Reading Response – Thai

1. Tufte Reading – Tufte is quite analytical in his approach to picking apart the typical bar graph design. One would think that its pretty hard to mess anything up as simple as a bar graph, but apparently its not. The article is a good reminder for us [as data is ever increasing with magnitude] to be efficient with our designs. I took a class last semester on info.design and found [from first few assignments] that many designers have a natural inclination to be overly decorative when designing information. While some of the observation he makes are arguable, the main thing I will take from this article is to look very critically at every element in a composition.
2. Christiane Paul – I had some reservations about this paper towards the beginning, it may have been the use of the term “meta-narrative.” This implied to me that from raw data elements, one can extract an overarching narrative.  I am not sure if breaking down any web page, hard drive or the internet in to component parts can tell a underlying-narrative. Its definitely a reductionistic point of view of stories/narratives. ex:Breaking a car totally apart might give me a few insights about its nature, but it wouldn’t give me a clue that it drives if I didn’t already know it could. The example of Alice in Wonderland assumes that one has read the story. If you have not read it, the TextArc would have no relevance since there is nothing to compare and contrast. The narrative and its constituent words are structurally coupled. So while its true that without an underlying configuration/set of words no story can be formed, without the story itself no truly meaningful analysis or “database aesthetic form” can be made using TextArc. I do agree that how we go about categorizing, structuring and filtering our data effects/constructs our culture because of value-full choices that are inherent in that process. The project Slippery Traces makes this apparent by making transparent  it’s database structure, but I am unsure of whether the creator’s [Slippery Traces] own values had more to do with results than phenomenon it is trying to shed light on.