Episode 30: [Mini] How to Use Help Desk Tactics to Build More Useful Visualizations

Welcome to episode 30 of Data Viz Today. Do you ever have a client that's not sure what they need, and you also feel at a loss on how to visualize their goal in the most useful way? In this episode, I talk about how I'm bringing back my old Help Desk skills to break through that wall (or vizzer's block ;D) and dig up useful dashboard ideas.

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

  • Welcome! I'm Alli Torban.

  • 00:30 - Today’s topic is about breaking through that wall that you hit when you’re trying to create a data viz for a client but they don’t really know what they want but you also feel at a loss on what to do to help.

  • 00:45 I had a low-stakes version of this this past weekend, where I asked my husband if he wanted me to create a dashboard for his new workout plan so he could track his progress but he said he didn’t think it’d be helpful in this case. I knew there was probably something that I could do, but didn’t know exactly what…I realized that I could use my experience on a Help Desk to breakthrough that wall.

  • 01:15 - I was on this small Help Desk team at the Pentagon a while ago, where I helped people who used our custom software learn how to use it and I’d also answer calls to troubleshoot any issues they had. If you’ve never been on a Help Desk, let me tell you what it’s like...It’s like trying to drink from a firehouse, and instead of water, it’s spraying lots and lots problems. People only call you when their work’s been interrupted, they’ve wrestled with the software and have now become so frustrated that they dig up your number to get the solution ASAP. When people call, they have a wide range of abilities in terms of how well they can communicate what they’re experiencing and where their problem is. It was really frustrating when I first started because I’d just take the information someone gave me, hung up and I’d run with it, usually spinning my wheels. But as I got more experienced with the software and how to deal with people, I started to know which questions to ask to get to the problem quickly, and also get to the solution quickly.

  • 02:30 - I’m sure as I’m saying all this, you can easily draw the parallels between being on a Help Desk and consulting with a client about their data viz project. So I wanted to share a few key questions that I found useful while on a Help Desk, and then show you how I used these questions on my husband to go from nothing to lots of dashboard ideas.

  • 03:03 - So my husband started this new workout routine last week called 5x5 where you do 5 sets, 5 reps each of some exercise with a certain amount of weight, and then each week you increase the weight. The idea being after like 2 months you’ve significantly increased the weight you’re able to lift. I’ve created dashboards in the past for him so he can track his progress on new workouts, so I asked him if he wanted one for this endeavor…but he said he didn’t think he needed one because he’s just increasing the weight the same amount each week so there’s not really much to track. And I thought he had a good point, and we left it at that. But I kept thinking that there had to be something that would be useful to track to inform him about his progress, and then I had that thought about being on a Help Desk…there are techniques that I know that can help me get more information from someone with a goal.

  • 04:04 - So I convinced my husband to let me ask him questions to see if I could break through this wall and see if I could create a dashboard that would help helpful…

  • 04:15 - First thing I did when someone called the Help Desk is ask what they were trying to do when they got their error. I want to start with their goal so I know where we’re going.

  • 04:30 - Then, second, I want zoom out to get as much context as possible to see what they’re seeing. Start as zoomed out as possible and zoom in. People usually want to just tell you the zoomed in issue (“document 126 is stuck”)...but if you start chasing document 126, you might realize 3 hours later that you’re in a different application than they’re in. So zoom out and get more details...What browser are you using? Which application are you in? What type of document is this? What were you doing with it when the error occurred? What exactly did the error message say? I want to feel like I’m in that person’s seat, using the same application, trying to do the same task and achieve the same goal. So I know their goal, and I know the context around the situation, so I now I have enough information to go try to investigate behind the scenes

  • 05:30 - The third part is that I’d recreate the issue in the test environment, see if I got the same error, then I’d start testing some hypotheses on why this error is happening… if I change this, can I get around the error and achieve the goal? What about this? I’m trying to hone in on the things that introduce problems and throw the goal off. Then I can tweak whatever I need to and report back to the user with the solution.

  • 05:50 - The most important questions to ask to solve a help desk issue efficiently are

1. What’s your goal?

2. What’re you seeing?

3. What’s causing the problem?

Goal, context, cause

  • 06:10 - So back to the workout …. I asked my husband these questions about his new workout plan.

1. What’s your goal? He said to get stronger. Each week increase the weight he can lift by a certain number each week over 8 weeks.

2. What’re you seeing? What’s the context? What are the things you can measure that’s around this goal? He said measurable things around this goal are the amount of weight and number of reps he completes at each workout, and whether he needs to repeat the workout because he couldn’t complete the last one.

3. What’s causing the problem? What’re some measurable things that could throw the goal off? He said things that might make him not be able to finish a workout at a certain weight is his protein intake, or the type of workout he did the day before.

  • 07:05 - After gathering all these answers, I went from a shoulder shrug and “there’s not really any useful way to visualize progress for this” to a bunch of ideas of things that I could visualize and build into an interactive dashboard that he could use to track his workouts - amount of weight use, workouts completed or repeated, protein intake, off-day workouts…The idea being that by tracking all those things, he can keep close tabs on where he is along the path of achieving his goal, and start to see patterns around what’s affecting his progress.

  • 07:40 - It was a really fun to try this in a no-stakes situation so I could kind of flesh out this idea… and I look forward to trying to work through these questions the next time I feel like I’m hitting a data viz wall with a client...

  • 07:50 - My final takeaway is that when you’re trying to build a data viz that will be useful to your client but you’re feeling stuck, try getting in the Help Desk mindset to uncover the metrics that are meaningful to the goal.

