In our last article on Johnny Holland we talked about the ‘interpret’ stage of the Social Life of Visualization. This was where a visualization can be tweaked so that the meaning of the data can be seen in a different way and annotated on so that the individual insights that users create can be displayed. The final stage in the shared storytelling process that will be explored in this article is where the tweaking and annotations made to the visualization are captured so the insights can be communicated to others in the community.

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In our previous article on Johnny we outlined the second stage of The Social Life of Visualization, which was the capture stage. If you missed reading it, it dealt with creating an interface that allowed a user to upload a piece of data, create a visualization that expressed an idea about the underlying dataset, and provide the visualization with an identity so that it can exist within an object-centred social network. This allows other people to join in discussions around it. In this article we outline the philosophies and design implications of the interpretation phases such as the notion of sensemaking. We also outline how people can use a data visualization as an interface to explore and make realizations about their data using interactive techniques like sliders and annotations as they go.

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In our last article on Johnny Holland we provided an overview of what a ’social life of a visualization’ might look like. Based on a person-centered social network, it showed how the identity of the visualization was important, and how having this allowed the underlying data to retain its integrity and facilitated the process of people interacting around it. Its implementation created a shared storytelling experience around visualization, and we broke this up into three phases; create, interpret and capture.  In this second article, we’ll delve more deeply into the creation phase of the ’social life of visualization’; including its rationale and the design challenges that it represents.

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In 2009 we are in the midst of an interesting era for data visualization, particularly as it becomes coupled with the social web. Increasing processing speed, bandwidth and storage capacity are making it relatively simple to render and access visual representations of data. Developers have released libraries of code so we can easily create our own visualizations; and access to all kinds of data is becoming incredibly standardized, particularly through the use of APIs. So as visualization becomes much more straightforward to integrate into online environments, it makes sense to rethink how it can best be used in this setting. … »