Our understanding after having read Munster’s chapter is that in the 1970s and 80s the corporate and scientific data sets started increasing in size (the number of data/objects being stored) and in the number of fields attributed to those objects. So data needed not only to be stored but also to be arranged in a way in which it could be retrieved more easily. Data mining techniques were developed in the 90s out of those needs.
…data mining began as a largely economic tool and method used by banks and large corporations. Its spread, then, into areas such as security and knowledge resources such as libraries and even ‘humanitarian missions’, signals a more general ‘economization’ of the social. Data mining might be seen as a way in which knowledge is literally mined – that is, turned into a raw material or natural resource – for cultural capital. Munster,Data Undermining
Farther into this piece, Munster begins to define data undermining in a networked art approach. Data undermining can be seen as artistic reactions on the corporate and institutional use of data mining.
From Munster’s explanation of data undermining, we gather that examples of artistic actions or works need to undo the process of data mining in order to not only collect data, but to interact with them in ways that are not predictable. An example of this would be the open source plug-in project entitled ShiftSpace. With this project a user would have the ability to interact and create new content on any webpage. We are unsure at this time whether or not this change would be accessible for the masses or just on the user’s computer.
From our reading of Munster’s chapter on data undermining, we struggled to gain a firm grasp on her topic. She is vague in some areas and highly theoretically in others. She begins the chapter going in depth into a technical and theoretical view point of what she calls data mining and undermining. It takes her several pages to finally come to a clear, concise point on the topic and we, as novices on the topic, finally began to understand her.
Jessica Dahn, Thomas Delle Donne, Beatriz