    1. What’s your goal? How can it be measured?

    2. What’re you seeing? What’s the context? What are the things you can measure that’s around this goal?

    3. What’s causing the problem? What’re some measurable things that could throw the goal off?

    4. Goal, context, cause

  • 08:21 - A bonus technique that’s helpful on a Help Desk AND when building data viz for clients is make it a priority to build trust. When you have a trusting relationship, those questions go a lot smoother. So make sure to really listen and leave your ego at the door.

  • 08:50 - If you’ve been enjoying the show, it would mean a lot to me if you could leave me a review in iTunes! :)

Allison Torbanmini, help desk
Episode 29: 3 Essential Steps To Finding Your Unique Style - Featured Data Visualization by Federica Fragapane

Welcome to episode 29 of Data Viz Today. How can you find your unique data viz style? I've started my quest to find mine, which I hope will help me find my voice and create work that’s more representative of my point of view. I know it’s not something that happens overnight, but what can I do to get started? Featured data visualization project by Federica Fragapane provides plenty of inspiration for how to get on the right path.

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

  • Welcome! I'm Alli Torban.

  • 00:22 - Today’s episode is about how to find your unique style. How can I take a step toward defining my own style and unique data viz point of view?

  • 01:00 - Today’s featured data viz project is a book called “Planet Earth” illustrated by Federica Fragapane. Federica is an Italian award winning freelance information designer.

  • 02:15 - So how does someone get into designing a kids book? Federica had started to experiment with combining data visualization and illustrations, and a publisher had seen some of her projects and approached her for a collaboration. They were looking for someone to design a whole children’s book combining data visualizations and illustrations, which fit exactly with her experimentations.

  • 02:30 - She collaborated with Chiara Piroddi, who is a psychologist and helped Federica design the infographics with children in mind. Some tips for designing data viz for kids: create a familiar connection with the shapes - have certain shapes and colors repeat often, and create legends in many places so they have all the information they need to understand the visualizations.

  • 04:00 - What was Federica’s design inspiration for this book? She told me that she wanted to create a joyful connection between the pages and the readers, so she actually went back and flipped through her own kids books that she read as a child. She still had them, and it helped her recall colors, shapes, and details that really brought out positive feelings for her when she was a kid. So she used those positive feelings and the visual elements that conjured them up as your starting point and worked from there to develop the style of the book. Federica looked at illustrations from her audience’s point of view, but she even took it a step further and sought out illustrations were meaningful when she was the intended audience.

  • 05:06 - Federica used Adobe Illustrator for the visualizations and Photoshop for coloring her hand-drawn illustrations.

  • 05:20 - Get the book on Amazon!

  • 05:50 - I love how Federica has a really unique style, and it inspired me to start defining my own personal style. How can I get to the point where people see my data viz and instantly know that’s from me?

  • 06:10 - Reminded me of data viz style guides used at companies. Check out Jon Schwabish’s curated list of style guides from around the world.

  • 07:30 - The thing about style guides is that they’re built with the company’s brand in mind, but also with their audience in mind. What color complexities and formats work best for their audience. Just like Federica does - she uses her audience as a starting point for her design inspiration.

  • 07:53 - I thought this was a perfect first step to defining my own style - get in the mindset of your audience.

  • 08:20 - Let’s build this out in 3 actionable steps…

    • #1 - Who is my audience? Who am I designing visualizations for? Is there a Style Guide in my organization? List out the “cracks” in the style guide where you can inject your own style. There might be certain colors and fonts that I have to use, but maybe font size and line style are free game. Or I use certain patterns and strokes to highlight certain areas that would look unique. Maybe there are certain techniques that I could use like we talked about in episode 27 about Edward Tufte’s book where he suggested some techniques for erasing non-data ink like the range frame. Federica uses a lot of circles, curved lines, small multiples, and plays with opacity, shading and layering… all things that give her a unique style that she’d probably be able to bring with her into many situations.

    • #2 - Build inspiration boards of designs that you catch your eye. Like color palettes, shades, fonts, spacing, lines styles, and chart techniques. Federica told me that she’s constantly looking for visual inspiration, even if she doesn’t have a specific project in mind. She’s learned that her eyes are attracted to certain shapes, colors and elements. She seeks out the visual elements that give her positive feelings and works on incorporating them into her work so that she can recreate that joy. So try scrolling through pinterest and pin the images (data viz or not) that make your eyes light up and bring you joy. Keep an eye out for different color palettes, shading, shapes, lines, corners, edges, spacing. All those little things….If you missed episode 26 that’s a great one to help you zone in on the tiny, specific elements of great design.

    • #3 - Embrace your evolution. Your style is going to change over time, and it’ll probably need to change from project to project depending on your audience, so don’t hold too tightly and just keep experimenting. So I’m just going to add anything and everything, knowing that my style is going to evolve over time.

  • 13:45 - My final takeaway is that the 3 essentials steps that you need to take in order to define your own personal data viz style are

    1 - Define the parameters around what your company or audience needs, and then identify which design elements are free for you to play with. Even with strict style guides, I bet you can find some cracks.

    2 - Start with one image that really brings you joy or you wish had your name on, and search Pinterest for similar image. Build a board of inspiration with color palettes, shading, shapes, lines, corners, edges, or spacing that you like.

    3 - Keep in mind and embrace that your inspiration is going to change and evolve over time and with each project so go with it and keep experimenting and refining.

  • 14:38 - Eventually we’ll turn the corner and create work that people can immediately identify as ours… just like the beautiful work of Federica.

  • 15:15 - You can keep up with all her work on Behance and on Twitter

  • 15:30 - Check out my Resources page for links to all my favorite books, blogs and tools!

Episode 28: How to Build a Connection With Your Data Through Original Visualization - Featured Data Visualization by Sonja Kuijpers

Welcome to episode 28 of Data Viz Today. Is it ever beneficial to stray from the usual chart types and create your own original, novel data visualization? i.e. A viz where you decide what each free-form shape, line, and color represents. In this episode, host Alli Torban explores how this technique can lead to a deeper connection with your data. Featured data visualization by Sonja Kuijpers perfectly illustrates how creating an original visualization can turn overwhelm into clarity.

