Scope

Concept
Creative Direction
Interaction Design
Coding

Credits

Thomas Fäckl

Cerebro

We conceptualized and developed a software that decyphers and organizes our collected data in a visually appealing and informative way to establish more transparency in the world of Big Data. Our ultimate goal was to help to democratize and decentralizing our stolen data.

Scope

Concept
Creative Direction
Interaction Design
Coding

Credits

Thomas Fäckl

Cerebro

We conceptualized and developed a software that decyphers and organizes our collected data in a visually appealing and informative way to establish more transparency in the world of Big Data. Our ultimate goal was to help to democratize and decentralizing our stolen data.

Data is the new oil.

Predicting the fluctuations in the financial market, developing preventive measures in criminology, winning political elections, improving healthcare, or just predicting a user's next click on an ad. This type of research revolves around collecting, processing and interpreting of (primarily) private data. This industry is called Big Data. To create more transparency in the opaque data market, we conceptualized a software called Cerebro to visualize every accessible data in an informative and captivating way.

Cerebro on Smartphones

Cerebro on Desktop

Screen of »My Data«

Research & Wireframes

To understand the structure of our program, we first had to collect and analyze our data ourselves. After we asked Google, Apple, Facebook, and other tech giants for our collected data, we categorized and deciphered them. This research process was an essential step in understanding the structure of Cerebro. We isolated ten main categories, each with its own subcategories.
Cerebro ultimately visualizes data in two ways: A relatively dry, informative and an explorative, pictorially and ambiguous one. The informative layer is easy to understand and clearly structured, whereas the explorative one visualizes the massive amount of data in one single picture in the form of icons.

Collecting and Categorizing Data

Desktop Wireframes

We developed a grid for the explorative layer, in which each column represents a year and each row a month. The width of a column is defined by the number of data categories (in this instance, ten main categories), whereas the height of a row is always represented by two units. If a specified minimum of data in one category was collected in that month/year, the associated box is filled with an icon. To generate irregularities, a box is not defined by the same data category but by chance, creating a visually more interesting image. The resulting visual should imitate the appearance of old punch cards.