Data Cuisine is a novel program that uses food preparation to make data visualization and interpretation a more palatable and meaningful experience. To learn more about it, Information Management contributing writer Gabriel Hauari spoke with its creators--Susanne Jaschko, the founder of prozessagenten, an organization that collaborates with artists and designers to change the way we see the spaces in which we live and work, and Moritz Stefaner, a data visualization expert.

Putting the finishing touches on data visualization displays.
Putting the finishing touches on data visualization displays.

Information Management: How did Data Cuisine originate? What were you hoping to accomplish with the program?

Susanne Jaschko: In 2011 I was curating a conference for the Helsinki festival Pixelache. The program focused on mapping as a social practice and addressed the ownership of the data that we produce every day. The idea for the Data Cuisine workshop arose from this; I felt that we are generally lacking an emotional attachment to data and should find new ways to look at it.

Moritz Stefaner: We were mainly just curious how we can connect these two worlds: the sensually rich, social experience of eating, and the abstract, cold world of numbers and statistics. To represent data [we use] the inherent qualities of food, such as color, form, texture, smell and taste.

Brainstorming ideas on how to visualize data.
Brainstorming ideas on how to visualize data.

IM: Most people rely primarily on their vision to analyze data. How did you come up with the idea of using taste? How does that help people come to better terms with their data?

Stefaner: Coming from a data visualization background, I am used to working with graphical elements like dots, lines and shapes. But it was quite eye-opening to me to learn how much we can do with food to express information. We have all kinds of sculptural 3D possibilities. We can work with taste, texture and cultural connotations.

Jaschko: The other interesting aspect is the deep emotional connection we have with food. So, while it is maybe a bit more difficult to represent large data sets precisely, using food can create a deep and memorable connection to the data, which can touch us in quite different ways than graphics.

Also, food is very social: We sit around the table, eat and communicate with each other. While data is often said to be abstract and ‘dry’, unemotional and non-tangible, food is exactly the opposite. We can perceive it with all our senses and it creates an intense experience when consuming it.

This makes data visualization a more sensual and exciting experience.

Members of a recent Data Cuisine program put their culinary and data visualization skills to the test.
Members of a recent Data Cuisine program put their culinary and data visualization skills to the test.

IM: What kind of data do you usually assist in visualizing? Is there a limit to what you can do with food instead of a computer or spreadsheet?

Stefaner: Generally, when it comes to tasting precise quantities and differences, of course, our taste organs are more limited than our visual system. It is simply much harder to determine what is ‘twice as sweet’ as opposed to as twice as long or twice as big.

Then again, taste is a much more emotional and temporally complex experience that just looking at a dot on a screen. So, the mechanisms to encode information might be fuzzier, but potentially much deeper.

Illustrating percentages with food types.
Illustrating percentages with food types.

IM: Who attends these workshops and where are they from?

Stefaner: Anyone can attend, from data professionals to regular people. They typically come from the general area the workshop is being held.

Illustrating how Internet sites are accessed and protected.
Illustrating how Internet sites are accessed and protected.

IM: How do people participate? How does that whole process work?

Stefaner: We usually team up with a local organizer - be it a cultural or educational institution, a conference, or a company - who takes care of the PR, registration and all the logistics. There are a lot of little pieces involved in making this workshop happen, so we are very grateful for all the great support we received.

We found that the workshop works best with around 12-15 people, with brainstorming on the first day and a spacious, well-equipped kitchen for the second day.

Using different foods to illustrate data types.
Using different foods to illustrate data types.

IM: Do the workshop participants provide their own data? Where does it come from?

Stefaner: The data should relate to the place where the workshop happens. We like to ‘ground’ the dishes by turning local data into local food. Second, the statistics we use should be striking and relevant, but cannot be terribly complex. So we investigate beforehand which themes and data sets seem promising and interesting, but a lot is actually researched on the fly by our participants.

The classic data graphic - the pie chart.
The classic data graphic - the pie chart.

IM: Can you give some examples of tying food images to data?

Jaschko: A good example would be using a pizza slice to represent a part of a whole. Circular foods that are served in slices, such as pizza, cake, pie, and so forth, are generally well suited to this.


Using colors and amounts of different liquids to represent data percentages.
Using colors and amounts of different liquids to represent data percentages.

IM: What are your plans for the future? Do you have any more workshops planned?

Jaschko: We would like to do the workshop again in various cities. We feel that we just have started to explore the rich possibilities [of using] food to represent data. Food provides such a rich palette to play with, but its exploration needs time and practice.

Stefaner: We are just getting started! Besides the workshop format, we are also thinking of “data dinners” put together with a chef for a small audience.


IM: When someone completes the program what should they leave with in terms of gained insights or skills?

Jaschko: We hope that they will have a closer connection to their data and a better sense of what it means to them.

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