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

  • Welcome! I'm Alli Torban.

  • 00:30 - Today’s episode is about how to build a connection with your data by creating original visualizations. And by original, I mean something that’s out of the typical chart type (bar chart, line chart, scatter plot), where you decide what each shape, line, and color represents and build a visualization from it that represents some dataset that you have.

  • 01:15 - Today’s featured data viz project is called “Keuzestress” by Sonja Kuijpers

  • 01:20 - Sonja is an information graphics designer who runs her own company called Studio Terp based in the Netherlands.

  • 01:35 - Her viz Keuzestress was a personal project of hers, translated from Dutch it means Choice Stress.

  • 01:45 - Sonja was searching for a mascara that fit her needs, but soon found out that the there’s an overwhelming number of decisions that you need to make in order to choose a mascara - add length, add volume, add curl, or how about all three? How can she pick one?

  • 02:10 - Well, since she’s an information designer, she decided to create a viz out of all the information.

  • 02:20 - She scraped the data on all the mascaras that a Dutch makeup webshop supplied using Parsehub, which is a free web-scraping tool. And she cleaned it up in Excel.

  • 02:50 - So she took the characteristics that she wanted to visualize and started drawing some forms that she felt fit for that characteristics, like a black circle to represent a black mascara and a thick grey ring to represent adding volume, and she put all the little shapes that she came up with and put them on top of each other and then she realized that all the shapes together actually looked kind of like an “eye”.

  • 04:00 - Then she was finally able to give her mascara anxiety a little bit of order and put each one in its place amongst the others. She identified the couple of specifications that she wanted and was able to put her finger on the exact one she wanted. The final viz was created using Adobe Illustrator.

  • 04:55 - By putting the mascara choices into a custom-styled data viz, she was able to take something that was giving her anxiety and turn it into something more tangible that she could sort, order, and connect with.

  • 05:35 - Sonja’s project reminded me Giorgia Lupi and Stefanie Posavec’s Dear Data project where they hand-drew visualizations of little things in their life like how many times they checked the time during the day.

  • 05:50 - Giorgia has a wonderful TED talk where she talks about this project and how when she explored her reality and visualized it with these hand-drawn visualizations, she was able to transform the abstract and uncountable into something that can be seen and felt, and it helped her feel more connected to her life. By handcrafting visualizations of information, she tries to re-connect numbers to what they stand for: stories, people, ideas. What she called data humanism.

  • 07:40 - My inspired viz used the data visualization survey results from Elijah Meeks.

  • 08:15 - Goal: feel a connection to my fellow data vizzers, specifically other women in the field. Listen for my process!

  • 10:00 - It was a really tedious process arranging every single shape to put together each of the 142 women who took the survey. But it was also really cool because it allowed me to feel really connected to each one, like I found my heart and I could see the women next to me who are similar age, similar experience, does she have a STEM major, was she self-taught, is she interested in learning more design?

"The Women of Data Viz" by Alli Torban 

  • 10:40 - My final takeaway is that by creating a custom, free-form visualization of your data, you can create something that’s not only beautiful and engaging, but also something that helps you connect with your data - like in my women in data viz project or help you quantify something that feels overwhelming to you like in Sonja’s project. If we can visualize data in an unrestricted way, it can open us up to appreciating our imperfect and intricate realities in a beautiful and meaningful way. So try freeing yourself of chart types, and see if you can connect with your data through this visualization technique.

  • 11:45 - Finally, I asked Sonja what’s her advice to designers just starting out, and she said “Ask. Don’t be afraid to ask! The dataviz community is a warm one (in my experience) and you can reach out quite easy on Twitter (where most are active) to ask for advice.”

  • You can keep up with all her work on her website and follow her on Twitter!

Artboard 1How to read# years doingdata vizSTEMmajormostlyself-taughtwants to spend moretime on data vizwants to get better atdesigndataage25 & under26 - 3536 - 4546 - 5556 & upNumbersmedian # of years doing data viz457%age 26 - 3549%STEM major77%mostly self-taught76%want to spend more time visualizing data49%32%aspire to be better at designaspire to be better at data

Episode 27: [Mini] 3 Things I’ll Try To Do Better After Reading Tufte’s Book

Welcome to episode 27 of Data Viz Today. I loved reading Edward Tufte's “The Visual Display of Quantitative Information” for the first time as part of the Data Vis Book Club! In this episode, I talk about the 3 things that I'll try to do better in my data viz now that I've read Tufte's book. What about you?

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

  • Welcome! I'm Alli Torban.

  • 00:30 - I just finished “The Visual Display of Quantitative Information” by Edward Tufte as part of the Data Vis Book Club, and I wanted to share the 3 things that I’m going to try to do better after reading this Tufte book.

  • 01:40 - #1 First thing I want to try to better is to refocus on showing the data. The proportions, the variation, the context. It’s easy to get caught up in the design and trying this or that color or technique, but throughout the book, Tufte always comes back to his main point - focus on showing the data clearly - show data variation, not design variation. Try to erase as much non-data ink as possible, that’s the ink you use to print things that are not essential to the data information. You want to strive toward erasing as much non-data ink as you can. It’s a really valuable perspective to think in terms of data ink and non-data ink, and assess what’s truly important to communicating your data and insights.

  • 02:40 - #2 The second thing is that Tufte came up with some interesting techniques in support of minimizing non-data ink, which I want to try:

 White grid lines through data.

White grid lines through data.

One technique is erasing as many gridlines as possible and if some gridlines are necessary, instead of using black lines in the background, you could use white lines that just go through the data so it gives you a way to visually break up segments without adding any non-data ink.

