Open cultural data, according to Mia Ridge, is “data from cultural institutions that is made available for use in a machine-readable format under an open license,” and museums have only recently begun to participate in this data trend. And with so many of them now available for open use, data enthusiasts everywhere have been producing amazing results, such as the MoMA study by Helen Wall. Although data analyses and visualizations, no matter how great, will never replace the experience of physically being present in a museum, they can add to the viewers’ experiences by providing educational details and fun facts and by invoking a richer interaction with the art pieces instead of a cursory or disinterested glance by the viewers.
For example, the Color History of the Cooper-Hewitt Collections is a nice data visualization, but it is essentially useless, especially for those who have never seen the collection or been to the museum. However, museum curators can use this visualization or the information extracted from the visualization to make viewers’ experiences more meaningful without distracting their interests away from the actual art pieces.
Furthermore, I agree with Ridge’s argument regarding the usability of open cultural data. I have had first-hand experience in wrestling with ambiguous categories, inconsistent quality of the records, and just the sheer messiness of the dataset when I had the pleasure of working on the University of Pennsylvania’s Schoenberg Database of Manuscripts just last quarter. It was very daunting and overwhelming at the very least. In order to produce higher quality work, museums and other cultural institutions should work on creating better and more usable open cultural data.
Also, I would have to warn against misusing and misrepresenting data. Anyone with the time and skills can conjure up beautiful visualizations. However, with careless data management, analyses can mislead readers into false assumptions which will be detrimental to the museums and the communities. We should take care to let the art piece speak for itself first and allowing the data visualizations to supplement the objects, the artists, and the viewers’ experience.