A post about synthesis in design.

Our team at Helix Centre conducted exploratory research around a topic where little we knew. Early-stage research like this focuses on understanding the problem space, which means learning about people's behaviours, motivations and goals so to build valuable solutions.

After design research comes synthesis, a process in which we distil insights from data. In Human-Centred design, the goal is to make the individual learnings group knowledge.

This post talks about how I analyse qualitative data in 4 steps. Although the following process uses survey data as a starting point, it is applicable for other data as well.

https://s3-us-west-2.amazonaws.com/secure.notion-static.com/090becf7-ecdc-4c2e-9770-386c2b5ea4c3/01_SynthesisQualiData_greatgreta.png

1. Extract the data

The goal is to find essential information from interviews, surveys' responses and write them down on sticky notes. In this phase, I use a concise and straightforward language and use one sticky note for each takeaway so I can easily organise them later.

Our team shared a Typeform survey with a pool of trusted healthcare professionals and families. I don't find Typeform data collection phenomenal, but you can download a csv (or xlsx) file or get a nicely layouted interactive report. You can then organise your data in a spreadsheet, or on a whiteboard.

I use whiteboards because I like a collaborative and visual organisation of the data. I am a massive fan of Miro, not just because I am a remote professional but also because it's an excellent tool for process documentation.

Custom template for extracting data from Typeform surveys.

Custom template for extracting data from Typeform surveys.

When extracting data, it's useful to be able to refer back to the sources (e.g., full transcript, survey participant, etc.). In this case, I note the questionnaire's respondents as a number.

Filled-in example.

Filled-in example.

2. Synthesising the data through affinity diagramming

After extracting all the data into sticky notes, I then move into visually identify patterns. You can do this by using a process called affinity diagramming. In which sticky notes that share an affinity (e.g., similar motivation, similar issue, etc.) are clustered together into themes. While doing affinity mapping the themes or categories rise from clustering the notes. So it's ok to take more than one round to get your categories right.

Before: scattered data from research.

Before: scattered data from research.

After the collaborative mapping session.

After the collaborative mapping session.

3. Create insights

To visualise insights from research, I like to use job stories. From each theme and for each user type, I create multiple short stories. Alongside the crucial focus on motivation, job stories take into account the context users are in, their causality, and anxieties.

Some job stories visualising the survey's data.

Some job stories visualising the survey's data.