 Range frame.

Range frame.

Another technique he calls the range frame, which is where you draw your x or y-axis, but only in the range where you have data. Like if you have a scatter plot that has data only between the values of 10 and 15 on the x-axis, then only draw the x-axis line in the range between 10 to 15 not like 0 to 15 like you normally would. It might be a nice minimal way to show the axes, and the reader can quickly see the range of the data points.

The book has lots of other little techniques like that helped to get me thinking about creative ways to reduce non-data ink.

  • 03:55 - #3 Third thing I want to try to do better, is not rely on color so much

He has a great section about not turning your data viz into a puzzle. He says that a sure sign that you created a puzzle is if the graphic has to be interpreted through a verbal process, rather than a visual process. Like if your reader has to rely on reading a legend and repeating phrases to themselves to decode what you’re showing, then you probably created a puzzle.

Tufte notes that often color creates graphical puzzles because we attempt to give color an order and assign it meaning, which means someone needs to decode it, so it can very easily get complex. So instead, think about using shades of grey because it has a natural visual hierarchy so there’s less decoding that needs to happen.

I think I rely on color a lot and go straight to it to start using it to encode attributes of my data, but Tufte makes a great case to keep things simple and consider whether you’re making a puzzle out of your graphics, and oftentimes color can be the culprit.

  • 05:00 - My final takeaway is that reading Tufte’s book “The Visual Display of Quantitative Information” gave me a refreshed perspective on data viz and was a great reminder to keep things simple and show the data, erase as much non-data ink as possible, and can be creative in the ways that you do this by not just erasing but using different techniques, and finally be weary of creating a graphical puzzle especially with color. You want to show data proportions, variations and context so your reader can gain insights, and as Tufte says, graphical elegance is often found in simplicity of design and complexity of data.

  • 05:45 - I highly recommend reading his book, not necessarily as a data viz rulebook, but as a valuable perspective to consider.

  • 05:55 - Did you read the book? What was your impression of it? Let me know on Twitter or Instagram.

  • 06:05 - I put together a Resources page with my favorite books, blogs and tools!

Allison Torbanmini, tufte
Episode 26: How to Develop Your Design Eye & Transform Your Work - Featured Data Visualization by Jane Pong
 Jane Pong

Jane Pong


Welcome to episode 26 of Data Viz Today. Being able to see the difference between well-designed and poorly-designed data viz is half the battle! But when your work always looks amateurish to you, it can be really frustrating. In this episode, host Alli Torban identifies specific ways that you can close the gap between your good taste and your developing skills. Featured data visualization by Jane Pong perfectly illustrates how dense data can still be designed in a clean and engaging way, and I take notes from her to remake my viz from a past episode!

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

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is about how to cultivate your design eye. I think before you can design well, you need to be able to see the difference between good and bad design. It comes more naturally to some people than others, but that doesn’t mean it’s not learnable! This is important because by training your design eye, you can elevate your taste and know what it takes to create even more beautiful and functional data viz.

  • 01:45 - In this episode, we’ll talk about the viz that inspired this, how it was built, and my 3-step process that I’m going to call the “IRA method” that I plan to execute every time I come across a data viz that I love so that I never waste an opportunity to further cultivate my design eye.

  • 02:09 - Today’s featured data viz project is called “Rain Patterns” by Jane Pong. Jane is a data visualisation designer based in Hong Kong.

  • 02:20 - I came across Jane’s viz because she was interviewed by Jon Schwabish from the PolicyViz podcast.

  • 02:55 - Her viz was exactly what I tried to do in my inspired viz in episode 23 where I wanted to show daily rainfall in D.C. over the past decades, except Jane did it for a Chinese newspaper back in 2013 and her design was so beautiful.

  • 03:15 - Jane created this viz because Hong Kong was heading into the monsoon season and she was curious to see whether it occurred at the same time every year, and when the typhoons were happening. The daily rainfall and cyclone warning data is from the Hong Kong Observatory.

  • 03:40 - Design inspiration: She knew she wanted to create a bar graph showing daily rainfall, but wanted to invert the y-axis so the bars looked like they were falling down from rather than rising up out of the axis. She thought it’d be a really nice visual metaphor for rain falling, which was inspired by Simon Scarr’s viz on the Iraq War where he showed the number of fatalities in red as if it was blood dripping down.

  • 04:18 - Jane said the hardest part of creating this viz is that back in 2013 she was just getting into data viz and her coding skills were pretty minimal so she spent a lot of time looking up how to load the data and draw the data with code. Another wrinkle is that since she was creating this for a newspaper, it had to published with the right timing, so she had to update the graphic several times while she was waiting for the rain to come!

  • 04:50 - The final viz was created in Processing, exported as a PDF and touch up for publication in Adobe Illustrator.

  • 05:58 - After seeing Jane’s viz, I became super interested in figuring out WHAT made the design of her viz so beautiful and effective?

  • 06:18 - It’s easy to see good design and appreciate it and also feel frustrated that your own designs look so amatear in comparison to others, but that thought reminded me of a short video by Ira Glass that I had seen a while ago. In the video, he said “If you’re someone in a creative field, you probably started because you have good taste.”  And he went on to make the point that when you first start creating, there’s a gap between what you are capable of creating and what you think is good. And it’s frustrating… and this is where most people quit because it’s hard making amateurish work when you know it looks amateurish… but keep pushing! Ira says to keep creating because through practice you can start closing that gap.

  • 07:22 - If I can analyze good design in an intentional way, I can start training my design eye to see all of the little choices that produce really amazing work. Which also reminds me of Andy Kirk’s blog series called “The Little of Visualisation Design” where he highlights and comments on a small design choice that can make a big difference in data viz.

