Collaborative analysis of research data: the art of generating actionable insights

Now that you have conducted your first user interviews, you have gained a lot of new information and want to share it with your colleagues immediately. But at this point, we recommend you wait and first analyze and categorize your messy data to generate some actionable insights. It is important that you take this step and follow a structured process to avoid changing something in your product without proper interpretation of your whole set of insights.

by Julia

As this will be the most challenging and time-consuming part of all, we want to share our approach and how it evolved along the way and provide some advice for you. There is still room for improvement. However, we have found a process that has proven to be quite effective compared to our initial efforts.

Our approach to analyze qualitative data

We decided to use thematic analysis, a popular method with six steps to uncover key themes from complex data. This flexible approach works well for exploratory interviews, where you are not sure about the outcome and for interviews where you investigate a specific topic and hence have categories in mind beforehand. You can find more details in the provided link.

However, keep in mind that thematic analysis can be subjective and might miss interesting details if it is done only by one person. This is where collaborative analysis comes into play. Involving at least two people in each interview helps us to mitigate biases and integrate different perspectives. This typically results in richer, more nuanced, robust and trustworthy insights.

What did we learn along the way?

We initially decided to code the first interview together. If you’re wondering what coding means, it’s the process of assigning meaningful descriptions to your users’ statements and organizing your data in a helpful way for further analysis. It helps to summarize statements into valid findings and to jump back to the respective statements at a later point in time. The analyzing part is easily managed in Condens, our analysis tool, which you may remember from previous blog posts. The image here is a glance at what it looks like. Due to limited experience, some team members have found the coding and analyzing part challenging. This has led to lengthy, unproductive, and frustrating meetings, with endless discussions about the details of the data and less progress in generating valuable insights. While discussions are helpful and having enough space for them is essential, we slightly modified our approach, which has since improved our productivity.

Today, the most experienced team member with qualitative analysis starts analyzing and coding the first interviews, detailing each assigned code/tag and explaining when to use it. Afterward, other team members review the same interview and provide feedback on naming or labeled statements that they do not consider valuable, or tagging new statements if the content is deemed valuable. If necessary, we may meet to discuss and refine wordings or merge similarities to ensure a unified approach when analyzing the remaining interviews distributed among the team.

Learnings

This change accelerated the analyzing part and offered less experienced team members a valuable learning opportunity.
Clearly describing each code’s intent enhances discussions, provides a shared understanding, and aids team members in applying these categories to subsequent interviews.
With these changes, we managed to reduce the time for analysis and promote flexibility as we can work more asynchronously.

When you finally have analyzed and categorized your messy data, the fun part starts again. You can interpret your data, discuss your findings and learnings, and generate actionable and valuable insights that you want to share across the organization. You will read more about the latter in our next blog post. We always do this last step in a joint meeting. It resulted in fruitful discussions about insights and learnings and still ensures the credibility of the results.

Summary and final advice

Research results must be organized, synthesized, and analyzed, preferably in a partnership or group, to broaden the perspective and to gather valuable insights. Collaborative analyzing tools enable team members to work on the data simultaneously, annotate, and track changes. Follow an approach, but adapt it to your needs.
Each category (code) should be clearly formulated and distinguishable from other categories (codes) to organize your data better.
It is recommended that you define some guidelines upfront, such as the smallest and largest text component you are allowed to tag. For us, it is at least one sentence, as tagging only one word was not helpful when you want to jump back later, as context information was missing.
We recommend starting the analyzing part after you have conducted 2-3 interviews. This allows you to work while the details and insights remain fresh, which greatly facilitates the process. Further, the first themes and commonalities typically become apparent after a few interviews, which streamlines the process of refining themes and ultimately saves time.
Drawing a map of your themes or putting them on sticky notes that you can move around – if you have no tool support – can help you visualize the relationship between them.
Meetings to discuss your results and interpretations are essential for integrating individual analyses into a coherent overall understanding.
You should be aware that besides the benefits, collaborative analysis presents challenges, such as potential conflicts over interpretation and the need for substantial time and effort to reach a consensus.

And that sums up our article for today. In our next one, we will let you know how we share and utilize customer insights across the organization. Stay tuned.