A certain category of design gaffes can be boiled down to violations of audience expectations.
Our data’s “body” is its form and function. Like the human body, it consists of both overarching similarities and individual differences. Here we can draw an analogy with dislocation: just because your arm twists a certain way does not mean that it is supposed to; on the other hand, some people are indeed flexible enough to move in just that way. When we exceed the limitations of our body, we hear the body’s complaints loudly. But your data won’t let you know when you twist it out of shape (though you may hear some poor statistics professor screaming on its behalf). When we consider our data’s “body,” we’re considering the performance it can achieve and the stresses it can tolerate. That process often begins with these simple questions:
How was it collected? What are its limitations? Which mathematical transformations are appropriate? Which methods of display are appropriate?
Our data’s “soul” is its context and broader meaning. One popular understanding of a soul is that it is some part of an individual that glorifies their uniqueness even while making them a part of a profound commonality. In a mystical context (whether you believe in it or not), we can easily understand this apparent contradiction. In a mundane context, many people would find the dissonance hard to swallow. This is unfortunate, because data must be understood in just this way: it is both an individual entity and a unit of a larger whole. Reconciling these is a notoriously tricky task, but some of the worst mistakes are avoided by collecting a few crucial bits of information:
Who collected it? Why was it collected? What is its context in a broader subject? What is its context in the field of research that created it?