My understanding of data has grown substantially from the beginning of this course to the completion of my data project. I never gave much thought to the source of data or if it's subjective or objective. Johanna Drucker states in "Data Basics" that "data does not exist in the world. It is not a form of atomistic information waiting to be counted and sorted like cells in a swab or cars on a highway. Instead, data is made by defining parameters for its creation." Our lessons' texts and videos taught me to think about who is gathering the data and to question their motives. Data is frequently referenced and considered neutral. The fact that data is collected from the real world and individuals, it can't be unbiased, as Manuela Ekowo explains in her blog titled "Why Numbers Can Be Impartial, but Data Can't." A quote by Helen Nissenbaum, a professor at New York University, is accurate when she states that "We talk of data as if it's raw fuel of algorithmic analysis. It's collected with a purpose and not unbiased." I used my iPhone's iOS Screen Time application to track my data for ten days. The application collects and retains data from the past several weeks. My dataset would have varied depending on the days I selected.

 

According to a survey conducted by Deloitte in 2017 of 2,000 consumers in the United States, 91 percent of individuals agree to the terms and conditions without reading them. I was previously in this category, but I now find the contents intriguing. I use Twitter regularly. To discover what data is collected, I had to go to their Privacy Policy via their Terms of Service. I was surprised they collect information from their Direct Messages (DMs) feature, which includes the contents of the messages, the recipients, and the date and time of the messages. Although I don't use the DM feature often, I stopped using it after reading the Privacy Policy. A few weeks ago, Elon Musk tweeted that "Twitter DMs should have end-to-end encryption like Signal, so no one can spy on or hack your messages." That's a change I would like to see made. I wasn't overly concerned about letting my iPhone's iOS Screen Time application collect data for my project. From what I've researched, Apple is one of the better companies in terms of privacy.

 

The Forbes article from 2012, "How Target Figured Out a Teen Girl Was Pregnant Before Her Father Did," was eye-opening. It revealed how utilizing customer purchasing behavior data, a statistician at Target assigned a "pregnancy prediction" score to customers based on the purchase and purchase volume of different products. I understand how this story garnered national attention. Ten years ago, the public wasn't as aware as it is today that data could be used in that way. In the video about how menstrual cycle apps have become big business, the interviewed women found the apps helpful but didn't give much thought to the additional extracted data. I'd imagine they'd be surprised by how their information is used.

 

I found the lessons leading up to the data project to be quite beneficial, from the texts, videos, and discussion threads to the data project peer reviews. I now have a deeper understanding of what data is and its numerous applications. I've also gained a new appreciation for the advantages of effective data visualization. I chose to track the number of daily notifications on my iPhone. I knew I received a substantial amount, but collecting the data, making adjustments to lessen the number of alerts, and visualizing the data on a line graph was exceptionally helpful.