braindump

Collaborative design Research

design felt lonely.
Creativity requires vulnerability, and designers spend most of their day exposed to scrutiny and interpretation.
Collaboration between designers always yields great results
They’ve helped to bridge the gap between the isolation of the design process and the emotional intensity of collaboration.

As a collaborative tool, critique has been sufficiently analyzed and how-to’d
In the research phase, it’s easier to identify and discuss assumptions.
When you’ve invested in a solution based on one of those unvalidated assumptions, it’s hard to take an honest look at its foundation for cracks. Early collaborative research serves as a safe space to explore assumptions without the specter of sunk cost.
Generating questions and research topics is easier for non-design collaborators.
Nobody is expected to have the answers, so we focus on generating questions instead
It sets the tone for the rest of the project.
Correcting course in the later stages of a project is a monumental task.
Promoting collaboration in the research phase ensures that your collaborators are along for the ride. It sets expectations that you’ll be including them every step of the way, and creates accountability — accountability is one of the biggest positive motivational drivers of collaboration.

How can I give hundreds of people a megaphone for their ideas without the result being completely incomprehensible?
series of range inputs or extremes (hot and cold)
Code the survey like this: the value of the adjective on the left is 0, on the right is 1. The slider starts at neutral, halfway between (0.5). The survey-taker shouldn’t see the exact value of the slider as they move it.1

the easiest is to get the average of the results, a nice democratically-elected result
But averages aren’t the most informative kind of information. You’re rolling all of the data up into a single insight burrito; it’s a nice snack, but it’s hard to tell what all the individual ingredients are.

Variance is, essentially, the level of disagreement between individual data points
The higher the variance is, the less confident you can be that the mean is a good representative of the group’s assessment.
data with the same mean can have a different variance, which is just one example of how data can hide valuable insights.
If you limit your analysis to averages, you’ll lose the individual, human aspect of the data you’ve gathered.

Modality is a measure of how many “groups” there are in a data set
data with the same mean and same variance can have different modality
This happens most often along organizational lines; for instance, the marketing team might see the brand in one light, whereas the engineering team has a different perspective.

sharing the results of the research with the participants.
It’s great to reflect on how individual data points fit into a larger narrative.
Regardless of the form it takes, the aggregated output of your collaborative research is likely to be more insightful than any of its individual data points.
Encourage people who participated to share their reaction to the output: my favorite question is, “Did this surprise you?”