this quote is cute in part because he’s taking a nonstandard view of “information.” that is, shannon defined information w/o thinking about the meaning of messages. by reintroducing meaning into the definition of information, he was doing something interesting. but it doesn’t come across in an isolated quote. kate hayles has a nice take on shannon in her books…

the image is also from beer; in “platform for change” he wrote a fair bit about how data should be collected from people… that the government was doing a sloppy job of it and it left people exposed to a host of abuses.

and yes, the book is a collection of his speeches and they are certainly eclectic. but he’s a good read…

So the question remains, how do we go about “turning data into information”? I think one key ingredient that is often missing in data visualizations is a meaningful background so that the data in the foreground has a frame of reference.

I recently went to Design and the Elastic Mind at MoMA and like you, I was also most excited by the data visualization portion of the exhibit. I left feeling reaffirmed that it’s only a matter of time before visualizations become a mainstream way of exploring and absorbing information.

It’s interesting however, that the 2 examples you called out in your post about the exhibit weremap and time-based visualizations.

I often feel a tinge of disappointment with visualizations that aren’t grounded in space or time. They always feel harder to penetrate and therefore less engaging. “There sure are lots of complex relationships going on here,” I think to myself. I’m mesmerized for a few moments by its elegance, but eventually I walk away, unsure of what *information* I was supposed to get out of it.

Visualizations grounded in maps, timelines and calendars on the other hand, always draw me in because the semantic-rich backdrop gives me an easy way to start parsing patterns in the data.

Without this kind of context, it’s difficult to get started on querying the visualization. There are too many axes to take into consideration, where do I begin?

This is not to say that time and space are the only interesting axes with which to build a frame of reference. On the contrary, I wish I knew of more examples of data visualizations that are not built on maps or timelines, and yet are grounded in a semantic space.

I pulled together images of 8 of the visualizations from the exhibit. I’m curious to get other people’s reactions on which ones are most immediately engaging.

Even if I dislike the term “translate”, from a designer/data visualization point of view I found this quote very clever. Thanks for the reminder.

In any event, I would argue that statistics doesn’t “translate” data into information, but rather extracts information already contained in the data.