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.
The creation phase – choosing the right tool
In the creation stage of the shared storytelling experience, the initial dataset is presented as a visualization. The problem that needs to be overcome is that people aren’t generally well versed in presenting information visually. So the purpose of this process is to help people to decide how to visualize their data and communicate the meaning of their data to the online community without resorting to text.
While visualisation can be an ideal medium for people to tell stories about their data, the problem is that they don’t necessarily know the best visualisation technique (and by this we mean box plot, bar chart, scatter plot etc.) to use that adequately communicates what their data is about. While this is a problem that exists for the individual person when they are trying to gain some individual insight into their data, it is especially problematic when data visualization is being introduced into a social network. This is because other people need to be able to interact with the visualization, continue the shared storytelling process and add more knowledge to what was contained in the initial visualization.
So an integral part of building any interface that supports a social network for data visualization has to be including a tool that helps people to better understand the techniques they should use to visualize data. Enabling this allows them to focus on the type of story they want to tell through the data visualization, rather than becoming preoccupied with how to tell the story. So the intention of the first part of the creation process is to helps people to visualize their data so that other people within a social network can understand its intention and interact with it accordingly.
The theory behind this comes from the work of several important figures in the field of data visualization and visual thinking: Tableau Software CEO Christian Chabot, ‘Back of the Napkin’ author Dan Roam, and noted visualization expert Stephen Few.
In his keynote address at InfoVis 2008, Chabot presented five flawed principles of data visualization. Some of these are considerations for the way this particular aspect of the interface should be designed, or in particular, what it has to achieve to help users. The first of these flawed principles is that people adopt visual analytics primarily to help them see and understand new visual paradigms. The answer to this is that most people’s needs can be solved with tried and tested visualizations such as bar charts, line graphs and scatterplots.
Dan Roam’s work in The Back of the Napkin introduces a simple and straightforward methodology for visual thinking and problem solving. So some of what he talks about provides the basis for the reasons why a person might choose a particular visualization approach. Roam’s approach is to begin by thinking about what sort of question needs to be asked of the data. These ‘problems’ are clumped into those that involve:
- who and what
- how much
There is then a corresponding ‘showing technique’ that equates to each of these problems, making a matrix (see below).
There are only six of these techniques, one for each type of problem, and as Roam points out in the book, that is all that is needed. All visualization techniques are derived from the portrait, chart, map, timeline, flowchart and multiple-variable plot visualization approaches that he uses. His take is that other visualisation techniques are great but they are not necessary in this type of application.
This fits in with Christian Chabot’s second flawed principle of data visualisation; that people adopt visual analytics primarily to help them see and understand massive data. The truth is that people want to better understand small datasets more readily than large ones, and so complex visualization techniques are unnecessary for this. His fourth flawed principle is that people adopt visual analytics primarily to help them see and understand hidden insights. However the real reason that people employ visualisation techniques on their data is to save time.
Complicating this need of people is that according to Stephen Few is that they are still struggling to achieve simple tasks because existing visualisation tools complicate the task of making sense of data and effectively presenting it to others.
However the key to the creation process is to help people determine their analysis or communication goals and then suggest a visualization approach that maps most closely onto their stated objectives and is appropriate for their dataset.This is instead of forcing people to concentrate on learning the merits of different visualization approaches, and rather it helps people to focus on what they already know about their data and the context they want to present it in. This can be achieved by attempting to determine the communication or analysis goals the person has for their data visualization, including; who they will be sharing the visualization with, what kind of data they will be visualizing, and what outcomes they want the visualization to create.
Based on these factors, it is proposed that the interface would suggest a visualization approach for the data, explaining to the person why that approach is best suited to their goals. Along with this a range of other visualization approaches should be presented to the person, stressing their individual strengths and weaknesses.
Through this process, the person is required to have a good understanding of the original data to be able to choose an appropriate visualization approach that communicates the dataset in the visual medium.
How can a visualization become ‘social’?
As we discussed in our previous article, setting up the social space as an object-centered social network (e.g. Flickr) establishes the visualization as the object that interactions occur around. So while this explains why interaction will occur, it doesn’t necessarily encourage it. On the other hand, giving a visualization an identity makes it recognizable and approachable within the social space, and consequently does promote interactions.
Firstly, to understand the importance of identity to an object, consider its importance to a person within a social network; it is a way of uniquely identifying that person within the social space. It is also the most basic requirement of any social space. However social spaces aren’t always built around people. To refresh the idea of object centered sociality that we’ve discussed previously, it is an alternative to the idea that a social network is a map of relationships, and instead says that people within a social space are connected through the existence of an object. The object centered sociality theory suggests that when it becomes easy to create a digital instance of an object, the online services for networking on, through and around the object will emerge too.
How can users make their visualisations social?
Therefore just as people within social networks have identities so they can be uniquely identified, objects need identities as well. Social spaces have ways of creating these for people. One of these ways is through the creation of a profile that allows the person to provide specific information about themselves that would give other people on the system some idea about the identity of that particular person. Often used in conjunction with a profile is a profile picture or avatar that provides the particular person with a visual identity within the system. This gives other people on the system extra information about the particular person that can only be conveyed in visual form.
When visualizations are objects within a social system, they can have identities attached to them as well. A visualization’s identity is created by the title given to it by its creator, its description and the content of original data set as well. However, a visualization can also have visual imagery or an avatar attached to it, in order to more clearly communicate its identity within the social space.
This serves an important purpose for those people within the space that did not create the visualization, in that it adds an extra layer of identity to an object that they have no pre-existing familiarity with. Specifically it helps them to make sense of the visualization, which aids any collaboration which may occur around the object. For the creator of the visualization, it helps them to make sense of what they have created by thinking about its identity and what sort of iconography they might attach to it. An avatar also contextualizes the visualization’s place within a social space. In turn, this objectifies it and allows it to exist on its own within the social environment. It also reduces the cognitive load on other people, and allows the inherent meaning in the visualization to be communicated and consequently transferred to the community with greater ease.
The process can be achieved by integrating with the search APIs of person generated content communities to access images and media that relate to the content of the visualization. The only issue that arises from this part of the creation process is that assigning absolute meaning to media can be tricky, and often fails to communicate effectively across different cultures. People can ‘read’ images and media very differently.
Ultimately the creation phase is the most important in ‘the social life of visualization’ because it is where an individual idea or question about a dataset can be transformed into an object for social interaction. So this process needs to help people who aren’t visual thinkers to make that jump and set up the visualization as an object so that interactions can occur around it. It leads into the next stage which is interpret, where the interface should act so that people can drag further insight out of the data. We’ll talk about this in a lot more detail in the next article.
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.