In January 2016, my colleagues and I from an interdisciplinary research group at NYU asked a group of human rights researchers and advocates about the challenges they face when using data visualization. Mixing formal interviews with informal discussion, we found that responses revealed a few common themes. Drawn from this modest sample, here are ten questions on the minds of the rights professionals we spoke to, challenges that complicate and impede the use of data visualization in human rights work.
Respondents wanted to understand the benefits of data visualization and how to get the most out of it — in particular, where and when is data visualization useful? How can we best use visualization for research, and what types of research are best suited for visualization? For instance, what techniques are best suited for qualitative data? What are some ways nonprofits can use visualization to take better advantage of the resources we already have?
Rights researchers and advocates also wanted to better understand the practical process of data visualization. They expressed a need for guidance on the fundamentals of creating data visuals to promote a rights message. Where do we start and what are the basic steps that need to be followed to produce quality visuals? How do we choose which data to use? And on what basis should we choose which chart or format to use? How do we integrate multimedia graphics? How do we start to think visually?
Some questions reached beyond visualization to look at fundamental questions about data collection and analysis, itself. For instance, what is data in the human rights sphere? What can we measure and what is immeasurable? How should we work with flawed, biased, or incomplete data from other sources? What can we do when an organization is interested in visualization but is not confident in data collection and analysis? How do we select the right dataset and establish connections among datasets? What kind of expertise is needed to engage in meaningful data analysis?
Some respondents were excited about data visualization but concerned about quality control. How do we know if we’re doing it right? Are we are using the best visual format? How can we avoid accidentally misleading audiences? How do we evaluate our data visualization?
Once a visualization is correct, how do we use data visualization for effective communication? How do we make it clear and accessible to our audiences? How do we simplify graphics and distill complex information without sacrificing nuance? And how can we make complex data analysis comprehensible to non-expert audiences? Especially those with different orientations — say, funders as opposed to members, staff, or advocacy targets?
Human rights researchers and advocates do not want data to gloss over the humanity of their subjects. For instance, how do we use data visualization to communicate the human costs of illness or poverty when exploring large datasets? How do we create an empathetic connection when analyzing human subjects as numbers? Are there visual ways to avoid compassion fatigue?
In addition to accuracy, respondents want their visualization to be powerful, effective, and convincing. How do we design data visualization to be persuasive and memorable? How do we figure out what is the most impressive format or how to best frame the data in a compelling narrative that catches people’s attention, changes their opinion, and inspires action?
Related to the question of impact, respondents want to know more about the strategic use of data visualization to support advocacy efforts. How should data visualization fit into a more general communication scheme? As part of policy advocacy? Or mobilization? What are some recent rights campaigns that have used data visualization successfully? What can we learn from their successes?
Related to questions about the fundamentals of the data visualization process, some respondents just wanted to know what software is available and how to use it — this group is particularly interested in free or low-cost tools that are also easy to use.
Finally, groups pointed to their lack of capacity: not just confidence in data analysis or visualization, but also time and resources. Where can we find help in using data visualization? And what’s the best way to work with consultants?