• Date of Class: 1/22
  • First due: 1/29
  • Comments due: 2/5
  • Revisions due: 2/12


  • Krause, H. (2017, March 27). Data Biographies: Getting to Know Your Data. Global Investigative Journalism Network. https://gijn.org/2017/03/27/data-biographies-getting-to-know-your-data/
  • Thwaites, T. (2011). The Toaster Project: Or a Heroic Attempt to Build a Simple Electric Appliance from Scratch. Princeton Architectural Press.
  • Weingart, S. B. (2019, February 22). The Route of a Text Message, a Love Story. Vice. https://www.vice.com/en_us/article/kzdn8n/the-route-of-a-text-message-a-love-story

For our first discussion, we talked about doing a deep dive. We read the articles “Data Biographies: Getting to Know Your Data” by Heather Krause, and “The Route of a Text Message, a Love Story” by Scott Weingart, as well as Thomas Thwaites’s book “The Toaster Project”. Each of these readings showed us something different about in-depth analysis. The “Data Biographies” piece gave us some background on what is important to know about your data, such as knowing where it came from, why it was collected, and who collected it. “The Toaster Project” and “The Route of a Text Message” were two very different examples of deep dives; one about the inner workings of a toaster, and one about the magic behind a text message.

We started by discussing what is most important to know about your data; the who vs. where vs. when vs. how vs. why, and if any one is more important than another. I also asked the group if knowing how something works is inherently important to using it (like knowing how a texts works to be able to send a text, for example). Talking about the different parts and people needed in sending a text led us to conversations about the people, materials, and money involved in building a toaster.

When discussing the who vs. where vs. when vs. how vs. why of data, we discussed if any one is more important than another. This was a tough question, as it is difficult to think wholistically about data without a complete picture of the data you are dealing with. We found that the “why” of data can you understand possible biases in the data. For example, say Planned Parenthood is gathering data on how many women have had abortions in the last five years to determine whether or not abortion was a popular/common enough procedure for PP to continue to offer. The data collected would be very different from the data that perhaps Christian Mothers Against Abortion would get if they were collecting data on how many women have had abortions to determine how many babies have been murdered in the last five years. The why inevitably produces different data. Questions are different, answers are different, and where, when, and how is likely different as well (in this example the where/when/how is almost definitely different as well).

The discussion of the Toaster Project led us to a discussion about innovation. To innovate something, you have to first be familiar with that thing. Only then can you analyze it and come up with ways to improve it or new wasy to use it. We agreed in the case of everyday technologies, however, that if you are using something as it is, without the intention of innovating it, you should know the basics in how things work, but more generally rather than in depth (knowing exactly how it works is not a completely necessary part of using it). We even touched upon the concept of consumerism when it came to the ease of buying something new versus the time it takes to fix something.