accessibility for hearing impaired


Accessibility is something we talk about frequently in the context of data visualization: we should make our visualizations accessible for our audience. For me, this word has a couple meanings. First, I think of accessibility as making things easy on our audience—using a graph that makes sense, taking intentional steps to make it clear where to look, putting words around our visuals both so our audience knows exactly what they are looking at and also to answer the question “so what?”. But there is another level of accessibility: designing for those who have different innate abilities than we do.

Sight is the sense that we tend to associate most with data visualization. It is visual, so this makes sense. Not everyone sees the same, which leads to considerations in how we use color and contrast, for example. Amy Cesal penned a great guest SWD post on this topic last year, outlining 5 ways we can make our data visualizations more visually accessible.

I’m excited to announce some ways that we are making our offerings at storytelling with data more accessible in a totally different way: for those having hearing impairment. We’ll be offering real-time closed captioning at the upcoming Milwaukee public workshop (details & registration). We’ve also been busily transcribing the podcast, both for those for whom it’s easier to read than listen, as well as those who’d simply rather read than listen. Transcripts are currently available for the non-interview podcast episodes:

Transcripts can also be accessed through the podcast page. The remaining episodes are in the transcription queue, so please stay tuned for those.

Consider: how can you make what you do more accessible for your audience?

#SWDchallenge: happy new year! let's try something new

Happy new year! I hope you were able to take some time over the holidays to be joyful and recharge and are ready to welcome the coming year.

For me, 2018 was a year of change: my family’s move from San Francisco to Milwaukee meant a new city, a different time zone, an unfamiliar house, an unknown school, new friends (plus variant ways of staying connected with those we already had), changed proximity to family (closer to some, further from others), extreme weather (amazing how quickly hot and humid can turn to freezing!) and many other changes. As I look ahead, I expect 2019 will be a year of reforged stability and growth.

The beginning of each year for me tends to be both a time to reflect, as well as a time to consider where and how I might push myself outside of my comfort zone and try new things. I’ll be encouraging you to do the latter through this first 2019 challenge—to try something new (irrespective of whether that makes you uncomfortable, though I’m a strong believer that productive discomfort can be great, as it means you are growing). I challenge you to try a new tool for visualizing data.

Many tools are available for visualizing data. There are spreadsheet applications like Excel or Google Sheets, chart creators like Datawrapper, Flourish, or Infogram, data visualization software like Tableau or PowerBI, you can write code in HTML, R, or Python or leverage libraries like D3.js. This is definitely not a comprehensive list, and I’m excited for the new tools that I expect to be introduced to as a result of this challenge!

When it comes to data visualization tools, my typical advice is to find a tool or set of tools and get to know them well enough so they don’t become limiting factors in the way that you visualize and communicate with data. There is no perfect tool: each has its own set of pros and cons. That’s actually going to be one of the great benefits from this month’s challenge—in addition to learning by testing out an unfamiliar tool directly, I’m hoping that we will all learn about tools from others’ explorations of them as well. I’d love if you could please include commentary with your submission about what you like or find useful or intuitive in the instrument you use, as well as what limitations or frustrations you may have encountered. If you explore additional resources as part of your learning process, please let us know about those (linking when possible) as well!

To kick us off, I figured I should attempt a new tool. I decided to test out Flourish, which touts “powerful, beautiful, easy data visualization” that will allow you to “quickly turn your spreadsheets into stunning online charts, maps, and interactive stories.” I signed up for an account (free, so long as you’re ok with your data and projects being made publicly available, if not you can upgrade to a paid account). I spent a grand total of a single hour (they said quick and my goal is familiarity, not perfection—you can decide for yourself your goal in undertaking this challenge and how long you’d like to spend) poking around and creating a graph.

Flourish is easy to navigate: when you elect to create a data visualization, there is a page of templates to scroll through. These range from all the standard lines and bars to some more nuanced stuff, like connected dot plots and Sankey diagrams (there are also looks to be some good mapping capability). There were also a few scary templates available (“grid of pie charts”), so as with any tool, you still need to use your brain to create something sensical. You can start with any of the templates—which are pre-populated with data—then replace with your own data and make formatting changes as desired. I spent a moment playing with the slopegraph template, which both looks very slick (nice default formatting and built in functionality for highlighting choice lines and pushing others to the background) and I liked how easy it was to toggle between absolute values, rank, and percent change.

When it came to making a graph, I decided to recreate a line graph I recently made in Excel:

My original graph created in Excel

My original graph created in Excel

My remake created with Flourish

My remake created with Flourish

You’ll note there are some differences. First, I’ll say it was super easy to import the data (you can also physically copy and paste from other applications). With more time, it’s possible there are additional formatting changes I could make to more closely mirror the original. I didn’t like that the default graph (not shown; I started with the basic line graph template) automatically smoothed my lines and rotated x-axis text diagonally, but both of these issues were easy and quick to correct. In terms of other shortcomings, I seemed to have less control over individual elements than I’m used to. For example, I couldn’t figure out how to upper/left align my axis titles, label my lines directly rather than use a legend, or format individual lines or data points. While I wasn’t able to highlight specific points, like I did in orange (above, left), there is a cool “add data colors to header text” feature that I made use of in the title (above, right). I also couldn’t change the order of the lines, but was able to brute force that behind the scenes by reordering my data columns so that the External line is on top and doesn’t have the Goal line crossing in front of it. While my visuals tend to be meant for a static environment, Flourish allows you to mouse-over to see values or get more details (I did this for the Nov data point in the screenshot), which would be great in an online/interactive environment. I have a dialog going with Flourish on some of the formatting nuances I wasn’t able to figure out, so I may have an update to include in the recap post. There is also a stories feature that I plan to explore.

