Affinity Diagramming
A data analysis technique where the researcher(s) jot down the data gathered in bits, on sticky notes and categorize them, generating insights from each category. Affinity diagramming is an inductive bottom up approach.
“Affinity diagrams and consolidated work models show how individual examples of work practice are instances of overarching patterns that define the whole population, and they provide concrete representations of those patterns. ”
Nature & context
Data Analysis
Resources
Sticky notes, all research notes, data items (generated from research notes), whiteboard/wall/table to categorize the sticky notes, marker.
Procedure
Before: Segregate data generated through all research methods. Create data points on sticky notes, each of which are of value or convey an insight in relevance to the research context. Ensure that the points mentioned has clear details on the sources of data, that will help later, while categorizing, to understand the context of data.
During: arrange the data bits into categories, with each category signifying a deeper meaning and connection rather than being straight forward. Merge categories into next top level categories, each of the category being an insight that can be related to the data within the sub category. By following this top down approach, generate a top level set of 2/3 categories each of which are final set of insights to help support concept generation.
After: Re read the insights generated and see if categories are deeper enough. The more you look at data, the deeper you think, the better the insights generated.
Use Case
After research phase of the waste management project, we used affinity diagramming in the project to uncover deeper insights. While in the beginning we followed a straightforward categorizing approach, every higher level required deep thinking and revisiting research data that helped uncover insights that are not directly observed. Although it was time consuming, the insights that were generated at the top level proved to be a great point to generate concepts that would help the community.
Sense Making Data
As a data analysis technique, focus is on every data note and corresponding categories generated. By going through and categorizing large quantities of data, insight generation becomes easier. While categorizing is to group the data bits together, care needs to be taken to make sure categories are based on a deeper understanding of data and not straightforward.