In doing this week’s readings, Mia Ridge’s point about the tension between the utilization of “easy-to-use datasets using common vocabularies” and “more sophisticated data structures and specialised vocabularies” stood out to me. I spent this past Sunday doing a “communication progression” as part of staff training on the UCLA challenge course (an on-campus space dedicated to experiential education). As part of this training, I participated in a series of activities with each building upon the last, that each had a different takeaway regarding communication methods and drawbacks. In two of the challenges, one group of people had an object (a multicolored lego structure in one, a complicated pvc pipe sculpture in the other) that they had to get another group to replicate, without being able to show the other group the object and without the ability to see the building groups’ attempts. The final results of our efforts (the comparison of the two structures at the end) provided a visual representation of the amount of information that was lost along the chain of conveyance. We recognized the need for establishing a common language, as many of the discrepancies occurred as a result of differences in explanation and understanding amongst different people (e.g. in trying to convey length of pipe, metric system versus customary system provided a discrepancy in the pipes chosen).
All this to say that once a widely-used common language has been established among those who practice data visualization, the graphs and charts themselves can act as a powerful common language for understanding museums and collections. With a properly done graphic, anyone from any walk of life or level of understanding should be able to look at it and gather what the creator was attempting to convey. I see this as the purpose of data visualization itself– it takes a trained eye to make sense of raw data, but visualizations transform that data into a universally accessible format. Such methods aide in transparency and public engagement, enforcing the openness of “open cultural data,” and the purpose of integrating technology into the museum sphere.
Very true! Although, as we’ll discuss a bit in class, it’s often striking how much information gets lost in a data visualization, even though you get a much more accurate bird’s eye view.
I must also wonder if that difference in communication and language, although damaging to the initial piece, may actually create newer ideas. Without differences, nothing new can be created or interpreted. Just as a set of common terms cannot be applied to another project if they do not fit to perfection. Indeed just how they should fit is open to individual interpretation.
I can definitely see how vital information can be both lost and gained with the use of data visualizations. It’s odd though, how in most museums I’ve experienced (thus far) don’t really showcase data visualization and only provide textual information to enhance visitors’ understanding of pieces. Communication for sure plays a part in this, and I feel that museums should communicate data to visitors through various means representations, which although may seem “unnecessary” to some, may benefit many others’ experiences.
I think there is also a pushback in what data museums want to openly display. Just because the museum may have opened their data to the public doesn’t mean they want any of that information on display in the museum. That is a huge factor in the communication between museum and visitor. It seems very similar to the tension between what history is constructed in the exhibit. What data does the museum want on the public’s conscience while they are actively engaging in the exhibits?
I really like the comparison you drew of a common language to data interpretation. As someone who tends to not be very familiar with data at all, that “common language” in terms of visualization becomes increasingly important for me and your relation to your training this weekend was spot on. Great connection!