A few weeks ago, I attended an interesting one-day workshop on the responsible and ethical use of data in the data visualization field in New York City. The Responsible Data Forum brought together about 35 people to address issues and topics related to ethics in data visualization. Topics included literacy, transparency, goals, uncertainty, culture and inclusion, and other great, topics.
Partly in response to that forum–and some other discussions going on–I’m happy to be joined by two guests on this week’s episode: Mushon Zer-Aviv from Shual Design Studio, who also hosted the forum, and Kim Rees from Periscopic, whose tagline is “do good with data.” We–Kim and Muson, really–talk about whether data visualizations can elicit empathy, who is responsible for calls to action, and compassion fatigue. Apologies if this week’s episode goes on a little long for your taste; I found the discussion so interesting that we just kept talking.
Please let me know what you think about empathy in data visualization in the comments below or on Twitter. And please rate the show on iTunes or your favorite podcast provider so that others can learn about the show.
Links
Mushon Zer-Aviv | Shual Design Studio
Periscopic’s Gun Deaths Visualization
Jacob Harris | Connecting with the Dots
Popular Science | Hiroshima Visualized
Awesome Twitter discussion on DataViz Empathy
Blog Posts
Mushon | DataViz – The UnEmpathetic Art
Paul Bloom | Against Empathy
Steven Lambert | And What Do I Do Now? Using Data Visualization for Social Change
Jon Schwabish and David McClure | Everyday data aren’t going anywhere, for better and worse
![Spotify](https://policyviz.com/wp-content/uploads/2023/09/main-og-image.png")
Thank you for a great show and a stimulating discussion!
I think i heard one of you mentioning the blog post by Catherine D’Ignazio on feminist data visualization. Here it is for completeness sake: https://civic.mit.edu/feminist-data-visualization
I have been listening to your podcasts while working today and just now stumbled on this one. I think a key piece in the argument over whether data visualization can or should garner empathy is the reminder that data is most often an abstract representation of a real thing. Alberto Cairo’s argument skips this point and moves straight into the pure abstract, comparing data to mathematics, which is usually abstract with no real anchor. Currently, most data visualizers are not attempting to visualize pure abstractions, but rather some representation of real-world things or events.
This reminds me of Claude Shannon’s introduction of the bit (which I also learned about this afternoon through your podcast, thank you!). In that podcast, the statement was made that Claude Shannon broke down the differences between all information (phone calls, writing, pictures, etc.) into the manageable and measurable bit. The data we use is the same way – using a universal abstract system to break ANYTHING of ANY SCALE into something manageable and measurable. Even if Kim Rees had a teleporting time machine that would allow a person to experience/witness the gun deaths that took place in 2010, without data we would have no language to effectively comprehend this group of events as a whole. Thankfully, we have this system of data/numbers that can aggregate and break down those experiences into something able to be comprehended (I think of the system that allows a photo to be scanned and sent via the internet). It is the job of data visualizers to unpack their representations from the world of the abstract and return it to the world of the real. To that end, I believe that data visualization absolutely can and should invoke empathy, because the data is not abstract, but rather abstract representations of real things. Rarely are data visualizers ever sharing numbers; they are sharing paper sales and hospital discharges and student graduations and gun deaths abstracted as numbers, because sharing the real thing is unfeasible.
This rooting in reality is a key piece that I haven’t heard much overt discussion on, and a key piece to storytelling in data is not to get your audience to care about your data, but rather to get them to care about the reality your data is representing. The trick is to do it so seamlessly that your audience forgets about the abstract medium of the data, and associates directly to the original world of the real (or at least as close as might be possible). Neil Halloran’s pieces are powerful because he takes time at the beginning to successfully root the abstract of his numbers within the reality of what they represent. That video was the first time I got a visceral inkling of Russia’s losses in World War 2, not because he made me experience everything that happened, but because he took those experiences (to the best my uninformed brain can guess) and successfully compressed them into the world of abstract data while keeping me rooted to their original meanings. Another example I can think of is when I watch an ESPN data stream of a football game because I can’t get the game on TV or the web. That screen is nothing more than updated data visualizations. Sometimes I get excited, sometimes not – the measurements are the same, but my excitement comes from which teams are playing and what the data is representing. Through their clever and intuitive representations, I have moved beyond the abstract medium of data and am now hopping around because the stat “3rd & 8” refreshed into “1st & 10”.
Hi Nathan,
Thanks for this thoughtful comment! In general, I think you’re absolutely right here, but I also think it can be hard for both analyst and reader/user to connect with metrics that may be a bit more abstract, like GDP, national income, or even unemployment. While there are obviously people behind all of those numbers, the size and underlying measure, can be hard to connect with.
Thanks again,
Jon
You bring a good point with your more abstract data sets; all the examples I had used were very concrete and simple to understand (in assumed situations). The knowledge level of your audience on a given topic will drastically affect how much groundwork you have to lay before your can make your point. If you uncovered an important insight about the GDP, you will probably be able to more quickly and easily deliver that insight to an economist than a gen-ed student in a coffee shop who peruses a random newspaper laying on the table. Even with my example about the ESPN football stream, those updating stats would be nonsense to a person who isn’t familiar with the sport.
Perhaps my point was less about true empathy than it was about connecting the reader with why your message and data are important. Calling more on the pathos appeal of the rhetorical triangle (which every dataviz should theoretically follow) rather than just trying to make every dataviz draw an emotional response to humanity somehow. My core passions and learning are in literature (I wandered into marketing and then into data), and many times I’ve thought of data viz through the lens of the hero’s journey. We can’t just go into the other world and get the boon (in this case, the big insight within the data) and call it a day – we have to successfully bring it back and deliver it to the community, and sometimes that’s trickier of the two treks. Depending on who that community is, that might include figuring out in haiku space and style how to get the general public up to speed on what the GDP is and why it’s important. A key here would be acknowledging that getting the audience to the appropriate context point is an important part of the equation, and allowing sufficient space/time/resources to successfully achieve it. It’s like what Jen Schiffer said on your newest cast about accessibility; she wants to be able to explain her ideas to others who don’t understand in a literate way.
This might be a somewhat off-the-wall and only partially-useful example, but I immediately think of Kim Stanley Robinson’s Mars Trilogy. In it, he starts with the journey of the first scientists setting up a colony on Mars, and then proceeds through the next 150-200 years of humanity’s life on Mars and all it’s resulting science, culture, conflicts, and politics. In them, the author takes lots of time discussing very technical details that make up how life on Mars is developing, but he does it in such a clear manner, laying out what’s going on and how it ties into the big picture, that it turns out to be fascinating rather than dry. While going through them, I remember being amazed that the author gave me a vested interest in the story’s debates over things like whether mirrors should be put in Mars’ atmosphere, or what methods should be used to get organic material into martian soil.
I might be naive, but I suppose I currently am under the impression that if an insight is important, it will be interesting, so long as it is properly understood. The trick is bringing the insight into the realm of understanding for the audience, or better yet, bringing the audience to a point of understanding for the insight.