Method
A way to analyse and synthesise all information generated from research.
Purpose
- To find patterns across the different types of data that is collected during research.
- To format research data into a way that makes it sortable and organisable.
- To generate research findings and insights from raw data.
What you get
- A collection of data points, or ‘observations’ that can be analysed in its entirety.
- Research findings generated by organising similar or related data into clusters. Each cluster reveals something new about the research topic.
Strengths
- Research observations are consistently documented so they are sortable and organisable (eg. a single idea per post-it note, or per spreadsheet cell).
- A great way to visualise all research data in one place, either on a physical wall, or in a digital space.
- Opens the data to more than one team member, so that research findings are collaboratively generated, allowing different interpretations of the data to be discussed.
Weaknesses
- Usually done with post-it notes, so needs wall space, and can get messy.
- When many people transcribe data to a format like a single post-it note, it’s hard to achieve a consistent level and amount of detail.
- It can be time-consuming to transcribe all relevant data from all research activities. Remember to include secondary research, card sort results, interview observations, verbatim, statistics, etc.
Tips
Affinity mapping can be done alone, but it's better when many people map together. Each person will see new patterns in the data. It’s helpful to talk aloud with colleagues as data is organised into clusters. If you see designers covering walls with post-it notes, this is often what they are doing.
Toolkits and resources
Updated