Insights to Stories

Shared on
October 3, 2019
Qualitative Research

An explanation of raw vs. interpreted qualitative data. Also, a discussion about whether or not we *really* have to choose between #jtbd vs. user stories.

Raw data:

Data collected during research without any synthesizing or modifications. Examples include: verbatim notes, audio and video recordings, photos, screenshots, or collected artifact.

Interpreted data:

The designer has started the process of synthesis. Finding commonalities, creating themes, making assumptions, generalizations, and developing insights against the research data as an attempt to meaning out of ambiguity.

Some examples of interpreted data include:

✨ Insights: People [what we observed/heard] because [underlying need, motivation], but [what we believe to be true/kernel of tension].

And then there are The Stories:

✨Jobs-to-be-done: When I [scenario], I want [underlying need, motivation], so that [desired outcome.]

✨User stories: As a [User], I want to [action] so that [desired outcome].

Insights, #jtbd, & user stories compliment each other quite nicely. Add them your toolkit and pull them out when it makes the most sense for where you are in the design process. ✨

Interpretation (Insight) → Motivation (Job Story) → Implementation (User Story)

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