This week I tracked personal interactions that I had with family, friends, and my partner through technology. In this case, technology is a phone call, Facetime hangout, and text messages.
My focus was to analyze how I interact with loved ones, and to keep this separate from the technology-enabled interactions that I have at work. Side note – If I was tracking interactions like Slack messages at work, we would likely need a small booklet of papers for the visual.
The interactions are very text message heavy and light on phone calls. I used colors to represent who I was interacting with and the symbol to denote the type of interaction.
I decided to place the symbols on the three lines radiating out, symbolising the interactions radiating out from me, i.e. I was the one initiating the interactions (calling, initiating a Facetime, or sending a text message). Each radiating line has slashes to represent day breaks, starting with Monday as the first day of the week. I chose not to track the specific time as that data point seemed ‘too much’.
In reflection, these three lines with symbols could in reality be anything – watching videos, engaging on online forums, sending tweets, etc. The only thing that ‘makes’ them interactions is the key of the visualisation. This point highlighted the following:
Labeling, understanding the label, and determining value of the data points is key when using a data set like this (i.e. a count) for policy and governance.
For example, there is nothing to denote whether or not these interactions were positive or negative. The symbols and colors only show that they occurred.
In this visualisation, the value is missing of each interaction.
For example, just because I used text messaging the most, does that mean it is the most valuable? Because I only made 3 phone calls, does that indicate that I don’t like phone calls?
My conclusion with this week’s visualisation is that data is just that – data. Without context, it’s difficult to demonstrate value. Conversely, too much context may spill into bias. Culturally, we often try very hard to find labels and categories to fit everything into a neat box, e.g. the box ticking exercise on any standardised test (age, gender, ethnicity, parents income level, etc).
The ironic thing is that life isn’t a neat box. It’s messy. I’d argue education is the same – messy. Education is where you are meant to make mistakes, learn by doing, practice and constantly build upon what you know. The learning process is messy, yet it appears that we are trying to fit it into a box when ‘everything’ becomes data (datafication) for the sake of policy and governance.