Information Visualization Manifesto
The purpose of visualization is insight, not pictures”
Ben Shneiderman (1999)
Over the past few months I’ve been talking with many people passionate about Information Visualization who share a sense of saturation over a growing number of frivolous projects. The criticism is slightly different from person to person, but it usually goes along these lines: “It’s just visualization for the sake of visualization”, “It’s just eye-candy”, “They all look the same”.
When Martin Wattenberg and Fernanda Viégas wrote about Vernacular Visualization, in their excellent article on the July-August 2008 edition of interactions magazine, they observed how the last couple of years have witnessed the tipping point of a field that used to be locked away in its academic vault, far from the public eye. The recent outburst of interest for Information Visualization caused a huge number of people to join in, particularly from the design and art community, which in turn lead to many new projects and a sprout of fresh innovation. But with more agents in a system you also have a stronger propensity for things to go wrong.
I don’t tend to be harshly censorial of many of the projects that over-glorify aesthetics over functionality, because I believe they’re part of our continuous growth and maturity as a discipline. They also represent important steps in this long progression for discovery, where we are still trying to understand how we can find new things with the rising amounts of data at our disposal. However, I do feel it’s important to reemphasize the goals of Information Visualization, and at this stage make a clear departure from other parallel, yet distinct practices.
When talking to Stuart Eccles from Made by Many, after one of my lectures in August 2009, the idea of writing a manifesto came up and I quickly decided to write down a list of considerations or requirements, that rapidly took the shape of an Information Visualization Manifesto. Some will consider this insightful and try to follow these principles in their work. Others will still want to pursue their own flamboyant experiments and not abide to any of this. But in case the last option is chosen, the resulting outcome should start being categorized in a different way. And there are many designations that can easily encompass those projects, such as New Media Art, Computer Art, Algorithmic Art, or my favorite and recommended term: Information Art.
Even though a clear divide is necessary, it doesn’t mean that Information Visualization and Information Art cannot coexist. I would even argue they should, since they can learn a lot from each other and cross-pollinate ideas, methods and techniques. In most cases the same dataset can originate two parallel projects, respectively in Information Visualization and Information Art. However, it’s important to bear in mind that the context, audience and goals of each resulting project are intrinsically distinct.
In order for the aspirations of Information Visualization to prevail, here are my 10 directions for any project in this realm:
Form Follows Function
Form doesn’t follow data. Data is incongruent by nature. Form follows a purpose, and in the case of Information Visualization, Form follows Revelation. Take the simplest analogy of a wooden chair. Data represents all the different wooden components (seat, back, legs) that are then assembled according to an ultimate goal: to seat in the case of the chair, or to reveal and disclose in the case of Visualization. Form in both cases arises from the conjunction of the different building blocks, but it never conforms to them. It is only from the problem domain that we can ascertain if a layout may be better suited and easier to understand than others. Independently of the subject, the purpose should always be centered on explanation and unveiling, which in turn leads to discovery and insight.
Start with a Question
“He who is ashamed of asking is afraid of learning”, says a famous Danish proverb. A great quality to anyone doing work in the realm of Information Visualization is to be curious and inquisitive. Every project should start with a question. An inquiry that leads you to discover further insights on the system, and in the process answer questions that weren’t even there in the beginning. This investigation might arise from a personal quest or the specific needs of a client or audience, but you should always have a defined query to drive your work.
Interactivity is Key
As defined by Ben Shneiderman, Stuart K. Card and Jock D. Mackinlay, “Information Visualization is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition”. This well-known statement highlights how interactivity is an integral part of the field’s DNA. Any Information Visualization project should not only facilitate understanding but also the analysis of the data, according to specific use cases and defined goals. By employing interactive techniques, users are able to properly investigate and reshape the layout in order to find appropriate answers to their questions. This capability becomes imperative as the degree of complexity of the portrayed system increases. Visualization should be recognized as a discovery tool.
Cite your Source
Information Visualization, as any other means of conveying information, has the power to lie, to omit, and to be deliberately biased. To avoid any misconception you should always cite your source. If your raw material is a public dataset, the results of a scientific study, or even your own personal data, you should always disclose where it came from, provide a link to it, and if possible, clarify what was used and how it was extracted. By doing so you allow people to review the original source and properly validate its authenticity. It will also bring credibility and integrity to your work. This principle has long been advocated by Edward Tufte and should be widely applied to any project that visually conveys external data.
The power of Narrative
Human beings love stories and storytelling is one of the most successful and powerful ways to learn, discover and disseminate information. Your project should be able to convey a message and easily encapsulate a compelling narrative.
Do not glorify Aesthetics
Aesthetics are an important quality to many Information Visualization projects and a critical enticement at first sight, but it should always be seen as a consequence and never its ultimate goal.
Look for Relevancy
Extracting relevancy in a set of data is one of the hardest pursuits for any machine. This is where natural human abilities such as pattern recognition and parallel processing come in hand. Relevancy is also highly dependent on the final user and the context of interaction. If the relevancy ratio is high it can increase the possibility of comprehension, assimilation and decision-making.
Time is one of the hardest variables to map in any system. It’s also one of the richest. If we consider a social network, we can quickly realize that a snapshot in time would only tell us a bit of information about the community. On the other hand, if time had been properly measured and mapped, it would provide us with a much richer understanding of the changing dynamics of that social group. We should always consider time when our targeted system is affected by its progression.
Aspire for Knowledge
A core ability of Information Visualization is to translate information into knowledge. It’s also to facilitate understanding and aid cognition. Every project should aim at making the system more intelligible and transparent, or find an explicit new insight or pattern within it. It should always provide a polished gem of knowledge. As Jacques Bertin eloquently stated on his Sémiologie Graphique, first published in 1967, “it is the singular characteristic of a good graphic transcription that it alone permits us to evaluate fully the quality of the content of the information”.
Avoid gratuitous visualizations
“Information gently but relentlessly drizzles down on us in an invisible, impalpable electric rain”. This is how physicist Hans Christian von Baeyer starts his book Information: The New Language of Science. To the growing amounts of publicly available data, Information Visualization needs to respond as a cognitive filter, an empowered lens of insight, and should never add more noise to the flow. Don’t assume any visualization is a positive step forward. In the context of Information Visualization, simply conveying data in a visual form, without shedding light on the portrayed subject, or even worst, making it more complex, can only be considered a failure.