Buckminster (Bucky) Fuller wrote in the 1950s that a strategy of “comprehensive anticipatory design science”  was required to create technology and systems suitable to sustainable living and sustainable business. This post examines what Bucky meant by comprehensive anticipatory design and then explores how Big Data can play a role in its deployment.
Bucky’s vision of design
Good design takes a whole system into account, rather than simply one of the system’s components. For example, a building designer considering the illumination of a room should, instead of simply placing a lamp in a corner, interrogate the room’s solar input and usage pattern before defining a solution. A comprehensive view encourages the designer to think big; e.g., a designer should think about a whole urban transportation system before considering the gears on a bicycle.
Good design takes the long view. Engineers anticipating obsolescence of the gadgets they create can design in recyclability. Building designers envisioning multiple uses of their structures beyond the design-time planned use can design in robustness to change.
Designed systems themselves can function in anticipatory ways through computerized feedback control. In these cases such a system takes data from the environment and uses it to make decisions (anticipating a human action). A simple example is a thermostat that turns on a heater when the temperature in a room drops below a given threshold. A more sophisticated, and yet unrealized, example is a system that monitors the demand on a power plant and adjusts every thermostat in a city to reduce the power plant’s load when needed.
To Bucky, design implied intelligence applied to a problem, a signal in the noise.
By calling it a science, Bucky asserted that realizable universal principles guide good design. Such principles can be discovered, taught, and applied with rigor.
Big Data and design science
Viktor Mayer-Schonberger and Kenneth Cukier define “Big Data” as the “ability of society to harness information in novel ways to produce useful insights or goods and services of significant value”. They speak of our “newfound ability to crunch a vast quantity of information, analyze it instantly, and draw sometimes astonishing conclusions from it” . Big Data represents a shift in thinking toward data-centered decisions and away from theory-centered ones.
Big Data comes from many sources: sensors on products that report conditions to stakeholders, social media connections, crowd-sourced reports, financial transaction records, etc. Particularly, Big Data involves the mixing of multiple modes of such information to derive insights unavailable from considering just one source. The analysis of this data spans from traditional statistical analysis to predictive machine learning. It often relies on significant programmer skill.
Here we connect Big Data to Bucky’s vision of design science to show how the two can act in concert:
Big Data, like design science, is about thinking big. While Big Data refers to working with large datasets (at a previously unavailable scale) and design science refers to holistic system configuration, they share an outlook that seeks complete understanding of a system or situation. In this way Big Data provides a comprehensive information source for the designer.
Big Data often provides higher resolution information to the designer than was previously available, since computationally intensive pre-design analyses can now use all data points rather than just a sample. This enriches the designer’s understanding of the environment they are designing within. In particular, it allows designers to examine outliers and subgroups more effectively.
Insights derived from analysis of Big Data provide better situational awareness. This leads to more effective anticipation of system change, which leads to better system control. Here we envision Big Data analysis not only as a one-time pre-design activity used to inform design, but also as a real-time monitoring strategy that can be incorporated into a design.
An example is Google’s Flu Trends . Google realized they can track the spread of influenza using searches for keywords such as “flu symptoms”, keyed to users’ location. They found their analysis predicted flu outbreak progression faster than government epidemiologists, who relied on doctors’ reports. A designer of a government disease response plan can now incorporate Google’s insight into their design.
Conclusions drawn from pre-design analysis of newly available large and diverse data sets can inform design. System design can be optimized using such data, and simulations of design response to similar data can be examined. Design decisions become more data-centered rather than theory-centered in the presence of Big Data.
The emergence of Big Data provides comprehensive and anticipatory insight to designers at a scale and resolution previously unavailable. Designers who learn to work with Big Data therefore gain advantage.
- Mayer-Schonberger, Victor and Cukier, Kenneth. Big Data: A Revolution That Will Transform How We Live, Work, and Think. 2013. Houghton Mifflin Harcourt.