  • 08:23 - I put together three questions to ask yourself whenever you see a data viz that you really like, so you can quickly identify what’s making it good design so you’ll know how to apply it to your own work.

1. What is my first impression? What feelings or emotions is it bringing out in me?

2. What makes it easy to read? Things like visual hierarchy, font choices, color, and sizing. It’s general readability.

3. What’s making this viz stand out or unique? What specifically is making it so alluring to me?

  • 09:00 - So in honor of the sage advice from Ira Glass, I’ll use the acronym IRA to remember these questions: Impression, Readability, Allure.

  • 09:15 - Every time you find a viz that pulls you in, ask yourself IRA and write down your answers: Impression - What’s my first impression? Readability - What’s making this easy to read? Allure - What makes it unique and alluring?

  • 09:27 - And list out a few specific things that are contributing to your answers… like is it specifically the color palette that’s making it alluring? Is it the abundance of white space that’s making it easy to read?

  • 09:40 - Listen for how I put the IRA method to the test on my data viz from episode 23 using Jane’s viz as inspiration!

  • 12:55 - My final takeaway is that we should take the advice of Ira Glass - don’t be discouraged if your work doesn’t match up with your taste - be patient and keep practicing and you’ll close the gap. Specifically for data viz, take a well designed viz and turn it into actionable edits to your work by using this IRA method. Write down what exactly is giving you a good first impression of the viz, what exactly is making it so readable, and what’s giving it that special allure or uniqueness. By taking note of these little design decisions, we can cultivate our taste and design eye so that we can edit our own work in a more refined and elevated way, and keep closing that gap.

  • 13:40 - Jane’s advice to designers just starting out: “Always remember the audience you’re designing for, and what you want to achieve with your data visualisation. Experiment and iterate, and judge your designs based on the goals you want to achieve.”

  • 14:00 - Jane’s website and follow her on twitter!

  • 14:10 - Join the in-person data viz book club if you’re in the Northern Virginia area.



Episode 25: How to Design a More Inviting Data Viz - Featured Data Visualization by Sarah Bartlett
 Sarah Bartlett ( source )

Sarah Bartlett (source)


Welcome to episode 25 of Data Viz Today. How can you create a data viz that feels inviting to your reader? Host Alli Torban explores the specific design elements that can offer your reader an enjoyable experience. Featured data visualization by Sarah Bartlett perfectly demonstrates how investing in an inviting design can lead to a pleasant, informative, and memorable experience.

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

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is about what makes a design inviting. What do I mean by inviting… the definition of inviting is “offering the promise of an enjoyable experience.” To me, an inviting design is one with a harmonious color palette, it’s easy to read and orient myself, and basically the whole thing doesn’t look intimidating, like it’s going to be a lot of mental work to decipher.

  • 01:35 - Today’s featured data viz project is called “Explore European Cities on a Budget” by Sarah Bartlett. Sarah’s a data visualisation consultant & Tableau Ambassador based in London.

  • 02:40 - Sarah said she almost didn’t submit a viz into the feeder competition this year because she had gotten super busy and her search for a dataset was turning out to be uninspiring. There was only 6 days left until the deadline so it seemed hopeless. Then she received the best advice from a former iron viz champ Tristan Guillevin: don’t get hung up on the data set. It’s never going to be perfect and exactly what you envisioned. Pick one, start visualizing it in tableau and a new idea and story will come to you.

  • 03:26 - So with this new perspective of finding something basic and building up from it, Sarah started searching with a tourism perspective and found a website called www.priceoftravel.com that breaks down the costs of traveling to different cities, like lodging, food, activities.

  • 03:50 - But she had a problem…She couldn’t get the data off of the website easily. Her friend Lorna Eden scraped the data from the website using Alteryx.

  • 04:28 - For design inspiration she used pinterest and the site CSS Drive to upload an image of a European city that she liked and it automatically generated a color palette for her to use which had soft blues and browns.

  • 05:40 - She added two things that I think really take it from a nice viz to a really inviting viz.

    1. She added a small map with her color palette using Mapbox and picture for each city. It’s super easy to create custom styled maps in mapbox and if you’ve been wanting to try it, check out my free Mapbox course.

    2. She used icons instead of labels for her bar chart.

      • Pros of icons: save a lot of real estate by replacing text with pictures, and these pictures give your readers the benefit of being able to easily scan and process the information. It’s inviting because people are drawn to real life objects that they’re familiar with and the data doesn’t seem so intimidating.

      • Cons of icons: you have to use icons that are really easily understandable so you don’t make it even harder to understand than text (test it with your audience). It can look cluttered if you don’t use the similar colors and style (like line thickness, curved or straight edges).

  • 08:25 - Sarah used the website NounProject to download royalty free icons

  • 09:45 - Then she asked some fellow tableau users to give her feedback. She said it’s always amazing to her how helpful getting feedback is because you just get so blind to easy mistakes because you’re staring at the viz for so long.

  • 10:33 - Sarah was able to pack in so much information but make it so inviting and fun to explore. I think the top 3 things that contributed to this was her harmonious color palette, the map and image that orients you to the city, and the icons that just give the charts a less intimidating feel.

  • 10:55 - Applying these three things to the viz that I did about chess in episode 11 because it just feels like kind of a cold viz to me, but it’s about something fun and interesting, so I think it could benefit from some design elements that make it softer and more inviting.

  • 11:40 - So first thing, color. I used CSS Drive to get the color palette out of an image of a forest that I chose because it made me think of those chess sets that are carved out of wood.

  • 12:10 - I added a map of the small town outside of Amsterdam where the tournament takes place to orient the reader to what this is. I used Mapbox’s site called Cartogram which lets you upload an image and it’ll automatically style your map features based on colors from your image. So my color palette was extended to my map.

