Module 4.1: Defining Data Visualization
Last updated
Last updated
In this module, we define data visualization, think through the value in visualizing data and the power that comes with that.
With this module, we connect you to an introduction to data visualization module created by the Project Team. This IMLS-funded project developed a literacy-based instructional and research agenda for library and information professionals with the aim to create a community of praxis focused on data visualization.
What is data visualization?
When is the appropriate time to visualize data?
How does the audience type impact our choices in designing data visualization?
The librarian is preparing an annual report on library computer and WiFi use. She is responsible for creating visualizations from these library datasets, but is uncertain about what type of visualization would be best for the data and for the community. After considering different approaches to data visualization through a critical, contextual lens, the librarian designs the ideal visualization for the data.
Overview: The most effective visualizations are built based on a strong definition of the target audience. This activity tears down an existing visualization and lets participants remix it for a different audience. This increases understanding of the interests of various audiences. The activity emphasizes that you need to target information differently for different audiences. Participants get to practice crafting a compelling message, and gain experience presenting data in a variety of forms.
This activity should be done in a group.
Supplies:
An example visualization
Big paper or white boards
Markers or crayons
Tape, if working on big paper
Time: 45 minutes
Step 1: Exploring an example visualization (10 minutes)
The facilitator should begin by introducing an example visualization to the group. As a group, talk through the data represented, the audience that the visualization targets, and the visualization’s goals.
Step 2: Brainstorm other audiences for data visualization (10 minutes)
Visualizations are created with an audience and purpose in mind. What are other audiences to reach through a visualization of the same dataset?
As a group, make a list of potential audiences, such as:
educators
University students
voters
people who disagree with the perspective in original visualization
folks who agree, but need motivation to care
policy makers
people within the system the data is about
Step 3: Remix the visualization (20-30 minutes)
Split into groups of three, with each group assigned an audience. With large paper or a white board, rework the original visualization with the assigned audience in mind. Write down the audience on your paper or white board.
“Think aloud” as a group about your design decisions and the audience-tailored framing. For example:
Will you carry over any of the design elements of the original visualization?
Use text or color differently?
Step 4: Gallery Walk and Share-out (10 minutes)
If working on large paper, hang up the visualizations. Participants can spend time touring the visualizations, observing the differences and similarities among the visualizations.
Reconvene for discussion. Share the design decisions participants made based on your understanding and assumptions about each audience.
This activity is adapted from the MIT Center for Civic Media’s Data Therapy project. The original activity is available on the Data Therapy .
The facilitator should bring a visualization that participants will remix. The Visualizing the Future project team is building a and, of course, there are daily examples in news outlets!
Use a different type of visualization? There are many data visualization types and, depending on the purpose of your visualization, one type may be a better choice than another. For this exercise, you don’t need to be an expert on data visualization types, but your team can begin to explore them using resources like: the , and the .
