In this linking assignment, I’m going to make connections and analyze differences between the reflections my classmate, Tanya Groetchen, and I did for Task 9 – Network Assignment.

I found that Tanya did a good job at organizing the data to read it more clearly. She provided screenshots of how she moved around the nodes, separating the music tracks from the curators. This was a good way of separating the two different kinds of data from the graph. The size of the nods in curators doesn’t represent any information, however, for the music pieces, it represents how many times these were selected. Although simple, I think this was a very skillful approach and made the reading of data much easier. I didn’t use this strategy and see now how it could have saved me some time in data interpretation.

This makes me think about how important data organization is and how agency is needed for such a task. This could easily have been done with artificial intelligence but it seems like that function is not featured in Palladio. It’s interesting to contemplate how computers are crucial for the assembly and reading of data. Without such agency, how we read and find data today would be very difficult. Search engines and data analyzers precisely organize massive content in ways that make our life easier. However, as we’ve learned from this task, the computerized assembly of data can only go so far. This data has no way of providing information about what were the implications behind the patterns in the curation, and this is where we have to be extremely careful because we can make wrong assumptions. Does the fact that the Fifth Symphony by Beethoven was the most popular track mean that everyone likes classical music? Or does the fact that Johnny B.Goode was also among the most popular tracks means that most of us would enjoy ourselves in a rock n’ roll concert? Or maybe it shows that it is very likely that we’ve all seen Back to Future? Like this, we can ask lots of questions without a clear conclusion. I don’t think this is something negative – quite the contrary. Data can stimulate our minds to build assumptions through which we can question beliefs and expose bias. In this example that might not be of huge significance, but in other contexts, it could be a great way of learning about assumptions we make on race, gender, and culture. This exposure can help us to remove judgments, discrimination, and unethical practices.

Both Tanya and I explored the issue of the superficiality of the data, concluding that any reasoning on the motives behind the curation remains at a speculative level. To understand the deeper dimensions of why individuals choose certain things more data is required. I think that human behavior is so complex that to understand the mechanics of choice profoundly, we would probably need more data of a qualitative nature. I find it fascinating how, as we go deeper into the motives behind choices, at a certain point, there is most likely a shift from the individual to the collective, meaning that the thought which produced a choice might be only an impersonal thought that comes from a cultural heritage. In that sense, data can also provide us with insightful information about culture and groups of individuals. I appreciate doing this data analysis task, as it has helped me to understand the necessity of being careful while interpreting data, and also the capacity data has to storage individual and cultural patterns.

I am left with an intriguing question: is our personal makeup a complex set of data? Maybe this is why when we lack profundity in our ways of viewing people, we only see superficial data and make assumptions and wrong judgments. If we were to de-compose the data of an individual to the point that it is seen as a cultural construction, maybe we could see the essential ‘blank canvas’ of an individual (essence) while understanding that we are the same. This might be the way to undo isolation, alienation, hatred, fear – and find love.