  • 12:38 - Then I started looking for places that I could add icons to make it easier to read. I used a brown person icon and an image of the eventual winner Magnus Carlsen instead of diamonds in the viz.




  • 13:37 - My final takeaway is that inviting design is your way of offering the promise of an enjoyable experience to your reader. And to me, an inviting design is one that’s easy to read, orients you, and doesn’t feel intimidating and cluttered.

  • 13:55 - Try using a color palette inspired by nature or art, use a map and pictures to orient your reader, and use icons to reduce clutter and make your information easier to process.

  • 14:06 - Sarah’s advice to designers just starting out: “Get as much practice as you can. Practice your craft every day if possible. To avoid getting bored, try and visualise data on a subject you enjoy such as your favourite band, movie or hobbies.”

  • 14:40 - Follow Sarah on her website and on Twitter

  • 14:55 - Come and join the Northern Virginia in-person version of the Data Vis Book Club (started by Lisa Charlotte Rost) on August 22nd!

Episode 24: [Mini] 3 Questions to Ask Yourself Before You Viz for Free

Welcome to episode 24 of Data Viz Today. Have you ever heard these words? "We don't have a budget for this work, but we can offer you exposure!" Whether you're more in the graphic design camp or the data journalism camp, you'll probably run up against this at some point in your freelancing journey. In this episode, I offer 3 questions to ask yourself before agreeing to do data viz work for free so that you can protect your time and engage in projects that are truly a win-win.

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

  • Welcome! I'm Alli Torban.

  • 01:00 - If you’re a data viz freelancer, whether your foot is more in the graphic design camp or the data journalism camp, then you probably have run up against people who expect you to work for free… what they like to call “for exposure” or “for experience”... and you consider doing it because you’re building a portfolio or resume or trying to get your name out there to get more work.

  • 01:25 - In this episode, I offer 3 questions to ask yourself before accepting a free project.

  • 01:50 - Listen for my story about hearing this for the first time: “Not sure what you mean by rate...we will print it to give you exposure.”

  • 03:20 - Some people might think of it as paying your dues - you just need to suck it up and get paid nothing or very little at the beginning while you build up your expertise, and I’d tend to agree with the idea of working up the ladder, but I see a big problem with this ‘work for exposure’ model…

  • 03:43 - Unpaid work is a barrier to social mobility. You’re excluding the people who truly can’t afford to do any work for free. We want diverse voices and by making working for free as a requirement to building a portfolio or expertise, then we make hearing diverse voices that much harder. And the more that people accept working for free as ‘just the way you pay your dues’ then the more companies come to expect it and more people are asked to work for free year after year. And it’s a vicious cycle. Not to mention, it’s straight up rude to de-value someone’s time and effort.

  • 04:30 - I’m not going to sit here and tell you never to work for free in order to build up your expertise or portfolio, because we all have different situations and I have worked for free plenty too.

  • 04:45 - Here are three questions that you should ask yourself before data vizzing for free (so that all free work is super strategic!):


  • 05:00 - Question #1: Where does this fall on the ladder to your ideal job?

    • If you’re not really sure what your end goal is - like you want to be a data journalist for the Washington Post or you want be a freelancer who provides custom dashboards to local banks - then define that first. Then you can easily assess how this project fits on your ladder to your goal. Is this project a solid rung that you can stand on to get closer to your goal?


  • 05:56 - Question #2: Could you turn this into a passion project instead for similar or better exposure?

    • With social media, you can create and share a cool project so easily and it can spread to just as many eyes as if you had done it for a company or magazine.

    • So if you’re in a situation where you think this project is interesting and could get some eyes on your work… ask yourself if there’s a way you could do a similar project just on your own as a passion project. You get the chance to learn new tools or techniques by creating it, and then share it across your network, tag people who you think would be interested and get exposure for yourself.

    • If you want to hear more about how to create a fulfilling passion project, check out Episode 18: How to Start a Passion Project That Hones Your Skills & Opens New Doors.

      There are so many benefits - like you have complete control over the project, you’re able to direct people to your personal site, you get to see all of the metrics on views and engagement, which you probably wouldn’t get if you gave it to someone else, and this would be one fewer instance of someone getting work for free.


  • 07:35 - Question #3: Can this person or company afford to pay you for this work? Are they making money off of your efforts?

    • Your time has value. Your current expertise has value. Just because you’re not where you ultimately want to be, that doesn’t mean that someone should make money off of you without compensating you.

    • So if you got the point where you answered question 1 that ‘yes, this would be a solid rung on my ladder to my goal,’ and ‘no, this wouldn’t work better as a passion project,’ then ask yourself what exactly are they gaining by receiving work from you.

    • Are you saving them money by building a dashboard that will streamline their process? Are you giving them interesting content to print in a magazine on the same page as a paid ad? If they’re making money because of your work, then you should ask to get paid. Or at the very least, get creative and ask for some kind of trade. Like in return you get an in-depth case study or testimonial, or a day with the editor to shadow the release process.

    • If they are technically saving money or making money off of you, BUT it’s for a cause that you’re really passionate about, then that might be a good reason to agree to work for exposure.

  • 09:45 - Sometimes taking on a few free projects to show that you can deliver can help get you to where you want to be. You can benefit from the exposure, experience, testimonials, and the relationships. But on the other hand, you can’t buy food with exposure, and it limits opportunities for people who really can’t afford to work for free.


  • 10:05 - My final takeaway is that while I don’t like the idea of people expecting work for free, there are cases where it could be a win-win. Just make sure you acknowledge that this should only be a short season, and that you are really thoughtful about the answers to these 3 questions:

1. Where does this fall on the ladder to your ideal job?

2. Could you turn this into a passion project for similar or better exposure?

3. Can they afford to pay you / are they making money off of your work?

  • 10:45 - I hope that these questions help you make a more informed decision in the future so you can protect your time and engage in projects that are truly a win-win.

