The Social Life of Visualization Part 4: The Capture Process

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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.

We’ll be looking at the rationale for including capture as part of our design framework such as its role in knowledge management and promoting a sense of community engagement. We’ll also look at some of the implications for designing it in the way we have, including the limitation of not being able to get an overall sense of the knowledge captured very readily.

What is the purpose of capturing?

The purpose of this stage is that when users are able to interact within the parameters of a pre-existing visualisation, they need to be able to store ‘snapshots’ of the visualisation to be able to save their work and communicate their understanding of a specific visualisation configuration. Through this process the visualization shifts from being an individual pursuit (where a user visualises their own data) to a communal process of looking for inisight and sharing knowledge (where many users can work on a visualization together).

The capture process is an important part of the design framework because it allows users to become citizens of the community surrounding the visualization by making contributions to knowledge. Through this it facilitates knowledge management by storing the insights that users have made within data visualizations for later retrieval. Knowledge management is not a new concept, considering that software vendors like Microsoft, SAP and IBM have been producing technology that enables it for more than a decade. However in that time social software has emerged which has precipitated two significant changes in the field.

How is knowledge management developing?

The first of these is that read/write social platforms like blogs, wikis and other social platforms have made it increasingly easy for users to create content, leading to a significant increase in the amount of knowledge generated, and therefore the amount that needs to be managed.
In 2003, the last time a significant report on the amount of knowledge contained on the Internet was conducted, it was found that:

  • the World Wide Web contained about 17 terabytes of information on its surface
  • instant messaging generated five billion messages a day (or 750 gigabytes)
  • email generated about 400,000 terabytes of new information each year worldwide
  • and the entire Internet generated 532,897 terabytes in electronic flows of new information in 2002.

In 2007, 281 exabytes (i.e. 281,000,000 terabytes) of information was created.

The second change is that the structure of knowledge that needs to be managed is changing radically, given the free form nature of knowledge generation that social spaces like blogs, wikis and social visualization spaces encourage. Consequently new ways of approaching knowledge management are needed, as opposed to simply tagging documents that are contained within a content management system and performing searches based on those tags.

The way in which knowledge is generated is also changing across a number of dimensions.

The first of these is the workplace. No longer is it always a single place for face-to-face interaction but rather, it can sometimes be an anytime, anyplace network of electronically connected spaces. This paradigm is known as the distributed workplace and is emerging as an alternative to the classic co-located scenario. This changes the way knowledge is generated within an organization, because it becomes more asynchronous rather than synchronous.

The second dimension is the approach. As the technologies have emerged to enable it, knowledge generation has taken on a communal approach known as collective intelligence. This is the belief that pooling everyone’s knowledge on a subject together creates a greater depth of information than if one authoritative figure had worked on it alone. Consequently everyone’s contributions create units of knowledge within themselves that it is also important to capture.

Why is capturing knowledge from a data visualization important?

In specifically tying knowledge management back to the shared storytelling process, being able to see what another person saw is an important way of understanding what previous users working on the data visualisation were trying to communicate. The particular way this process is facilitated through the design is also important. The proposed interface allows snapshots to be collected along with discussion, and is a good way to illustrate the evolution of understanding around a dataset. This method allows other users to see individual contributions to see visualizations. It avoids the chaos that might exist if every user’s contributions could be viewed at the same time. Instead it allows a user to use another user’s work as a further exploration and extrapolation of the dataset.

Example of a visualization state in Many Eyes captured and attached to a user comment.

What else is important about the capture process?

The capture process ties other processes within the Social Life of Visualization together because while comments and annotations allow knowledge to be exchanged and to an extent captured, the nature of a visualization means that without seeing what the original user was seeing while they made those comments or annotations, a great deal of the insight that could come out of the process would be lost.

So capturing is the ultimate degree of sharing within the framework, because it shares the community based work that is taking place around the dataset in a visual way. It creates a trail from the initial visualization that really establishes the visualization’s role as a social object within the community by giving it a rich history.

How can an interface be designed to support this?

To specifically design an interface around the capture process, when a user is commenting on a data visualisation they should have the ability to attach a ‘snapshot’ of how they have configured or reconfigured the visualisation at that exact moment in time. They should then be given the option of attaching a text-based comment to the visualization state that they have created.

From here, another user should be able to select a comment that a previous user has made and the interface should work to reconfigure the visualisation to reflect the ‘snapshot’. From here a user should be able to recognise the contribution that the previous user has made to knowledge around the visualization. They should then be able to make a further contribution based on the work of the previous user.

However the real limitation of snapshots is that they do not provide a good overview of the insight that a community has extracted from a visualisation. It is necessary to look through each snapshot and comment to get a sense of what has transpired, when it would also be useful to get a sense of the collective contribution that the community has made through exploration of the dataset.

Conclusion

This concludes our article on the capture process, where we have particularly paid attention to the importance of knowledge management, because the process aims to preserve ideas and insights generated in other parts of the design framework. It also concludes our series on The Social Life of Visualization. For interaction designers, we feel that this is a change in approach towards visualization; no longer is it about making the most visually appealing and sophisticated representations. Instead this creativity should be constrained to giving back people control over the manipulation and control of their data, and providing a good experience along the way.

We have created detailed interaction design patterns for all the phases that we have discussed in this series of four articles. You are welcome to use them to help your own work. You can find out about the interaction design patterns that we have proposed in more detail at our website.

Background

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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