Opportunity Framing
You've run discovery and come out with a pile of notes, quotes, and half-formed hunches. That pile isn't a decision - and left alone, it quietly becomes one anyway, made by whoever argues loudest. This module is the disciplined path from raw signals to a clear, defensible bet the whole team can get behind.
Learn on the move: press play and turn your commute, walk or drive into time that grows your design career.
Listen to the full Opportunity Framing course on DesignPath
You'll take the mess apart to find the patterns that hold up, name the opportunity hiding inside them, pressure-test it, and write it down so plainly that anyone can repeat it from memory.
Analyzing Discovery Data
Analysis is the first move from signals to a bet - the work that turns a pile of raw notes into patterns you can act on. It isn't magic or intuition; it's a concrete process, and here are the four steps.
Step 1: Break Notes Into Single Observations
Go through your notes and put each separate observation on its own card - physical sticky or a digital board. One card holds one thing someone said or did. Keep the exact words. "I dread Monday reporting" stays as written; don't tidy it into "dislikes reporting," because the tidy version quietly deletes the feeling.
Step 2: Cluster by Affinity
Move similar cards together into groups, and let the groups form from the bottom up - don't start from categories you decided in advance. When a cluster takes shape, name it in plain language: "re-entering the same data in three tools." This is affinity mapping, and the point is that the themes emerge from the evidence, not from your assumptions.
Step 3: Count and Weigh Each Cluster
For every cluster, mark two numbers:
Frequency:how many different people mentioned it. Five people across five separate interviews is a real pattern; one person saying it five times is one person.
Intensity:how much it hurts - a mild annoyance, or a Monday-ruining ordeal.
A cluster that's both frequent and intense is a strong signal. One that's rare or mild is a lead, not a finding. This is how you stop the single loudest interview from hijacking the whole direction.
Step 4: Pull Out Jobs, Pains, and Gaps
For your strongest clusters, write out the same three things you listened for in interviews - the job, the pain, and the gap - now backed by the weight of the whole data set:
- Job: "keep clients updated on progress"
- Pain: "the data lives in three tools that don't talk"
- Gap: the distance between the two
These three - in the user's own words - are the raw material you'll shape into an opportunity.
Pro tip: Do this with the product trio in the room - PM, designer, engineer clustering the same cards together. You skip the argument three weeks later about "what the research actually said," because everyone watched the pattern form.
Where AI Helps
Prompt: "Here are my raw discovery notes: [paste]. Break them into single observations, cluster them into named themes, and for each theme count how many separate people mentioned it. Keep the exact quotes."
AI clusters fast; you keep the sharp words.It's genuinely good at grouping and counting a messy pile, but it tends to smooth vivid quotes into bland labels. Check its clusters against the raw notes and put the real phrases back - the sharp phrase is the one you'll use later.
