When demonstrating Parallel Sets^(ref) to guests and visitors, I often use the Titanic data set, because people can relate to it and it is entirely categorical. I like pointing out interesting facts the visualization shows (like that the second class was smaller than the first class), but it's really just a collection of numbers to show what the visualization can do. Some of the people I have shown this to feel different about it, though.

Perhaps the first reaction that made me think of the Titanic as not just numbers was an artist who asked me how I could present such a horrible incident with such lovely colors. Of course I was aware that almost 1500 people died when the Titanic sank, and most of them in horrible ways. But it's also almost 100 years ago now and just so abstract and distant that it never occurred to me to be specifically sensitive about it.

Titanic visualization using parallel sets. Too neat and colorful?

Another incident involved a Titanic buff who pointed out an error in the data: the only girl in the first class did not survive (as our data says), but rather drowned because here parents would not let her get into a life boat without them. This was another thing I was not prepared for, because first of all I assumed the data set to be correct, and second I was only using it to show off a visualization method, why would I care about the correctness of the data?

Titanic visualization using an information graphics style. Easy to read, but every figure represents a number of people. Also perhaps a bit too playful?

Of course, telling a good story about a data set requires understanding and interest. The impact of Hans Rosling's TED talk (which I keep linking because it has made a lot of visualization people rethink why they do what they do) comes as much from his obvious interest in the data (and its relevance) as from his presentation style and gapminder graphics. Would we care that much about a presentation that just used the data to show off the gapminder visualization?

Interesting in this regard is also Swivel's official source program, which allows organizations (like UNESCO, WHO, OECD, etc.) to publish their own data directly on Swivel, providing trustworthy data for people to scrutinize and compare with other data. Swivel's focus is clearly not visualization (and I like to chastise them for that), but the data. Is this a good approach? Not if you're only looking for the best visualization techniques, but in terms of data availability and analysis, it's an absolute necessity to build credibility and a stock of relevant and interesting data sets.

Titanic visualization using an extended pie chart. Each dot represents a person; color, size, and placement describe properties sex, age, and class/survival, respectively.

Ultimately, who cares about a visualization or a particular statistical method to figure something out? The only people who even know about John Snow's use of graphics are visualization people, but I doubt that the people cared whose lives he saved. What counts are the outcomes, not the tools that get you there.

Two presentations in a session about Visual Analytics (by Stephan Diehl and Daniel Keim) presented work that had been based on visualization, but was not in itself visual. Diehl talked about a system for software development that was informed by insights from an exploratory visualization of version control data, and Keim gave a great overview over the problems when trying to "sell" visualization to potential users and funding agencies.

The current view of course is that visualization systems should be designed in a way that is useful to domain experts with little to no knowledge of visualization. In reality, of course these are not the actual users of visualizations: rather, the tools are run by their developers, in communication with the domain experts. Also, the idea of broadly available visualization tools á la Swivel and Many-Eyes presents us with a completely new type of user: the casual (but interested) non- or semi-expert.

So let's face it: we're deluding ourselves with our current user model. A much more realistic taxonomy of users (IMHO) is the following:

• Visualization Experts. We develop the tools, we use them. Simple as that. We use external data, and we communicate with domain experts, but we do not hand the tools over. We know how to read our displays, and so we can make things that are far more advanced and complex than we would expect somebody without experience in visualization to understand.
• Casual Users. The people who actually use visualizations they did not develop themselves are casual users, who are curious about something, or who just like playing with something visual. These users need general (i.e., not data-specific) tools that will be much simpler, and that will need to follow known interaction paradigms as much as possible.