As in all research projects, our beginning was fuzzy. We collected an overwhelming amount of information within the research phase and ended up with pages of unclassified information, data from 15 semi-structured interviews with industry professionals, endless articles, excel sheets with benchmarking results, mappings of consumer needs, to name a few.
Quite early, we realized that traditional thematic analysis will not be enough in order to tame and interpret all the data. Luckily, we had a service designer on the project, who suggested exploring some new tools. This is where the affinity diagram came to rescue. Geared with coffee and snacks we wasted no time and embarked on a workshop day.
We started the day by going through transcribed interviews, highlighting relevant sentences and writing them separately on post-it notes. Walls were quickly filled with chaotic information, until we classified it into categories. We then let the data talk to us and started finding patterns. Slowly we gained a common understanding on what elements to include into upcoming trainings and how to find relationships between concepts.
Getting answers to unasked questions
The affinity diagram method was created in 1960´s by Japanese anthropologist Jiro Kawakita. It is also referred to as the KJ Method, after its creator. The affinity diagram is a good choice for analyzing a big amount of various, typically qualitative data. The idea is to understand the essence behind the content.
The material is broken down into observations and grouping them based on similarity, dependence or closeness. One benefit of the method is its power to bring a team onto the same page and to achieve a shared understanding not only of the actual data, but also of the connections, ideas and hidden meanings and findings, which emerge from the data during the collaborative analysis.
Towards a deeper understanding of a topic
The affinity diagram allows the researcher to be the instrument with which the data is processed. Some can think it risky, as it apparently can lead to qualitative research related bias. All the information is interpreted and filtered by people who know the subject like the back of their hands and who may even lack neutrality in certain cases.
However, the interpretations are the result of deep understanding of the data. If the data is classified and presented as it is, it is a result of data collection in a research project. The affinity diagram method can lead even further, and result in sophisticated outlooks around the topic. It lets new unidentified themes emerge and thus, brings new light to the outcome. This gives the courage to make fresh and accurate suggestions based on the findings and understandings from the analysis of the data.
Our process was laborious as it took several weeks to classify and interpret all the information. However, the outcome was beneficial as it sheds new light on the phenomenon we are researching. The outcome creates shared meaning and knowledge for all team members included. The ability to reflect on the results from different perspectives, trusting the process and letting the data “speak to us” was a human factor, which undeniably was needed for adding value to our project work.