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How to structure and make sense of UX research data

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Imagine this scenario: It is your first day at work as a daycare professional. One of your responsibilities as a provider is to not just make sure the babies will survive under your protection but also meet their basic needs and even flourish to their best baby-selves. Now imagine that all the items that you need in order to fulfill their basic needs are spread in different places.

Their clothes are in one department down the hallway, their food in another one four floors up, and their diapers out in the yard up on a tree. And you are the one who needs to collect all these items and calm the crying babies.

This is exactly or approximately how a UX designer feels when all the UX research data and insights are spread in different spaces and tools. UX designers/researchers thrive from making connections and identifying patterns in user behavior in order to deeply understand the users, gain empathy and provide a relevant and pleasurable user experience.

But usually, the case is that the responses from surveys are gathered in one tool, the customer support input in another, the usability testing notes on someones hard drive (or hopefully on the cloud) and it becomes logistically harder to make sense of all this data and create desirable connections.

Another challenge some of us UX designers face when working on a project is creating alignment and understanding between the different roles and stakeholders regarding the importance of user research. Being on the same page is of high importance for achieving goals, both for the user and the organization.

These two core challenges and much more can be potentially tackled by creating a centralized user knowledge space or UX research repository.

Having a centralized user research repository is important for: 

  • Documenting all your research initiatives in one space. For instance when it is time for usability testing you can document and save the test plan, scripts, video recordings, notes, insights, and reports in the same space. You can even create templates for all your initiatives to save time and effort. 

  • Making your data searchable. You may seem to have the structure in all these separate tools and documents under control but it can be hard for others (and yourself) to find past data or just search through.

  • Avoiding loss of knowledge when employees leave the company and new people take over. Handovers can become much easier when all/most of the qualitative data is in one space.

  • Creating a space that stakeholders can be a part of. You usually share research reports and presentations but letting the stakeholder play around in your research world and use the tool can be really valuable in creating alignment within your team.

  • Tagging taxonomy. The main reason for gathering all data in one tool, apart from the obvious structure benefit, is to identify patterns in user behavior and make connections – sometimes even across different projects. That can look different in different tools but from my experience with Dovetail, that can become possible with highlighting and tagging relevant information with specific keywords (tagging taxonomy). When accessing a specific tag you can later view all the information from various users and sources that is related to this specific tag/keyword.

    Tips on how to tag taxonomy:
    There is no golden rule when it comes to the art of tagging since a lot depends on the project, the context, and the individuals creating and managing the tags. You will definitely need to iterate the process as you go but it is good to start with a rough idea of what is important when tagging raw data and being aligned with the team.

    For instance, is a specific quote on a user interview related to a goal you have set? Does it have a positive or negative connotation? Is it related to a specific feature or is it related to a specific behavior? These are all examples of how tagging taxonomy can help you organize your insights.)

  • Prioritization and decision-making. By using taxonomy and by visualizing how users engage about certain “features” your team can take more data-based decisions. However, always under your careful consideration of how this data was collected, processed, and used and how taxonomy was created. Imagine how you are using Google analytics data to come to certain conclusions about the users. You are (I hope) mindful of using quantitative data for decision making and the same should apply to your qualitative data.

  • Documenting and sharing lessons learned, creating a knowledge base for your company.

What to look for in such a tool:

  • Since you will save all your user data it is important that the tool complies with GDPR and is secure.

  • Plus if the tool offers a video transcription of the user interviews.

  • Plus if the tool offers a good way of showcasing the user insights and creating reports or presentations directly on the tool, embedding various image and video formats.

Examples of software for creating a research repository:

  • I have been using Dovetail and it offers most of the above functionality but there is other software out there that offers similar functionality like Stravito and Dscout. It seems that this market is growing as the need for gathering, organizing, and making the most out of user data becomes more prominent so let us know if you use any other tools that are worth looking at. 


Don’t hesitate to reach out and ask if you have more questions on how to structure data. We’d love to talk!