The Social Life of Visualization Part 3: Interpretation

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.

The Interpretation Phase

This next stage in the shared storytelling process is being able to interpret the data visualization. The purpose of this stage of the proposed interface design is two fold; users need a way of shifting and reformatting a data visualization so that they can make sense of the whole data set by understanding how it responds to dynamic changes. Users also need to comment on, or draw attention to specific elements of a visualization without compromising legibility of that visualization.

The point of interpretation is that users within a visualization environment can alter a data visualization so that it conforms to their understanding of the data; and thus allows them to have opportunities and tools for making their own sense of the data and consequently make contributions to the shared story.

Gapminder allows a user to dynamically tweak a dataset through the interface of a visualization

Underpinning this process are specific ideas about knowledge management and sensemaking, and how these relate to one another. This process is specifically about providing an interface that enables users to see structure in the data visualization they are working on by ‘tweaking’ it. This is a particular example of information use that defines one of the behaviours of sensemaking – what people do to make sense of the information in their world.


Sensemaking can be described as a process of creating situational awareness and understanding in situations of high complexity and uncertainty in order to make decisions.

Sensemaking arises when we change our place in the world or when the world changes around us. It arises when new problems, opportunities, or tasks present themselves, or when old ones resurface. It involves finding the important structure in a seemingly unstructured situation. It is an activity with cognitive and social dimensions, and has informational, communicational, and computational aspects.

So an important aspect of the interpretation process is implementing an interface that allows users to take part in sensemaking activities.


In the first part of the interpretation phase, users should be able to tweak the visualization parameters, such as when there is a variable that can be changed to something else (eg. When the value of profit margin can be changed to the value of unit cost). Either that or it can be offered to the user when one or more of the visualization parameters is ordered either ascendingly or descendingly (eg. Time, scale, amount, location). Essentially what occurs through the interpretation process is that rather than the visualization becoming a snapshot of the interface, it becomes an interface that allows the dataset to be explored by the user in an interactive and playful manner. This should encourage them to make greater sense of the dataset and uncover insights.

The ability for users to be able to tweak a parameter value and see how it affects a data visualization helps communicate the relationship that the parameter has to the whole visual analysis. This approach can help people see trends and make sense of complex datasets more quickly than with static visualizations.

Visualization to Interface

In order to specifically turn the visualization into an interface, controls should be built into the data visualization interface that enable users to perform actions such as resorting the date, excluding certain parts of the data, or changing a variable that reflects the outcome of the data. This implementation can be achieved through the use of interface objects such as drop down menus, radio buttons, check boxes and sliders.

The only usability issues that exist in implementation of a data visualization as an interface are clearly communicating which parameter is selected, and what visualization element this affects.

Communicating Insights

Once this has been achieved, users need to comment on, draw attention to, or in other words annotate specific elements of a visualization without compromising legibility of that visualization. This ability has been developed out of research into how people collaborate; and into collective intelligence principles that drive the social web. This ability that is built into the interface works on collaboration and collective intelligence principles. Collective intelligence assumes that everyone knows something about the subject they’re contributing to, and that combining all this knowledge together creates an object that contains a better overall presentation of the subject matter than any one person could hope to come up with. However it is a chaotic process due to the differences of opinion that people may have about a subject.

Example of different users' annotations of a visualization in Many Eyes


How can an interface be designed to support this behaviour?

Consequently to promote the annotation process and guard against the chaos that is a byproduct, this works by creating tools within a collaborative visualization interface that give everyone a chance to contribute something to the original visualization, but at the same time try to avoid the chaos that may ensue. This is achieved by preventing users from drawing freehand over the visualization to make their contributions to the process, but instead provides a type of marker that is in keeping with the visualization that was chosen. This once again aids people’s sensemaking processes by providing a common visual language for people to use to work on the visualization, making the transfer of knowledge from person to person easier as well.

The reason for allowing this process to exist within the interface is to promote discussion of visualization details and sub-elements. This can be achieved by giving users a set of drawing, arrow and box tools as can be found in some desktop software, which provides users with a single method of annotating a visualization that is in keeping with the visualization approach used (eg. Such as using highlight bars in a bar chart, or showing the height of ranges in a flow graph). The only issue with this design choice is that non-disruptive annotations limit the types of insight users can show in a visualization, whereas drawing tools might have allowed users to show other patterns and insights in the data.


This part of our series has discussed why its worthwhile to allow users to explore and re-interpret a visualization, and how setting it up as an interface to a dataset allows them to achieve this. We’ve also explained how you can go about designing an interface to support this type of behaviour. In our next article on Johnny Holland we’ll discuss the final stage of the shared storytelling process which we’ve called capture, and is about creating an interface that supports the preservation of insights into the visualization by individual users and allows these to be communicated back to others within the community.


In 2008 the Australasian CRC for Interaction Design (ACID) was approached by Deloitte Digital for their expertise in data visualization which was being developed through the Loupe Project. Deloitte Digital was preparing its accounting firm in Australia for the introduction of XBRL (eXstensible Business Reporting Language) which would see a significant change in the way business reporting was conducted. Rather that sending multiple reports to different agencies, XBRL would produce one set of data that agencies could draw upon for their own purposes when needed. As part of this change, Deloitte has released an online accounting platform called Accounts IQ which will change the relationship between accountant and client to become an ongoing conversation online. This process needs visualization to make complex business data more easy to understand for the client, and an interface to make this conversation process a good user experience. The Social Life of Visualization is the outcome of our research into this solution for Deloitte.

Jeremy Yuille

Jeremy Yuille is an interaction design researcher with ACID at RMIT University. He researchs the effects of social media on different industries, ranging from sport to finance.

Hugh Macdonald

Hugh Macdonald is an interaction design researcher with ACID at RMIT University. He researches the effects of social media on different industries, ranging from sport to finance.

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