16 Data Visualization
Learning Outcomes
By the end of this chapter, learners will:
- Reflect on different factors to consider when preparing a data visualization
- Understand and implement best practices and accessibility when designing a visualization
- Consider how a data dashboard might be useful for OER work and advocacy
Introduction
Data visualization is the presentation of data in a format that uses graphics or imagery. Presenting data visually can help to identify patterns in data, illustrate more complex ideas that are found in data, and summarize key concepts and ideas found in a dataset.
In OER work, data visualization can be a useful tool in advocacy. Summarizing data and presenting it in a visual format can help stakeholders to understand the importance of OER.
Example
There are many different types of data visualization. Examples for OER include:
- Perception of OER from the perspective of instructors or students,
- Student savings from an OER,
- Number of OER used at your institution, based on discipline,
- Outcomes from OER programs,
- Student satisfaction and success with OER.
Preparing for a Data Visualization
There are several factors to consider before creating a data visualization:
- Defining purpose.
- Determining what indicators can be used to support the purpose.
- Determining what metrics or data points are needed.
- Determining which type(s) of visualizations are needed.
Purpose
Consider the purpose of sharing a visualization:
- Who is my audience?
- What is their background knowledge on this topic?
- What is my goal in sharing this data visualization with this audience?
Understanding your audience and your goals will help to shape the visualization. Refer to our discussion of Audiences and Stakeholders for further information.
Example
Indicators and Metrics
Likely, you have collected more data than is needed to meet your goals. Once you have defined your purpose, review your data and analyses to choose which points will help you to achieve your goals. Start by determining the kinds of metrics that would support your goal. Then, select the data points that achieve this.
Example, continued
I surveyed the OER Grant recipients to learn more about the impact of the program. I know my goal is to show the positive impact of the program. Impact can look like:
- Financial impact
- Students impacted
- Impact on learning
In the survey, I included questions to determine how much money students save each year from the OER. I can use this data to show positive financial impact. I also asked the recipients how many students enroll in the course where they will be using OER. I can use this to show how many students are impacted by the Grants each year. I will use these points to make a visualization.
Types of Visualizations
Visualizations can include:
- Graphs
- Pie charts
- Word Cloud
- Dot map
And many more.
Data Visualization Best Practices
According to CARL’s Data Visualization Toolkit (2023), there are several elements to keep in mind when creating a data visualization:
- Consider your audience.
- Select a tool and type of visualization that best conveys your key idea.
- Visually speaking, keep things simple (eg simple colour schemes, avoid 3D imaging).
- Avoid clutter. Be selective of the data you include.
- Data visualizations should always contribute to your goal or point.
- Visualizations should be easy enough to understand that the main point can be identified in approximately 8 seconds.
Tools for Data Visualization
Refer to the Tools and Technologies chapter for a list of data visualization tools.
Accessibility
As a visual medium, there can be challenges with ensuring accessibility. However, there are a few strategies that can be used to improve the accessibility of a data visualization:
- Colour: the colours in a data visualization should not be the only way that a message is conveyed. There should be other ways of understanding the information. If using many colours, ensure there is sufficient contrast between colours and avoid colour combinations like red and green.
- Crowding: make sure that the visualization isn’t too crowded. Having sufficient white space makes the visualization easier to read.
- Labelling: instead of using a legend on a data visualization, label the data directly. For example, if your visualization is a multicoloured pie chart, label each wedge of the chart with an arrow pointing to the wedge instead of using a colour-coded legend on the side. Make sure to use an accessible, sans serif font.
- Titles: an effective title will help readers understand the visualization. Use a descriptive title that highlights the key message of the chart.
- Alt text: all visualizations should include descriptive alt text that highlights the key takeaways and points in the chart (Nussbaumer, 2018).
Using Data Visualizations
The way in which a data visualization is used will ultimately depend on the goals. For example, data visualizations can be used to supplement a report to the Provost to highlight the impact of an OER service. Data visualizations can also be part of a promotional communications campaign, serving as a visual aid in social media posts. Data dashboards can also be a static place on a library website where data is shared.
Data Dashboards
A data dashboard brings together several different visualizations to provide an overall snapshot of an analysis. The goal is to use coordinating visualizations to highlight the impact of something from several perspectives or to tell an overall story. For example, an institution could create an OER Data Dashboard to highlight the impact of an OER service on campus. There could be several visualizations including:
- Cumulative savings to date
- OER by discipline
- Student satisfaction ratings
Examples of Data Visualizations
- ECampus Ontario: this data dashboard brings together several data visualizations that highlight the impact of ECampusOntario’s OER service.
- Open Education Network Data Dashboard: this example from America contains interesting visualizations that analyze OER from a collections perspective.
- Kwantlen Polytechnic University: this example shows how a single institution implements a data dashboard and demonstrates the type of data that can be highlighted.
- Affordable Learning Louisiana: In this example, several different visualizations are used to show the same data. It provides an example of how different visualizations can have different impacts.
Conclusion
Data visualizations can help illustrate key findings about OER impact. They are a useful tool to use for advocacy to help stakeholders better understand OER. Visualizations can take a large variety of forms. Considering a visualization’s goal and audience will ensure that the results are effective. Though they are a visual form, there are still many strategies that can be used to ensure that visualizations are as accessible as possible.
Resources
References
Ambi, A., K. Cushon, T. Gottschalk, J. Hirst, P. Morgan. (2023). CARL Data Visualization Toolkit. Canadian Association of Research Libraries. https://www.carl-abrc.ca/measuring-impact/carl-data-visualization-toolkit/
Bongiovani, E. (2022). Chapter 6. Navigating the Research Lifecycle for the Modern Researcher. Pressbooks. https://pressbooks.pub/researchlifecycle/chapter/data-visualization/
Nussbaumar, Cole. (2018). Accessible data viz is better data viz. Storytelling with Data. https://www.storytellingwithdata.com/blog/2018/6/26/accessible-data-viz-is-better-data-viz