  • 10:55 - Have you ever worked for free and it turned out great? Or really bad? Hit me up on Twitter or Instagram… I’d love to hear what happened.

Allison Torbanmini, free
Episode 23: How to Visualize Streaks for Pattern Analysis and Perspective - Featured Data Visualization by Frank Elavsky
 Frank Elavsky. ( Source )

Frank Elavsky. (Source)


Welcome to episode 23 of Data Viz Today. How can visualizing streaks in your data help you find patterns and gain perspective? Host Alli Torban dives into specific ways you can see your data in a new way by highlighting the period of time that something is happening - a streak! Featured data visualization by Frank Elavsky perfectly demonstrates how streaks are not only beautiful, but can also reveal patterns and new questions.

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

  • Welcome! I'm Alli Torban.

  • 00:55 - Today’s featured data viz project is called “Hot Streaks” by Frank Elavsky

  • 01:03 - Frank’s a data visualization specialist who collaborates with and trains researchers at Northwestern University.

  • 01:37 - He was connected to the researcher Dashun Wang (Professor at Kellogg School of Management at Northwestern) to create a data visualization for his recent research results on creative hot streaks - the idea that winning begets more winnings.

  • 02:00 - They looked at the careers of thousands of movie directors, artists and scientists and studied their hot streaks - the periods when someone’s performance was substantially better than their typical performance, this was determined by the top 3 IMdb score of movies for directors, the top 3 sales for artists and the 3 most cited publications for scientists.

  • 02:30 - Almost everyone experienced at least one hot streak during their career, and it usually lasted a few years, but there’s no way to determine when it’ll happen.

  • 02:45 - They wanted to create a viz that was beautiful and would captivate their audience, but also convey how ambitious the model and the datasets were.

  • 03:07 - Frank went through a very iterative process, doing about 40 different sketches before sending a few over to the research team for feedback.

  • 03:23 - He started to code one line represented each person’s career, and he stacked them on top of each other so they all had the same beginning and end point. He highlighted each hot streak in organge-gold colors (which was inspired by God of War concept art), but leaving it like this made it look like everyone had a hot streak that lasted their entire career.

  • 03:45 - Frank explained that it’s a result of perceptual biases that we have in our visual system: we see brightness as a visual feature much more readily than we see darkness.

  • 04:00 - So he knew that he had to sort the data meaningfully in order to make the design meaningful. He ended up using a sorting algorithm that he called “streak middle,” which finds the first time someone begins a hot streak and then the last time someone ends a hot streak. We then find the point in their career that is between these two, and sort based on that position, from people whose "streak-middle" was early-career to those whose streak-middle was late career.

  • 04:45 - This is what led to the final viz where you could start to see a pattern that suggested that directors tend to streak much earlier than Artists and Scientists, while Scientists seem to streak the latest. The viz was not only cool to look at, it actually raised new questions for the research team - why do people in certain careers tend to streak earlier in their careers? Can they build a model to explore this relationship?

  • 05:00 - Frank used javascript and d3.js for the final viz.

  • 05:30 - He said the hardest part though was that they had a very short window for completion, so he had to rapidly iterate and make a lot of decisions about which features to drop.

  • 06:25 - Other applications of streaks:

    • Sports - stack the football seasons for one team on top of each other and see if there’s a pattern to when your team has a winning or losing streak

    • Marketing - like streaks of high traffic to your website every week. Are there patterns throughout the year? If you’re looking to launch a product, maybe you want to time it with your longest streaks of heavy traffic.

    • Or visualize streaks in gambling, financial markets, video gaming, or tracking health related streaks like visualizing the days you go without smoking - is there a pattern in when your streaks occur or are broken?

    • Example about my podcast release day - I could visualize the hot streaks of downloads every week by stacking every week on top of each other, and visualize the 3 consecutive days with the highest number of downloads in every week, and see if there’s a pattern or general trend… Maybe my download streaks are actually over the weekend so it’d be better to release then to better fit your guy’s actual listening habits. If I felt like I needed to experiment with something like that, then first analyzing the pattern in my download streaks might be a good first step.

  • 08:20 - Inspired viz about putting recent rain streaks and rainfall in perspective for July in the D.C. area.

  • 09:10 - I downloaded the daily rainfall from 1948 to 2018 for the Reagan National weather station from the National Oceanic and Atmospheric Administration website

  • 09:24 - After I downloaded the data, I added a new column to represent streaks. I used an excel formula to count consecutive rain days, and then pulled my csv into tableau.

  • 09:36 - Andy Kriebel’s Visual Vocabulary with a bunch of useful charts that you can download!

  • 10:25 - I struggled with simply showing rain streaks and amount of rainfall. I’m still iterating!

  • 11:20 - You can immediately take away that there’s not really any pattern and that the recent rain streak was pretty long and heavy compared to past years but not the biggest.

My inspired viz. A work in progress. :)


  • 11:37 - So my final takeaway is that next time you have data over time, think about whether visualizing a hot streak could reveal any useful patterns or insight or give you a different perspective on your data. Line up your data with the same start and end points and stack em up!

  • 11:55 - Frank’s advice to newbie designers: learn to sketch out your data viz, and more importantly, consult with your audience - it’s the best way for you to know how effective your data viz is - you need to know how your intended audience is perceiving and understanding your message in order to make a really powerful data visualization.

  • 12:26 -Follow Frank on Twitter!

Have you ever created a streak data viz? Comment below! :)

Episode 22: [Mini] How My Design Process Has Improved Since Episode #1
 Photo by  Icons8 team  on  Unsplash

Photo by Icons8 team on Unsplash


Welcome to episode 22 of Data Viz Today. How has my data viz design process changed over the past 5 months? Well, it was pretty non-existent to start with, but after interviewing 20 top data viz designers, I've learned a lot and now have a much more defined design process. I want to share my process and would love to hear what yours is like so we can learn from each other!

