A recent forum on Responsible Data Use generated some categories and avenues of inquiry around this topic. I've read through the summary several times now, and with each glance through the list, I find new things that I'd like to discuss. Here are a few that catch my eye:
- How do we communicate uncertainty in data?
- In metadata?
- How do we represent gaps in the data?
- What if our knowledge of the uncertainty in the data is anecdotal?
- How can visuals show “no answer”?
- How can data visualization promote ambiguity?
- How do we improve everyone’s data visualization literacy, as creators and as viewers?
- How do we educate people about the data they create?
- Which people most need data literacy?
- Can we provide interactive tools that let viewers adjust data visualizations in real time as a means of improving literacy?
- How can we support grassroots groups to create better data visualization?
- Is there a need for basic design principles and data viz 101 resources?
- How do we navigate a fear of numbers?
- Is meaningless data visualization worth anything?
- What about when people make decisions based on bad data viz?
- If raw data is unrepresentative, will visualizations on it be bad?
- We should collect examples of unethical data visualization.
- How do we involve the audience?
- Who is the audience, and why?
- How do we create community ownership of a data viz?
- How do we allow a data viz to speak to multiple disparate audiences?
Some of these questions are easier to answer than others---we can think of a few ways to represent a lack of data. Others, like those in the "BAD data viz" group, are not so simple, but would be fun to kick around and see where we get. What would be your priorities in your workplace?
The summary with all of the categories and questions also has links to a variety of resources and notes connected with the forum. They are well worth exploring, if you have a few moments.