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.

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

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.

Tweaking

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.

Conclusion

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.

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.

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.

Guidelines

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

when

• where
• how
• why

There is then a corresponding ‘showing technique’ that equates to each of these problems, making a matrix (see below).

Dan Roam's visual thinking matrix from Back of the Napkin

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.

Goals

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.

Identity of a dataset in Swivel

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.

Conclusion

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.

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.

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.

You will have possibly already come across social networks about visualizations if you’ve ever used IBM’s Many Eyes or Swivel. Now is a great time to expand on the work of these pioneers in the field, considering that there is a great need for data visualization as a way of addressing the problems of information overload and the technology to support it is now falling into place.

Support storytelling

After some extensive research into the area, our take on visualization within a social space is that it should support a shared storytelling process around a data set. By calling it a shared storytelling process, we mean that the process of data visualization starts with an individual with an idea (or intent to tell a story) and ends with a community who share the story and adapt it. An individual who wants to tell a story needs to know how to do it. But then they need to be able to tell the community about it in a way which lets everyone understand what is going on. Communities like to be able to pass stories around to be able to entertain and educate, but without forgetting what the story was originally about.

…our take on visualization within a social space is that it should support a shared storytelling process around a data set.

visualization

This shared storytelling process can be supported by designing an appropriate web-based interface. In this article we’ll be talking about some ‘big picture’ ideas that have become design implications in our process of conceptualizing this interface. In subsequent articles we’ll be delving into these ideas in more detail and presenting excerpts from the interaction design patterns we have collected to construct the experience of using visualization within a social environment.

Rather than a medium for storytelling, visualization has traditionally been a tool for the analysis of data, and the aforementioned Many Eyes and Swivel have extended its use to the social web in different ways: Many Eyes has focused its attention on supporting the analysis of data through the visualization in a collaborative environment. The site achieves this by using an interface that supports transforming the data to change the look of the visualization, annotation that keeps to the style being used and the ability to save any view being worked on and add a comment to it.

Rather than a medium for storytelling, data visualization has traditionally been a tool for the analysis of data

bringing it to life with Flickr photos

Swivel takes a different tact and prioritizes the idea of using visual communication to express the meaning of the data. It achieves this by using the assigned title of the data set to query Flickr and this produces a set of images that should visually define what the data set is about. This potentially helps to create a wider understanding of what the data is, and how it might interest people within the Swivel community who are looking to contribute. But Swivel doesn’t support much community based analysis of data. Visualizations are presented in a very standard manner (using bar charts, pie charts and scatter plots) and can be commented on but not annotated or manipulated.

Support an holistic process

What we’re looking to do is extend some of the functionality of these sites to support a more holistic process that better supports social activities like collective intelligence and sensemaking as a visualisation is created and reiterated.  We’ve conceptualised this as a three stage process that begins with creation, extends to interpretation and ends with capturing and reintegrating these interpretations back into the conversation.

• The creation process is an individual process;
• Interpretation is one that belongs to the community;
• Capturing saves the process for posterity and allows it to iterate.

For now we’re going to talk about the theory of object centered sociality and how it holds these three different processes together. In subsequent articles we’ll talk more about the importance of the individual processes and how interaction designers can use them when they want to get the most out visualization in a social web setting.

The basis of capturing this shared storytelling experience through visualization is creating an object-centered social network in a similar style to Flickr, where interactions occur around the photos that users share (as opposed to an ego-centric one such as Twitter where users ‘follow’ other users and receive their updates) that enables it. To do this the visualization needs to become a social object within the network. This essentially means that the interface elements used to represent a visualization will afford discussion, or contain things for people to talk about. We can create a visualization as a social object by giving it an identity and making it interactive. We’ll talk about why identity is important in a moment, and come back to interactivity after that.

The basis of capturing this shared storytelling experience through visualization is creating an object that affords discussion

Social begins

Before identity comes into play, the visualisation needs to be created. This is more imperative within a social network because the creator of the visualization needs to understand their own data, and other people need to understand it as well. So we need to understand how data is best visualised. This can be difficult to understand at the best of times, but focusing on the communication goals and intended audience is often the best way to start. This prevents users from needing intricate knowledge of various visualization techniques such as the different properties of a box plot and a scattergraph. Foregrounding these aspects of the process we refer to as MAPPING in the diagram above helps people to focus on the answer they are seeking from the data rather than being bogged down in the process of presenting the visualization correctly.  Once this is achieved then social processes around the visualisation can begin.

But objects can’t exist on their own within the social network and need to have identities built around them. Identity of an object can be implemented just like identity of a person within an ego-centred social network. You can see in the image above that the visualisation on Swivel has an image, a source, categories and it could have tags. These all work together to provide an sense of identity and give people within the social network some chance of being able to work out what the visualisation is about. Along with identity comes reputation and history. As different users interact with the visualisation and make contributions to it, its identity remains unchanged. Adding an avatar provides visual clues about the identity of the visualisation that may not be communicated through the text associated with it.

Back to interactivity

Now we come back to interactivity. In order for social processes within the network to have any real value, then the visualisation should be one that can be manipulated. This allows for further insights to be drawn out of the data that may have been missed when the original user chose to visualise it. So it is not enough to be able to leave comments on the visualisation, which is the standard way that content is treated within most social networks. Users need to be able to tweak a visualization, from switching axes on a line graph, to filtering or bringing in new data, or even changing the type of visualization used.

Capture the process

This process also needs to be captured. Users should be able to highlight parts of the visualization that represent interesting insights into the underlying data set. They should then be able to store these as an attachment on the original visualization that doesn’t in any way detract from what the original visualization represented. In this way the shared storytelling process we talked about earlier is at play. If the visualization is the story, then as it is passed from person to person they choose to promote different parts of the story and neglect others.

Capture insights

Because the visualization has an identity, its original form is recognizable through this process and the subsequent retellings of it become versions of this original that make up a bigger narrative about the issue, or in this case the original data set. This process would probably occur without the interface in place to support it – everyone sees things differently, and everyone expresses the way they see things differently. But with an interface in place to support the processes of an object-centered social network, we can capture the insight that the storytelling process brings to visualization and store it as an artifact that creates greater knowledge about the original subject.

The power of data

Considering that sites like data.gov are launching soon, and technology guru Tim O’Reilly has been saying for a number of years that data will be the ‘Intel inside’ for the web operating system the amount of data that is available for people to manipulate is going to increase exponentially. Visualization has always been a good way of gaining insight into data, and creating a storytelling experience around it feeds into human needs to ask questions and tell stories. So for interaction designers, implementing this type of experience within the data-driven websites of the future has a number of benefits. One of these is that the particular website becomes more engaging for users, and drives take-up. Another that springs to mind is that by making this an interesting and engaging experience, much insight can be drawn out of the data which can be of benefit to the community (in a similar way to Wikipedia in its collation of vast amounts of information) and to the owners of the data, who may discover things about their data they’d never thought of before. So watch out for more articles from us in the future, as we go into further detail about how to implement this experience.

Background

In 2008 the Australasian CRC for Interaction Design (ACID) partnered with Deloitte Digital to research applications of data visualization, 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 than sending multiple reports to different agencies, XBRL would produce one set of data that agencies could draw upon for their own purposes as needed. As part of this change, Deloitte has released an online accounting platform and aims to 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 process a better user experience. The Social Life of Visualization is one outcome of our research into this solution with Deloitte.