On Wednesday I had the pleasure of attending the annual one day Tapestry Conference for the first time. I was blown away by many things - the nearly equal representation of genders, the quality of thought presented by speakers, and the desire of attendees from many disciplines to experience the day together and improve, question, and share their practice. In an effort to synthesize my notes and reflect further I'm going to share my key takeaways from some of the speakers. While I know these bullets could never replace the richness of experiencing this live with carefully curated examples to enrich the takeaways, I hope that you find your own “aha” moments and questions arise.
News Apps Developer, ProPublica
- Data doesn't speak for itself - it reflects your thinking.
- Visualization are not neutral and one design rarely fits all realities.
- Sometimes it's harmful to reduce individual people to dots.
- Provide context to your visualization - e.g. flaws, source, what “good” or “bad” mean.
- Users are not always who we imagine so design against bias by diversifying your teams, seeking peer review, or consulting an expert.
- Have procedures against bias such as diverse user testing.
Assistant Prof of Data Visualization & Civic Media, Emerson College
- Creative data literacy recognizes that citizens and non-specialist need alternative pathways into working with data that connect to their lived realities.
- Think about a community survey. How can you take a lot of data and synthesize the information in deliver it in a creative way for non-data specialist?
- Her students followed this process:
- Divide and Conquer: Subsets of the data were assigned to people.
- Analyze: Each subset was then analyzed using a tool called databasic.io - it's free, check it out!
- Themes: The analysis was used to identity themes.
- Visualize: Each theme became a question of the community and an appropriate visualization (in this case, gifs) to illustrate the question.
Data reporter, Tampa Bay Times
- Be careful of getting enamored by technology and focus on the story you’re telling.
- The story is not the tool, the story is the data.
- Avoid distractions; remove unnecessary graphics, tooltips, etc.
- Sometimes, the design may unfold as the story unfolds.
Cole Nussbaumer Knaflic
Author & Speaker, storytelling with data
- Great stories include repetition, story, and picture.
- The typical business presentation follows a linear path but that can be a selfish path.
- The same information can be delivered in an arc where you start with the plot, have rising action, climax, falling action, and the ending.
- Question the accuracy of an analysis. Matthew used the example of the Bechdel test. The test analyzes whether a work of fiction features at least two women or girls who talk to each other about something other than a man or boy. It’s recieve a lot of criticism so
- So what can we do? Fix the test and address the concerns. Matthew decided to analyze the film data in a different way and attempt to make it better (see http://poly-graph.co/bechdel/).
- Once he made his analysis public there were two reactions: 1) There was an echo chamber effect and the people that liked the Bechdel test before still agreed with his results and 2) There was still divisive rhetoric and the people who disagreed still found issues and critique with the new analysis.
- So was it a waste of time? No. New perspectives and continued dialogue are needed to move the needle forward.
- Matthew recommends reading “Zen and the Art of Motorcycle Maintenance”
Assistant Professor, Northeastern University
- Michelle and others ran an experiment to measure encoding, recognition, and recall of data visualizations.
- Visualizations that are memorable “at-a-glance” have memorable content.
- Titles and text are key elements in visualization and help recall the message.
- Human recognizable objects (e.g. pictograms) can help with the recognition or recall of a visualization.
- Redundancy helps with visualization recall and understanding.
- The most memorable had visual associations whereas the least memorable had more semantic associations.
- Interestingly, when it came to color, people remembered the segments the color created but not the colors themselves.
- Their data visualization library database is open to your use: http://massvis.mit.edu/
- For more detail on the study check out: http://www.csail.mit.edu/node/2628
- Also, here’s an interesting rebuttal of the findings by Stephen Few: https://www.perceptualedge.com/blog/?p=1770
Filmmaker, Higher Media
- We have a greater emotional response to individuals, not statistics.
- Emotion is not scalable and there’s an upper bound to how much we can care for strangers.
- Stressing how big the numbers are is more important than the number itself.
- How do we bring the humanity back to the numbers?
- The status quo is to focus on individual stores but the flaw is that it doesn’t give us the connection to the bigger picture.
- Hans Rosling does this well and shows that the data visualization itself doesn’t have to be complex to build that emotional connection.
- Think of when you play a note on a piano. You feel something and when you play it for others, they feel it too.
- How do you make data feel big?
- Really think about the best way for someone to feel and understand the story and do the things that’s better. For example, if it would be better if you were presenting it, present it. Create a video.
- Do we feel compelled to be interactive because we can? Is that always the right design?
- We have a tendency to blame misunderstanding or lack of understanding to be a data literacy problem but that’s blaming the audience. As designers, we can push the blame to the user.
Aside from the great speakers, the attendees offered a wealth of knowledge as well. They came from many different industries including government, journalism, academia, and design. By experience every session together, each opportunity for one-on-one conversation were led to really though-provoking reflection and ideas around the several questions that arose throughout the day. While many examples were presented that expressed the speakers points, I found I left the conference with more questions than answers and I think that’s okay. The presentations already sparked new ideas for me and I gathered a lot of helpful thoughts from others too.