Cambrian Analytica is a speculative design project that aims to answer the question: “What would it take for people to care about their personal data?” By using real personal data to generate zoomorphic incarnations of daily behavior, the project seeks to bring attention to the opportunities for self-reflection in a digital age.
The Cambridge Analytica data breach leaked millions of people personal data.
The revelations left me with the question:
“Why don’t people care about the data that is being collected about them?”
Hoping to better understand people’s relationships to their personal data and why we’re so uncaring about what gets collected,
I first set out to explore my own.
I began building a map of the data collected on me.
By requesting an export of the user data collected on me from each and every online service I use.
Seeing the immense amount of data being collected on me was sobering.
Activity data detailing which applications I was using at any given time intruded past what I had expected to find
I came to realize there are no tools built for the analysis of personal data.
Most data collected on you is kept in a loop between companies and marketers.
Perhaps a tool could help foster a relationship between the average citizen and the data they produce.
I began mocking-up a holistic dashboard for a persons daily data exhaust.
In the hope that if the tool was built, curiosity would drive users to explore their own personal data.
But I soon realized, for a person to really care, to form a long-term affiliation with their data, would require a more personal design.
After all, personal data is a reflection of oneself; albeit a crude representation of a complex being.
Humans have an innate instinct to relate to and care for zoomorphic designs.
With this insight I began designing how each of my data streams would make up the anatomy of a new species.
I found inspiration in the diversity of biologist Ernst Haeckel’s illustrations.
The simplistic nature of these ancient beings proved a poetic metaphor for the early stage of personal analytics we find ourselves in today.
Pictured below is an early idea of how the different data streams produced would infer the anatomical dimensions of the representation.
Next, I refined a modular design
that could resize and add or remove aspects of its anatomy to accommodate variances in the data.
Now came the toughest part:
Parsing and unifying data from various services
I used Python to parse through over 20 gigbytes of my personal data.
And the Unity Game Engine to develop the WebGL prototype.
Thousands of lines of code later…
I had a working example of how data could be used to generate zoomorphic representations.
By popular demand I created three different colorways.
For those who would rather not think of themselves as a bug.
Pictured below is a render of how this visualization might exist within the home.
Living on a secondary display, able to recede into the background while providing the user a means of monitoring their own daily data production.