EagerEyes recently proposed a post titled rethinking the user, treating what I think is one of the most interesting open points of InfoVis: who is the user in infovis?

In recent years, information visualization reached a typical maturation state where people start to retrospectively critique the field, highlighting gaps, misconceptions, unexplored areas, false myths, etc. And the question "who is the user?" is in my opinion one of the most relevant, and with most consequences on how we will perceive and develop the field in the future.

As pointed out by Robert in the post, the problem of infovis is that we often design our tools in a way that they are somewhat easy for us to use but extremely difficult for domain-experts:

"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."

Robert suggests also to classify users in two classes: visualization experts, who develop the tool and use it for others and casual users, who do not develop the tool but use it for simple purposes. I think this classification is too gross and that we need to dig deeper into the question. For instance, does a visualization expert necessarily have to be a visualization developer? I don't think so. Does a visualization user necessarily have to be a casual user? Maybe not. And then, after all what do we mean by visualization expert? And who is a casual user? What do they produce and consume?

I started thinking along these lines on how to classify people and tools and here is the result of my reasonings so far.

User types

• Visualization tool designer: a person who actually implements (and often designs) a visualization tool. It is very rare that a visualization developer is not also a visualization designer and thus a visualization expert (most visualization researchers fall in this class).
  
• Visualization designer: a person who actually designs visualizations but does not develop the tool to design them. To this category belong, for example, all the consultants who produce business intelligence visualizations (e.g., dashboards) for companies. Less often, but more frequent now, there are designers who design custom (interactive) visualizations by means of what I call "meta-visualization tools" (see below).
  
• Domain-expert: a person who uses a visualization as a support tool to analyze data in his specific domain (the person I picture myself when I think about the slogan "using visualization to think") (e.g., in business, biology, security, etc.). He uses it to make sense of data, discover new facts, and take informed decisions to act in his own field.
  
• Casual user: a person who uses visualization for short amounts of time without a clear goal, mainly because it's cool or to investigate some data of general interest and discuss about it. Most visualizations found on the web attract this kind of people. A recent trend is to analyze things like census data collaboratively to extract information of common general interest.
  

Visualization types

• Meta-visualization: A visualization tool that permits to create visualizations. It is generic both in the kind of visual techniques that can be generated and the specific domain or data type that can be analyzed. Example:Tableau.

• Generic visualization tool:The tool is general enough to handle data coming from different domains, but a limited set of techniques and interactions are allowed. The degree of generality can vary however. Examples: TimeSearcher (time series), IN-SPIRE (visualization of text corpuses), HCE (biology experiments).

• Domain-specific visualization tool: The tool is designed to support people working in a specific domain. The visualization is optimized to represent relevant data features from the domain and to support custom typical tasks. There are numerous examples from several domains. Examples: Rumint (Network Security), Visual Sciences (Web Analytics), Visual i|o (Business Intelligence).

• Visualization instance: A single screenshot or printed graph to communicate important findings to interested people. A lot of tools, especially in business intelligence or web analytics, provide functions to create ready to print summaries to resume and communicate relevant information.
  

Current practices and limitations
I've designed a table to show what is produced and what is used in visualization. The table links user type to visualization type indicating if the user type mainly uses or mainly produces the corresponding visualization type.

After having segmented users and tools I think it's time to wrap-up and share with you some ideas I have on it.

• A visualization tool designer produces almost all kinds of visualization. Often the problem here is that the designer is not aware for whom he is designing the tool. And this happens especially in research, where people is not enough motivated to ground their research on real-world cases. A meta-visualization should be designed for visualization designers not domain experts! A generic visualization must be tested on specific domains to see if it works equally well in all of them. A domain specific tool cannot avoid involvement from domain experts.

• A visualization designer is often a consultant. It's an interesting figure, and not enough supported in my opinion. A visualization designer is an expert and often knows very well what works and what doesn't in the real world. I have the feeling that current tools are still not flexible enough to support these people. Either the tool is too specific to let the designer invent new techniques or too generic and complex to find solutions in reasonable time. Excel is the current standard for static visualizations. For interactive visualization we still lack a working tool.

• A domain expert can be a person using the tool to find things for himself or for others. As an example, a biologist uses the tool to explore the results of his own experiments, a web-analytics consultant uses the tool to provide findings and, hopefully, suggested solutions for others. It's important to make the difference between the two because the first is supposed to be less expert (and less passionate maybe) in visualization than the second. But still our goal is to be able to support both of them. Too often, in my humble opinion, in research we forget to partner with these people when building something for them. We pretend we are their domain's expert as well as visualization experts.

• A casual user can be anybody. I included this type because I feel it describes well all the people playing with the thousands of little visualizations we can find in Internet. Some tools like Swivel or Many Eyes are useful to discuss and share ideas with visualization. Some others are just curious but nothing more. I suspect that the large majority of these visualizations are used only few times and then are forgotten. Are they useful? I don't know. A good thing about that is that they contribute to the diffusion of interactive visualization among people.
  

What do you think? Am I missing something? Am I getting it right?