While creating my first project exploring data visualizations, I had to monitor and record my own behaviors at a closer level than what I’m used to. The process of collecting my own data and assigning numbers to something I perform daily made me feel like some sort of robot or machine. This raised the question of whether the quantified self is truly helpful, or **just a toxic practice.
tools used: Figma, Procreate, Illustrator, Google Sheets, Clockify
instructor: Cathryn Ploehn
course: DES 350 / Advanced Interaction
timeline: 5 weeks
For this project, we were asked to “create a data visualization (print or digital) that represents an aspect of [our] everyday life.”
To get the ball rolling, we sketched our “data body” which helped us come up with potential ideas. I ended up wanting to focus on my productivity levels, since I was having trouble with striking a good work-life balance at the start of the semester. Once I settled on this idea, I came up with the design question that would guide me throughout the process.
<aside> 📢 How might I monitor my daily productivity levels to improve my work ethic?
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This question acted as a reminder of what I hoped to gain from this process. I hoped to boost my overall productivity levels, and I was curious to see how much school interferes with opportunities for self-growth. I hoped to discover some interesting insights into my work-life balance, and use these findings to adjust my work ethic accordingly.
Before creating our own, we were encouraged to better understand data visualization as a medium. We read and watched lectures, as well as created sketch notes on this practice.
This research helped me learn more about the relationships between data and an audience, as well as between data and designers.
After performing this research, I ended up with four main takeaways:
<aside> 💭 Designers influence the way data is perceived and understood
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<aside> 💭 Data visualizations can shape our worldview
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<aside> 💭 The relationship between data and an audience should be at the forefront
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<aside> 💭 Data visualizations are subjective interpretations
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