It can be tempting to complete a set of interviews or usability tests and think your research project is done. But without a process for systematically reviewing and making sense of the data you've collected, you risk leaving valuable insights on the table. The next step is to zoom out and look at your findings anew through a process called research synthesis.
Synthesis helps us take all of the info we've collected, organize it into patterns and themes, and translate those themes into insights. At the end of this process, you should have answers to the core research questions you set out to answer and evidence that proves or disproves your hypotheses.
Below are some guidelines for how to set yourself up for successful analysis and synthesis while you're conducting research sessions and after your interviews are complete. For information on how to ensure your data is useful, check out these tips (link coming soon).
While capturing your research data, you'll focus on documenting observations (what you saw the user do, or what problems you saw/heard them experience) and quotes (the verbatim of what the user actually said, in quotation marks). You may also want to make notes on early interpretations (what you believe something a user said or did means) and possible solutions (concrete ways to solve the problems identified).
While the bulk of research synthesis happens after you've finished gathering all your data, analysis can start right away. After every user interview, debrief with your teammates who observed the session about what stood out.
Capturing these insights while the session is fresh in your minds makes the overall process much faster and easier. Discussing observations as a group also leads to more thoughtful analysis, reduces cognitive bias, and helps your team form a strong shared understanding of the problem space you're investigating.
Synthesizing user interview data Affinity diagramming is one way of finding themes in a collection of ideas, quotes, or observations. This method helps you draw out insights from qualitative data quickly and collaboratively. This is traditionally done in person with sticky notes on a large blank wall or whiteboard. At GitLab, we use a tool called MURAL to recreate this experience remotely.
We use Dovetail to capture notes from each participant session and to store the insights which result from synthesizing the data. As you record data from research sessions, please remove any personal identifiable information or PII (such as first and last names). Additionally, Dovetail has the option of allowing you to highlight and tag your notes within the platform.
Aside from Dovetail, MURAL is a helpful tool when you are just learning how to synthesize and analyze your data.
1. Put your notes into MURAL Don’t just copy your notes word for word into a sticky note in MURAL. Read through them and choose the most salient information to create a sticky note from. Best practice is to use one piece of information per sticky note. Have each contributor take one participant to create sticky notes for. At the end of this step, you should have groups of sticky notes for each participant.
2. Cluster the data into themes Once all your individual notes are in MURAL, begin by grouping similar stickies together. The themes you use should tie directly to your research hypotheses, research goals, and objectives. You can copy these right into your MURAL board so you can keep them top-of-mind while you group individual findings into broader themes. You might also reference the questions listed in this article to ask yourself and your team throughout the synthesis process. Here are some potential ways to group findings:
Equivalence: "This finding is the same as this other finding."
Association: Around the same area of the experience being analyzed, or "This finding is best considered at the same time as this other finding."
Hierarchy: Larger thematic trends which several findings support, or "This finding is an example of this other finding." As your groupings start to come together, use a text heading to write titles or catch phrases that summarize each cluster of similar stickies.
3. Discuss and revise as needed Organize and reorganize your findings into meaningful categories until everyone seems to be in agreement. See if you need to adjust any of your themes before moving onto the next step.
You also need to check for possible bias. Try “arguing the other side.” In other words, build a case from your research against your key insights to see if they still stand up.
4. Distill findings into insights Once you're happy with your groupings, distill your findings into insight statements and put them in your Dovetail project. Insights you uncover should come from multiple sources in your research. Depending on how much research you did, the number of insights you uncover may vary. There are two types of insights, actionable and informative.
This video (team members only) shows this multistep process described above on how to take information that has been transferred into Dovetail and MURAL, so you can identify, analyze, and publish new research insights.
Right now, you can use Dovetail to create tags that help distill your user data into pieces of evidence for actionable insights. While this is useful, this can also be problematic. Here's why:
To properly manage research insights within Dovetail, here are some do's and don'ts when creating your own tags.
| Bad | Good |
|---|---|
User is confused by navigation and fails the task. |
Confusion, Navigation failure, Task failure |
🆕 Features communicate the problem being solved and value to a new user |
Positive Value |
Lack of clarity for some users |
Confusion |
