angle or a reason why now is the time to take notice.

3. Clarify your emotional arc. Susan says “start in delight, end in wisdom” - you want to start strong, introduce conflict, and have a resolution. For data viz, the start strong part I think is wrapped up in the visuals - to varying degrees you’ll use your design to catch someone’s eye, then your angle on your data will introduce the conflict and possibly resolution, depending on whether it’s exploratory or explanatory.

  • 04:25 - Ok, then I took these three elements and made a workflow diagram out of it to use before I do any analysis to get me warmed up and ideally take me from one statistic or fact, and turn it into 8 possible interesting angles to pursue.

  • 04:51 - First, write your stat or fact at the top. Then we move into conflict: If that stat is true, then what’s the consequence? Who is affected? Then tackle the timeliness of each consequence: Why is this important now? If it’s not, what can I compare it to that is important now? Then think about possible resolutions: What can help? What action can we take?

  • 06:10 - Listen for my example using the Makeover Monday dataset on avocados!

  • 08:44 - My final takeaway is that you can take an editor’s viewpoint, and squeeze interesting angles out of your dataset so that your visualizations are telling a compelling story. So try out this workflow, and let me know if it’s helpful to you! Remember you’re looking for Conflict, Timeliness, Resolution.


Allison Torbanmini, editor
Episode 32: How to Add Impact & Inform Your Reader by Handing Over the Power - Featured Data Visualization by Ludovic Tavernier

Welcome to episode 32 of Data Viz Today. How can you add interaction to make your story more impactful and memorable? In this episode, host Alli Torban explores specific interaction techniques that you can try in your visualizations to more effectively inform your reader. Featured data visualization project by Ludovic Tavernier perfectly shows how handing over the power to your reader can create an engaging experience.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:21 - Today’s episode is all about giving your reader ways to interact with your visualizations so that your story is more impactful and memorable.

  • 00:30 - In this episode, we’ll talk about the visualization that inspired this, how it was built, and 3 specific interaction techniques that you can try in your visualizations to more effectively inform and teach your reader.

  • 00:45 - First I have a quick announcement - many of you have asked me for resources on how to get started making visualizations in Adobe Illustrator, so I finally made my own resource! I just launched a new course called Design Your First Visualization in Adobe Illustrator in under 30 minutes, which has 6 short videos that walk you through everything you need to know to start creating and customizing charts in there. This is basically the course that I would have wanted to take a year ago when I started learning - after hearing almost everyone that I interviewed say they used Illustrator for design and annotations. So I’m excited to provide this shortcut to you now. you can enroll at dataviztoday.com/courses and use the coupon code PODCAST for 25% off until October 10th.

  • 01:35 - Today’s featured data viz is called “The Amazing Letter E” by Ludovic Tavernier. Ludovic is the Data Visualization Lead of Valoway, a french consultancy firm that specializes in the data sector. He recently won the first feeder competition of the Tableau Iron Viz Contest with this visualization that I’m featuring today.

  • 01:55 - This is actually the second featured viz that came out of an Iron Viz competition  - the first one was in episode 25.

  • 02:50 - He studied past winners and saw that he needed to really focus on 3 elements that are really important to the judges: analysis, storytelling, design.

  • 03:13 - He finally settled on telling the story about the letter E.

  • 03:30 - Then he began sketching how he’d lay out this story in his viz. He also wanted to add a mix of simple and complex charts. Some basic charts to reassure most readers, and some complex ones to engage the experienced readers.

  • 04:04 - When he had sketched out the sections of his viz and chose his chart types, he started creating it in Tableau. The data he used was text from various books around the web. I was surprised to learn that his data table has 10 rows and 4 columns (title, author, year and excerpts). Then he let Tableau to do all the hard work with formulas. For example, he took the excerpt from the book and had tableau divide it into letters and then added calculations to add up the frequencies of the letter E in each one.  

  • 04:52 - And the beautiful thing about Tableau Public and the Tableau community, is that most people make their dashboards downloadable, which Ludovic did, so you can download the actual Tableau file and dig into how he did it so you can recreate it too.

  • 08:09 - So the Tableau wizardy aside, I think the true genius in Ludovic’s viz was that he told the story of the letter E by giving us, the reader, the power to control the story, which to me, has 3 big benefits.

