Sensing adds new dimensions to design research and challenges designers to synthesize and decipher an ever-growing array of information.
Consider a typical design activity from the last decade. A team is handed a wicked problem—let’s say they’re asked to design the next generation of the automobile dashboard. After a month or so of preparation, they “enter the field” by visiting the people for whom they’ll be designing. The team tours participants’ homes, discovers what they drive, how they use their cars, what motivates them to take a trip, and what they carry between their home and their car.
When the design team returns to the studio, they begin organizing their many pages of notes and hundreds of photos. They make another few hundred or so observations. Only then, through intense dialogue with their teammates, do they embark on the challenge of truly making sense out of what they saw.
Their workflow looks something like this:
This ethnographic approach is thoughtful, includes a small but valid sample of people, and can be somewhat unpredictable. It has a clearly defined end state constrained by time and money.
Now, imagine adding sensing technology as a research technique into the same process. Many of the activities remain similar. The technology can overcome a host limitations that constrain the face-to-face nature of ethnographic study, including but not limited to: distance, time, attention, social desirability, memory, danger, prediction, and evaluation.
The sensing workflow might look something like this:
Time is one of the biggest differences. The sensing process is well-suited for longer-term studies that are constrained by the time and cost limitations of a typical ethnographic study. Behavioral sensing makes it possible to assess a larger, more disparate audience over a longer period, and to gather comparatively more data points for sensemaking.
To better understand the potential of combining these two modes of research, let’s explore some common steps used to interpret field research findings.
Three Acts of Sensemaking
When a design research team returns from fieldwork, their first order of business is to quickly externalize the journals, notes, worksheets, photos, audio, and video they collected during their interviews. The glut of data produced by sensing technologies may further complicate this initial stage. Sensing data presents a new set of requirements around the act of externalization because its sources are generally computational and so therefore must be read, internalized, interpreted, and then externalized by the designers. The data may also prove deceptive through its quantitative form, which can be tempting to trust over the more subjective, qualitative kind. “There’s a very real danger that ‘the data’ can be seen—internally and by clients—as ‘the unarguable truth,’” says frog Interaction Designer Mayo Nissen.
Once a comprehensive set of data has been gathered and externalized, the team begins to extract meaning by synthesizing it. This is a seminal moment in the design process, often characterized by anxiety brought on by the ambiguous nature of making sense from heterogeneous, subjective information. The process can pivot from tedium to cathartic insight and back again. Here, sensed data may offer the possibility of validating—or invalidating—hunches. It may also amplify meaning drawn from the analysis of fieldwork. “I’m optimistic that sensing will provide legitimacy to some intuitive beliefs that, today, defy simple reason,“ says Stallings.
The final stage of sensemaking includes applying insights to models and stories that reframe the original problem through the infusion of new intelligence. Sensing may provide another vector of data that greatly accelerates the gathering of feedback for an idea. Consider instrumentation, installed in a car during field research, that may be rewired to monitor a later prototype. The design team can learn something about the validity of incremental design iterations before calling an end to the primary research. This type of insight can instill tremendous confidence and refine how more formal prototyping is used later.
A Hybrid Approach
The playful flow of the design process is a deeply human endeavor. We inject our bias and our own experience into the act of making sense of the data, which informs and inspires ideas. A general logic works something on the order of: I saw this + I know this = Design Idea. The reasoning is abductive, a flash of insight that is the product of “the argument to the best explanation,”  as designer and educator Jon Kolko puts it. He goes on:
By comparison, before human eyes see it, data generated from sensing is the work of computers. The result: inductive reasoning. As Kolko says, “sound evidence that something might be true based on structured experience” is the sensemaking logic that generates the first wave of understanding. This forms the basis of any data visualization drawn from sensing. But from this point, human interpretation takes over. If they so choose, the design team may begin another process of making sense by interpreting the visualized data and considering it along with insights from their ethnographic field research.
In a hybrid approach, the continued presence of humans in the synthesis and design process is supported by the added stream of sensed behavioral data. To be sure, both abductive and inductive forms of reasoning — what author Roger Martin calls “the logic(s) of what might be” — are imperfect. But, they greatly support the process of design by drawing on outputs of structured experience and by creating insightful context for new ideas to occur.
Transforming Insight into Design
Prototypes are the most obvious manifestations of insight applied to design. Whether a dashboard mock-up or a smartphone interface, people can touch, feel, and use them. In doing so, people can provide feedback about their experience to the design team and the client.
Prototyping is often characterized as a “rapid” activity because it takes less time to build a model with limited functionality than to build a more fully featured version. But, in traditional design cycles, the prototyping workflow includes making the thing, as well as gathering and incorporating feedback into a design revision. The next version must then be redesigned, re-tested to validate the improvement, and documented.
Sensing holds the promise of reframing the prototype process by moving a product into a perpetual state of design improvement. It gives the design team and client alike the ability to not only test feasibility and implementation, but scalability too. Of this promise, Stallings predicts:
So sensing may provide realtime feedback to a design and catalyze a process of immediate improvements. The yield of sensing—its data rich pie charts, scatter plots, and timestamps—may inform both the preliminary acts of synthesis and the later stages of design. Imagine testing and reformulating a design on the fly, without delay to the results.
The union of ethnographic research with sensing enables us to see parts of lives that may otherwise be too hard to detect. The unique combination of skills needed to solve increasingly complex, system-oriented problems—qualitative, quantitative, technical, analytical, and visual—will define the next generation of design talent. Taken together, these skills and methodologies add fresh knowledge and wisdom to the stories that often inform and inspire design.