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

  • Welcome! I'm Alli Torban.

  • 00:40 - Today’s topic: how my workflow has improved over the past almost 5 months of doing this podcast based on what I’ve learned from the designers that I’ve featured.

  • 00:50 - When I first started making data viz on purpose like a year ago, my workflow would go something like… have some data, put it in a chart whose type was determined by my whims, see if I could clutter it up with some color or complexity so it would be more interesting, add a data source at the bottom and done…

  • 01:15 - How are we supposed to know what makes a good data viz workflow? I learn the best by hearing real-life stories and hearing about one person’s experiences, so I wanted to share with you what my data viz process looks like now that I’ve been studying data viz designers and learning about their design process every week for the past almost 5 months. My hope is that it’ll provide some insight or inspiration for your workflow.

  • 02:10 - Here’s my workflow right now (assuming I already have a clean data set and I’m ready to start designing):

  • 02:20 - First step: I write my goal in one sentence, think about the balance I want to strike between attention/beauty, understanding, implication. And write out some descriptive words and motivations of my audience. Referenced: Episode 1 and 6.

  • 03:20 - Second step: Exploring different chart types based on my data. Diagrams that I use: Chart Choosing Diagram by Andrew Abela and data-to-viz website. Then sketch out different options. Referenced: Episode 4

  • 05:03 - Third step: recreate my sketch in a tool/software using a basic chart type. Referenced: Episode 12

  • 05:30 - Fourth step: Add design elements/creativity to try to humanize the data and connect my audience to my story. I use color (Viz Palette tool), visual metaphor, etc. Referenced: Episode 5, 9, 14

  • 07:15 - Fifth step: I add the annotation layer that has a clear visual hierarchy and simplify the viz. Referenced: Episode 7

  • 08:20 - Sixth step: Seek feedback from others. Referenced: Episode 10, 21

  • 10:00 - Seventh step: Step away to get perspective. Referenced: Episode 19

  • 10:21 - There is it! Clearly define my goal, the needs of my audience, explore my chart options based on my data, sketch, make a clear, basic chart, then think about adding creativity and engagement through things like color or visual metaphor as necessary, add my annotation layer, seek feedback, iterate.

  • 10:45 - I fully expect that I’ll be slowly refining and improving this workflow over the next 5 months as well!

Episode 21: How to Gain Insights by Identifying Intervals in Your Line Chart - Featured Data Visualization by Ben Jones
 Ben Jones. Image via  LinkedIn .

Ben Jones. Image via LinkedIn.


Welcome to episode 21 of Data Viz Today. How can segmenting your line chart into intervals help you gain insights into your data? Host Alli Torban dives into specific ways you can visually break up your line chart into intervals as a way to quickly see your data from a different perspective. Example questions you could answer: Which marketing campaign had the greatest effect on sales? Does varying the basketball shot clock length change how many points are scored? Featured data visualization by Ben Jones perfectly models how this technique can visually communicate a relationship between two metrics.

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

"Monthly US Unemployment Rate by Presidential Term" by Ben Jones

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is about visually breaking up your line chart into intervals to help see how your metric’s variation coincides with something else, so that you can gain insights and possibly take action. What are some examples where this would be helpful and how can you create your own?

  • 01:20 - Today’s featured data viz project is called “Monthly US Unemployment Rate by Presidential Term” by Ben Jones. Ben is a technical evangelism director for Tableau Software and teaches data visualization at the University of Washington.

  • 01:55 - Ben created his viz because he noticed that Political leaders often claim credit or assign blame for economic indicators, such as the seasonally adjusted unemployment rate, but he wanted to understand how this particular indicator has changed over time in the United States, relative to the presidential administrations that have coincided with the periodic swings. His goal was to plot the unemployment rate over time, and see how the presidential terms coincided with rise and fall of the unemployment rate.

  • 02:25 - Ben grabbed the unemployment rate from the Bureau of Labor and Statistics website. He added columns for president and party.

  • 02:50 - He used Tableau, and visually showed us the presidential intervals by using color, vertical lines, and labels at the top.

  • 04:30 - You can quickly look at this chart and visually comprehend how the presidential terms coincided with rise and fall of the unemployment rate, which was Ben’s goal.

  • 05:15 - Lots of use cases for plotting metrics over time and identifying intervals of change in power or leadership, like Ben did, whether it’s the president, or the CEO of a company.

  • 05:28 - You could also use it build a viz that will help you visually understand the impact of different marketing campaigns on a metric that’s important to you. Great way to visually assess periods of experimentation.

  • 06:15 - Other examples could be plotting sales and visually identify intervals where there was a tariff in place or not.

  • 06:26 - Or in sports, you can plot a particular metric and visually identify intervals where different rules were in place to see if that rule might have been affecting the game in some way.

  • 06:40 - My inspired viz: Showing the average points per game in Men’s College Basketball in the ACC from 1977 to 2017 and identifying the intervals by what kind of shot clock was in place at that time.

  • 07:25 - My goal was to plot the average points per game over time, and see how the change in the length of the shot clock coincided with the rise and fall of the scores. I used Tableau. See it here:

My inspired viz.

  • 09:35 - Final takeaway is that you can visually break up your line chart into intervals to help see how your metric’s variation coincides with something else. Then when you add small things like coloring the line by which interval it’s in, adding vertical markers, labeling each interval at the top and in the same color, and even adding trend lines or average lines, and then you have a powerful tool to assess what’s going on.

  • 10:15 - Listen for Ben’s 8 tips for data viz newbies!

  • 11:15 - Follow Ben on Twitter! @dataremixed

  • 11:42 - Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.