I found these chapter helpful in how they addressed the potential reception of your argumentation structure by readers. The structure should not only be clear, defined and comprehendible, but already take in account the knowledge, interests and views of the audience. Acknowledging the expectations and probable criticisms of the readers helps, trying to look at the claim, reasons and evidence from their point of view, contributes to the fine-tuning of the claim.
I guess in data visualizations, the claim or reasons might not always be clear. And if the claim is clear, we might focus on more on one reason because of how the supporting data looks like. The point is that data is central to the visualization, maybe more than a research paper in which there is space for theoretical foundations and hedging by supporting reasonings and caveats. As has been mentioned in class, sometimes we may visualize data by departing from the data itself whereby the claim follows afterwards – after the visualization shows a pattern, relationship or otherwise remarkable observation. In other words, whereas in written research the claim may be the starting point, in data visualization this starting point is the “evidence” or data with the claim as an important after-thought. That said of course, by showing the data in a certain way, we make choices and assumptions on what we want to communicate (also by leaving certain data out). To what extent is data visualization helped by a clear pre-determined claim to frame the visual? How important is it that readers immediately “see the claim”? Can there be a use case for data visualization that makes an ambiguous claim – to be filled in by the readers’ subjectivity?