Posts tagged mini
Episode 41: [Mini] How to Go on a Color Diet

Welcome to episode 41 of Data Viz Today. Are you terrified of color? Me too! :) Sometimes there are just too many choices. So I’m going on a color diet! What is a color diet? Well, for me, it means to be more conscious about how I use color in my visualizations to make sure that I’m using color to solve a problem. In this episode, we’ll talk about how a color diet can improve your work, plus a few tips on how to get the most out of the few colors that you do use.

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

  • Welcome! I'm Alli Torban.

  • Today is a mini episode where I talk about a data viz topic that I think is important. And today that topic is how to go on a color diet. What is a color diet? Well, for me, it means to be more conscious about how I use color in my visualizations to make sure that I’m using color to solve a problem and not just as visual entertainment. So in this episode, we’ll talk about how a color diet can improve your work, plus a few tips on how to get the most out of the few colors that you do use.

  • I don’t have any formal design training, like many people in the data viz field, so I never learned the ins and outs of color like a lot of people who come from like a graphic design background. So my color evolution went from just accepting software defaults, to changing defaults to other colors based on my whim, to where I’m at now where for personal projects I like to find colors that first make sense for my data, like is a diverging color palette appropriate or can I group these categories in a reasonable way so I don’t have 100 colors? And then I find other colors that are complementary, or pleasing to the eye as needed and color-blind safe. One of my favorite ways to find color palettes is to the Google Art & Culture Art Palette site which shows the palettes of tons of artworks, and then run it through Susie Lu and Elijah Meeks’ Viz Palette tool to make sure there aren’t any color conflicts.

  • But when it comes to picking color for work projects, I’m frozen into a panic. The choices feel super overwhelming the stakes seem so much higher. So I decided to go on a color diet and explore how to do more with less.

  • The first step in going on a color diet is to design your visualizations without color, using a monochrome palette, which means displaying images in black and white or in varying tones of only one color. Think of a color ramp from white to black and all the grays in between.

  • A big benefit of designing without color, is that you can really focus on the data first. Anand Satyan wrote a really great Medium article about the benefits to UX designers to design with no color. One of which is that you allow your eye to really see the layout and spacing of all your elements. Your eye isn’t getting drawn by color, so you notice how things are grouped, how readable is your text…

  • Another benefit is that the people you’re working with, clients, stakeholders, will start asking better questions when you show a monochrome design first. Anand says that you can have a conversation about what color works for which elements, rather than the conversation focusing on why you chose yellow.

  • So first designing without thinking about color will help you focus on your layout, spacing, alignment, hierarchy. And it’ll also help you and your client focus on HOW you’re using color rather than which colors you’re using.

  • That line I recently read in Scott Barinato’s new Good Charts Work Book, which is an amazing book that I’m working through with hands-on exercises. In the chapter about color, he wrote: “Think HOW, not WHICH.” And this itty bitty change in thinking was huge for me. It totally changed my perspective and anxiety around color. Instead of freaking out about WHICH colors to use, I first needed to think HOW. How will using color improve my reader’s understanding? How will this color in this spot make this viz more effective?

  • When your goal is to use color to solve a problem, the choices are a lot easier. Do you have a line chart of temperatures in 10 cities? The software default will give you 10 nice bright colors for each, but HOW is color going to improve your reader’s understanding? Maybe your point is to show how your city compares to other cities, so 9 will be gray and your city will be blue. Color has helped focus your reader on your story.

  • So how can you get the most out of using a monochrome palette, it can feel kind of restrictive at first, but I came across a presentation by the cartographer Daniel Huffman where he argues why you should design maps in monochrome. The presentation is only 10 minutes long, and definitely worth watching all the way through, but there were two tips in there about getting the most out of monochrome that I really liked.

  • One tip is say you have a map and you make the water white and the land gray. That would be my initial instinct if I were going to only use a white to black ramp. But now, the grays you have left for everything else are limited. But, you can get those grays back, if you make the water and land both white, and you just give a glow to the land so it looks like it’s popping up away from the water, or add concentric water lines to the coastline. So your reader can distinguish between the two, but you still have your whole color ramp to use for other things.