  • 08:25 - The first instance, where Ludovic lets you slide the bar to reveal the line chart so you can see the letter E’s frequency stabilize around 33%, that gave me the power to reveal the chart at my own pace and it almost made me feel like I was drawing the chart, which makes it more memorable.

  • 08:53 - The second example where Ludovic lets you choose a book from his bookshelf to explore or when he lets you enter your own text at the end, that gives you the power to choose what you’re most curious about. And when you let someone explore, guided by their own curiosity, they’re more engaged and have a more memorable experience, like we talked about in Episode 6.

  • 09:20 - The third example is when Ludovic lets you walk through building his complex circular connection yarn-ball viz, word by word. He took a complex viz and gave me the control to build it slowly so I could understand it at my own pace. Everyone learns at a different pace, and letting me walk through it allows me to truly understand what’s going on and appreciate the viz that much more.

  • 10:00 - My final takeaway is that you can really engage your reader and teach them something they’ll remember by giving them control over the narrative.

    • You can do this by allowing them to reveal or draw a chart themselves,

    • give them a choice of the example they explore, or

    • allow them to walk through and build up a complex viz at their own pace.

  • 10:18 - Benjamin Franklin said “Tell me and I forget. Teach me and I remember. Involve me and I learn.”

  • 10:27 - Think about how you can add interaction so you can add impact & inform your reader.

  • 10:35 - Ludovic’s advice to designers just starting out: “Just practice, don’t be afraid to show your work. We all start from somewhere!”

  • 10:46 - You can follow him on twitter @ltavernier7

  • 10:55 - And don’t forget to use coupon code PODCAST by October 10th in order to get 25% off of my Adobe Illustrator course!


Episode 31: How to Decide If Your Visualization Should Be 3D - Featured Data Visualization by Ryan Baumann

Welcome to episode 31 of Data Viz Today. When is it beneficial to visualize your data in 3D? We know that people love the “cool” factor of 3D, but I think most people know to avoid it now. But in which cases could it be useful in understanding your data? In this episode, host Alli Torban explores the pros and cons of 3D viz. Featured data visualization project by Ryan Baumann shows a beautiful way to make custom 3D visualizations of spatial athletic data.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:28 - Today’s episode is about how to decide if your visualization should be in 3D. When does 3D benefit your viewer?

  • 01:00 - In this episode, we’ll talk about the visualization that sparked my curiosity about the third dimension, how it was built, and what to consider when you’re tempted to make your dataset 3D.

  • 01:14 - Today’s featured data viz project is a book called “Athlete Data Viz” by Ryan Baumann. He’s a former pro-cyclist and an engineer at Mapbox where he collaborates with customers to develop new products. His side project is a data visualization tool for athletes called Athlete Data Viz, which is a website that Ryan created where anyone can visualize their athletics or training in a custom way on a map.

  • 03:03 - So how did Ryan get into this cool little side hustle? As an athlete, he’s always loved tracking and visualizing his performance on his bike rides. One day in 2014, a post on the Strava subreddit caught his eye… someone named Kevin was trying to build a heatmap of his Dad’s rides for Father’s Day. Ryan loved this idea and built out a proof of concept of how it could be done and reached out to Kevin to ask if he could help him.

  • 03:45 - How? He uses the Strava API along with Mapbox data and custom styles to gather the data for the app. A user logs into their Strava account and can pull their latest ride to visualize over different map styles. Listen for more of the technical aspects explained!

  • 05:30 - He really wanted to make something that felt more like art than business intelligence, and give people the joy of commemorating a great race or experience by creating something beautiful and unique.

  • 05:45 - Tools used: Ryan used Mapbox GL JS, javascript, Postgres + PostGIS extension, Python Flask for the backend API, and Heroku for the platform.

  • 06:08 - If you want to try this out and make a beautiful map of your athletic data, you can head over to athletedataviz.com.

  • 06:16 - I liked Ryan’s idea of making paths 3D especially when the height of the path is determined by elevation. That feels like a natural and helpful use of 3D to me.

  • 06:30 - But then I began to wonder what is it exactly that makes 3D visualizations a bad choice in most situations?

