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Shared Understanding2:15 with Tomer Sharon
Figuring out what happened during research and what it means is a team effort.
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Creating an Affinity Diagram Sophie Brenny and Freek de Bruijn
Using Affinity Diagrams Arizona Public Health Training Center
An African proverb says, if you wish to go far, go together.
I couldn't agree more, especially when it comes to user research,
analysis, and synthesis.
Figuring out what happened during research and
what it means is a team effort for two excellent reasons.
One, a shared understanding clarifies what
different people in the team learn from research.
Sharing those learning bits increases everyone's understanding.
Two, a shared understanding also means everyone in
the team is committed to what's coming after reasearch is done.
If people agree on what's next, they are more committed to making it happen.
Jeff Patton is visually explaining the concept of
shared understanding in a brilliant way.
Let's apply his concept to making sense out of research data.
First, different team members have different insights from research.
Yet, since they were all involved in research as moderators, observers, or
people who go over the data, they think they agree on what they saw and
what it means.
As soon as each person's understanding is put into the sketch, or
some kind of a written form, everybody understands they were seeing, thinking or
concluding different things.
The team then shapes their various learnings, understanding and
insights through a process of analysis and synthesis.
In the end of the process, the team has reached a shared understanding about what
happened during research and what should be done about it.
Shared understanding is the collective knowledge of the team that builds up
over time as the team works together.
Shared understanding is the currency of user research.
The more a team collectively understand what it's doing and why, the less it
has to depend on second-hand reports and detailed documents to continue its work.
Your key takeaway is to involve as many team members as
possible in research in general, and specifically in
the process of coming to conclusions after data collection has ended.
Next, we'll introduce the first analysis and
synthesis technique, the experience sampling classification.
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