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Data visualisation toolbox

6 How to communicate around visualization


The next and final steps tackle how to communicate around your visualization. To do that, the toolbox will provide some general considerations to keep in mind, as well as specific tips for communicating toward accountability.

6.1 General considerations

First and foremost, it is always essential to tailor communication to the audience. Doing so requires understanding the context of our audience and what their prior understanding of the detail of the context under investigation, as well as their data literacy. From here, you aim to ‘tell a story’ with the data visualizations – when starting from a clear background, what can we tell from the data, and what did we expect that the data did not tell us? Are there any new research questions that arise that the data does not have evidence towards that we should be looking to understand in further data collection?

Through data visualization, you are taking something quantified and communicating it in a visual in the easiest to understand way possible, thereby allowing your audience to remember and comprehend the main points brought forward. Therefore, essential to clearly communicating data visualization are the points mentioned above; use the right graphs and make them clear through reducing the clutter!

6.2 Communication for accountability

Communication of results back to the people you collected data from is essential in the humanitarian and development context(s). Communication of findings is essential for building an understanding and relationship with communities.

While also promoting accountability to affected communities, communication of findings (including through visualization tools) can also be a valuable exercise for triangulation and/or confirmation of interesting or unexpected findings. For example, using visuals produced from quantitative data collection within focus group discussions can be a tool to spur discussion and understand the intricacies of correlations, patterns, or outliers. Therefore, doing so can provide more details on the Why questions associated with the patterns you are seeing in your data, while ensuring community involvement in the quantitative research process.