Overall, I will say it was quite easy to make a good looking graph—all through drop downs and without any need for specialized knowledge, or code, which I believe is one of the stated goals for this tool. While I experienced some minor frustrations (Why can’t I make the Goal line dashed? Why can’t I add markers and labels to just some data points? Why can’t I move the axis title text or make the main title text bigger?) I also enjoyed the mind-bending that playing with the unfamiliar forced. It removed some constraints, while imposing others different than those I’m used to—inspiring me to think creatively and problem solve in new ways.

So, with all of that said… Are you ready? It’s your turn!

the challenge

My challenge to you: identify a tool for visualizing or communicating data that is new to you and put it to use! Feel free to pick a graph you’ve made before in your typical tool, or grab one from the SWD book downloads, or create something new using data of your choosing. I recognize trying out something new can take time and that you may still be getting back into the swing of things after the holidays, so will provide additional time this month relative to the regular week. DEADLINE: Tuesday, January 15th by midnight PST. Full submission details follow (be sure to email it to us, taking note of specifics below, for inclusion in recap post!). You're also welcome to share at any point on social media using #SWDchallenge.


  • Make it. Identify your data and create your visual with the tool of your choice (something new!). If you need help finding data, check out this list of publicly available data sources. You're also welcome to use a real work example if you'd like, just please don't share anything confidential.

  • Share it. Email your entry to by the deadline. Attach your image as a .PNG. Put any commentary you’d like included in the follow up post in the body of the email (for sure this time tell us what tool you used, any resources you leveraged, and pros/cons you experienced and want to share); if there’s a social media profile or blog/site you’d like mentioned, please embed the links directly in your commentary (e.g. Blog | Twitter). If you’re going to write more than a couple paragraphs, I encourage you to post it externally and provide a link or summary for inclusion. Feel free to also share on social media at any point using #SWDchallenge.

  • The fine print. We reserve the right to post and potentially reuse examples shared.

I’m super excited to see all of the tools that get explored through this month’s challenge! Stay tuned for the recap post in the second half of January, where we’ll share back with you all of the visuals created and commentary shared. Check out the #SWDchallenge page for past challenge details and recaps. Happy 2019!

tapestry conference


At the end of November, I had the pleasure of attending the Tapestry Conference in Miami. I don’t attend a ton of conferences and this is actually the only one that exists where I’ve (two years in row!) been present for every single session (both physically and consciously) and found something useful or inspiring in each one. If you’re reading these words with slight envy for not having been there—I can’t recreate the great break-time chit chat with attendees, but I can share the presentations (huge thanks to organizers for making these available): here are the videos.

In particular, I’d recommend the keynotes. Mona Chalabi opened the conference with an entertaining session discussing a number of her hand-drawn graphs (a quick scroll through her Instagram will give you a sense of your work if you aren’t familiar; unfortunately her talk isn’t being shared). She described wanting to feel something about the data and marrying the subject and the visualization so that if you see the visual, even without labels someone can get some sense of what it is about. She also worked in good reminders on significant digits (too many conveys false sense of precision), designing with visual impairments in mind (using alt text or sound, like in this work), and how important the simple question “do you get it?” posed to people unfamiliar with your topic can help point out issues or help you to identify improvements.

Matt Kay’s keynote on Uncertainty (“A Biased Tour of the Uncertainty Visualization Zoo”) was fantastic—he made the point that it isn’t necessarily true that people aren’t good at understanding uncertainty (a claim often made) and that there are intuitive ways to communicate uncertainty that we should be using. I like the onus this puts on the designer of the information. Matt illustrated several specific methods—icon arrays, quantile dot plots, and animating—for better communicating uncertainty. I also learned a new term: subitizing, which describes how we can see a small number of something, for example three circles, and we recognize (without counting) that there are three. This is both useful to be aware of when designing graphs and also simply a word that I will enjoy adding to my vocabulary.

Elijah Meeks delivered the closing keynote on the “Third Wave of Data Visualization.” He describes wave one as Tufte-inspired with the goal of clarity and the second wave of systems following Wilkinson’s The Grammar of Graphics, leading into the third wave of today. Rather than tell you more about it, I encourage you to listen to Elijah tell you about it directly (plus more!) in Episode 12 of the storytelling with data podcast.

In addition to the keynotes, there were eight short stories (roughly 15 minutes each, standout ones for me were Jonni Walker’s and Alex Wein’s) and a number of short talks (about 5 minutes each). You can hear Jon Schwabish and me chat about more of the sessions in our Tapestry roundup. I highly recommend watching the videos of the Tapestry presentations.

Big thanks to organizers, speakers, and attendees for combining to make this an awesome event (and extra thanks to the organizers for recording and making the content widely available!).

let's visualize the holidays!

‘Tis the season to be merry (irrespective of which holiday you celebrate), so let’s combine that festive cheer with something near and dear to us all: data visualization! This month, your challenge is to visualize data related to the holidays.

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