  • 06:50 - I’m currently reading Tamara Munzner’s Visualization Analysis and Design and she has a section is called “No Unjustified 3D”, which I found refreshing because it wasn’t just “Never 3D”, but a carefully laid out section about the pros and cons. I like the idea of keeping 3D in my toolbox as long as I also have the knowledge of its strengths and limitations.

  • 07:20 - My two big takeaways from Tamara’s analysis is that 3 dimensional visualizations

    • 1 - have the problem of occlusion and

    • 2 - have the problem of perspective distortion.

  • 07:30 - For #1, occlusion means to obstruct, so when you show data in 3D, some data might be obstructed by other data, which can be helped by making the visualization interactive, but this comes at the cost of the viewer’s time and cognitive load, since the viewer is having to construct a mental model by remembering past views, which is especially hard with abstract data.

  • 08:00 - And for #2, the perspective distortion problem, when you plot data in 3 dimensions, the objects that are further away appear smaller, which is a problem when you’re trying to compare the size or length of objects.

  • 08:15 - So you want to keep your data in 2D when relative position is important - when you need to precisely judge the distance or angle between objects.

  • 08:25 - A time when 3D is easier to justify is when the viewer’s task involves shape understanding of inherently 3D structures, which is usually spatial data.

  • 08:45 - This made me curious about what people are using 3D models for in the real world, so I asked my sisters, who are both engineers, whether they ever visualize 3 dimensional data, and one said she does because she runs simulations of air or water flow around an object so if she graphed it in 2D, she’d miss out on what’s going on on the other side, especially when the object isn’t symmetrical.

  • 09:15 - It was a similar case for my other sister who said she uses 3D models to see how atoms interact with magnetic fields and lasers, so seeing the full picture in all 3 dimensions is essential.

  • 09:27 - This is actually similar to the example that Tamara used in her book to show a well-justified use of 3D. She showed a visualization of streamlines of fluid flow through a volume. In these cases, you’re trying to understand the shape of data in a 3D space.

Example of 3D visualization of abstract data that works well. From Tamara Munzner’s book Visualization Analysis & Design.

  • 09:44 - When you try to apply 3D to abstract data though, it gets a lot trickier to justify.

  • 09:55 - Tamara had one great example in her book where she shows a visualization of voltage readings over time, but in the 2D view, all the readings are graphed over each other so it looks like all the readings are about the same, but this interactive viz allows you to kind of pull it out like a drawer and expand all the readings out like an accordian, revealing this 3D view that shows that half the readings actually look different than in the 2D view, but since so many readings were on top of each other, the fact that the shape of the readings was changing over time was obstructed.

  • 10:35 - The important benefit of 3D was the shape of the data as time progressed. You could see how the data changed in a way that wasn’t clear in the 2D view.

  • 11:02 - Cons of 3D:

      • 1 - When you extrude objects, you might block some data behind it - called occlusion.

      • 2- You introduce perspective distortion and it becomes difficult to compare lengths and angles accurately.

  • 11:17 - Pros of 3D:

      • 1 - You can quickly understand shape.

      • 2 - Helpful when the viewer needs to understand the 3D geometric structure of objects or a scene.

  • 12:00 - If you have a use case for extruding buildings or other shapes on a map, I actually just made a series of short video tutorials for Mapbox on how to do this.

  • 12:15 - My final takeaway is that I think Tamara Munzner summed it up perfectly in her book by naming the section “No unjustified 3D”. Not never 3D, but take note its limitations and keep it in your toolbox.

  • 12:35 - Finally, I asked Ryan what’s his advice to designers just starting out, and he said as a beginner using color in your visualizations, it’s too easy to tell a story you don’t intend or that even misrepresents your data. So keep your colors simple - start with well known designer palettes for data visualizations that you can find in tools like colorbrewer.

  • 13:00 - You can follow him on twitter @RyanBaumann and head over to athletedataviz.com to make your own visualization of your training data!

  • 13:12 - I’m finishing up another course, this one is on creating and editing visualizations in Adobe Illustrator, so if that’s something that you’re interested in learning, you can sign up for my newsletter to get notified of its launch and maybe a coupon code too… :)


Allison Torban3d, baumann