  • Another tip that Daniel had was to think about how you can use patterns, like diagonal lines or dots. This allows you to use one color of gray for a whole bunch of things.

  • If you think about it, some other benefits of designing in monochrome is that you don’t even have to worry about whether people are going to understand your viz if it’s printed out in black and white, or if your palette is color-blind safe!

  • My final takeaway is that designing your visualizations without color or in monochrome helps you in the design process by allowing you to focus on the layout and helps you focus on HOW color will help your reader. You can get the most out of using a monochrome palette by using techniques like glow or shadow, or using patterns. Once you max out your monochrome, think about how that one pop of red will make the HUGE impact you’re looking for.

  • I’m looking forward to going on a color diet and pushing my monochrome limits. If you have a pro-tip on designing without color, I’d love to hear! I’m on twitter at DataVizToday.

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

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 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 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 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 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 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 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 19: [Mini] How to See Your Data Viz With Fresh Eyes
Photo by  Bud Helisson  on  Unsplash

Photo by Bud Helisson on Unsplash


Welcome to episode 19 of Data Viz Today. How can you see your data viz with fresh eyes? Host Alli Torban dives into specific ways you can view your work from a new perspective when you're short on time!

  • Welcome! I'm Alli Torban.

  • 00:45 - This week’s mini-episode is about how you can hack the “fresh-eyes effect” and see your data viz from a new perspective immediately!

  • 01:25 - I put together a list of 5 of my favorite ways to quickly see your data viz from a new perspective. Here we go.

    1. Read it out loud. Read your title and accompanying text or annotations out loud. When you pull it outside of your head, the main message of your viz can feel different. Or, even better, record yourself reading it and then play it back with your eyes closed - and focus on what your words are making you envision.

    2. Print it out. You can print out your viz in a few slightly altered ways to give yourself a new perspective. Like, you can print it in black and white, or print it on a smaller scale. Then you can do other things with it like hold it up to a mirror or turn it upside down. Think about what stands out to you in your viz when looking at it in these different ways?

    3. Change your scenery. Try to re-read your assignment or goal and then grab your print out and look it over while you’re walking or ideally outside. Changing your physical location can help get you out of all the minutiae of designing your viz and help you see it from the perspective of someone who’s looking to learn something. Think about whether you’re clearly communicating your main point and desired action?

    4. Imagine the best compliment. Think about the moment you send your viz to your client or boss. What is the most meaningful compliment you could receive about your viz? Are you hoping to hear something like “this viz is so engaging!” or “wow, we really need to take action” or “this is beautifully designed” - think about the compliment you’d want to receive the most, and that can help you re-frame how you’re seeing your viz. Ask yourself if your viz offers enough to be complimented in that specific way.

    5. Pretend to start over. Imagine that you just lost all of your work and you need to quickly recreate the viz in one sketch. You can think about your specific assignment or goal again, and then sketch out on a piece of paper a viz that solves that problem. I find that I sometimes go in a particular direction when creating a viz for whatever reason, and with this exercise, I get the chance to re-make the decisions that I made at the beginning, which can help alert me to problems or give me a new idea. So try to recreate your viz with a quick sketch with your final goal in mind, and notice if you’re drawn to creating anything differently.

    6. BONUS tip: Ask someone else. This one is obvious but worth mentioning - you can try to use someone else’s fresh eyes - give it to someone else and let them look at it for an appropriate amount of time (like if your goal is for someone to understand something quickly, then give them a handful of seconds, but if your viz is meant to be more exploratory, give them more time), and then ask them what they took away from the viz. If they seemed to miss the mark, maybe there’s something you could change to help.

  • 04:50 - My final takeaway is that real, true fresh eyes are the best way to give you some perspective on your data viz, but if you’re short on time, there are a few ways to hack it - you can read it out loud, print it out, change your scenery, think about your most desired compliment, or sketch out a redo.

  • 05:30 - Nominate a data viz to be featured on the show!

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