I know this can be quite controversial. My question is: "should an infovis tool always be easy to use"? I ask this question because I don't see a similar requirement in other areas and also because I believe there is quite some confusion when speaking of "ease of use".
Think about it. Many of the most useful software tools are not necessarily easy to use, nor they have to be. Photoshop is a great piece of software for instance, isn't it? Who would say it is not well designed? It is the result of years and years of careful refinements. And professional users are very happy with it! Is it easy to use? No. The same can be said for Excel ... easy to use? No. Or what about Mathematica? 3D Studio Max? Cubase? Etc. These are all great pieces of software, used by millions of happy users. Are they easy to use? No. What's the point then?
First of all we have to consider that usability is often traded off with utility, which very often plays a central role in the real world. Users are ready to learn and use very complex applications if they see a value in them. This happens every day.
Beyond that, it is important to understand that ease of use might be the wrong focus if not considered on a larger perspective. Two parameters must be added: learning and domain task complexity.
The role of learning
Not all interactive applications must be easy to use from the first time they are encountered. Learning has its role and should be taken into account. One of the most foolish belief I see in visualization is the idea that visualization users should be effective after few minutes of their use. No, no, no! Many visualization techniques are complex by nature and cannot be made any simpler. Learning how to use them cleverly can bring enormous advantages. After all, this is the same way we do millions of activities every day (think about cars, photo cameras, music instruments). The problem with interactive applications is not when they are difficult to use but rather when they are made more difficult than they should.Some months ago I was discussing this topic with a bunch of people at the CHI 2008 conference and somebody (unfortunately I don't remember who was) told me of having assisted to Alfred Inselberg, the ideator of Parallel Coordinates, giving a demonstration of how to use them effectively and being stunned at how rich and powerful the process was if learned; quite complex though.
The best applications are those which permit to perform some simple operations from the first time and little by little offer "hooks" to improve, perform better, and do more. Therefore an infovis designer should not focus on ease of use alone in a vacuum but rather and foremost on how to make it easy to learn.
The role of domain task complexity
Learnability however is not the single thing to take into account, it must be balanced with task complexity. Some visualizations should in fact be easy to use soon, especially in situations where limited amount of time in learning can be invested and, more importantly, when the task at hand is simple in nature. Again, as I said before, the complexity of the tool should mirror the complexity of the task. Simple tasks should be supported by simple software, complex tasks require more reasoning and learning.If we try to use fancy visualizations to help people do simple things, most of the time we fail. Well designed simple bar charts, line graphs, and scatter plots is what users need most of the time, because in many cases tasks are simple. More complex visualizations and interaction schemes are justified when the complexity of the task is higher; the major goals of visualization is to make things simpler not harder.
This reminds me of Stephen Few's talk at InfoVis 2007 (of which I talked about last year in this post) where he classified infovis users in:
- Information consumers and presenters (around 80%)
- Informal data analysts (around 19% and growing)
- Sophisticated data analysts (less than 1% and in need of growth)
They are ordered according to complexity of tasks and thus to the need to provide complex visualizations. Learning and complexity are always interleaved: information consumers and presenters need simple visualizations which require little learning; sophisticated data analysts need much more complex tools and we can correctly assume they have to invest some time to use them proficiently.
