Organizing UX Research

The purpose of UX research is to yield insights — make this easier by spending some quality time organizing your research in a way that is easy to access not just now but also at later points in time.

Having organized research data is a great way to:

• Prevent and clear confusion when differences of opinion arise between teams
• Allow easy and manageable sharing of insights between teams and stakeholders
• Revisit and reuse key findings in the future

What do a star, a thumbs-up emoji, and the number 5 have in common? They can all be used to rate things. But what happens if you use them all to rate the same thing? You’d have to perform a whole new study to understand what a star means in relation to a thumbs-up emoji! Total chaos.

When logging research data, make sure you use a consistent form of note-taking to avoid such confusion. You can ensure consistency by using a uniform rating scale, unit of measurement, formatting style, color codes, etc.

Your notes will likely be read by other team members and even by yourself in the future, so make them easy to access and understand.^[1]

Research insights are incredibly useful, but a lot of people find the analysis process intimidating because of the sheer volume of data that gets collected. Many novice UX researchers struggle with data, especially when it is collected and recorded haphazardly.

When conducting UX research, do yourself a favor and organize your data as and when you collect it. This will make the analysis phase a lot easier and quicker for you.

When you’re collecting data, make sure you tag them with relevant keywords or themes. This process, called thematic analysis, makes large volumes of data easily scannable and also reveals any patterns or trends that exist. For example, you can thematically tag positive and negative feedback from users.

Doing so can also help you search your data by keyword and view all categories and notes tagged under it, making it easier for you to locate specific data quickly and precisely.

Thematic analysis is particularly well-suited for dealing with qualitative data. It can be done manually or through user research tools like Aurelius and Userzoom.

Affinity mapping is a great tool to help you organize similar data together and draw insights from these groups. Like a thematic analysis, an affinity map is most useful when dealing with qualitative data. For example, after a series of user interviews, you could use affinity mapping to identify the words most frequently used by your users to describe your product, the most liked/disliked features, and so on.

To build an affinity map:

• Record your data on individual cards
• Search for patterns and group related cards together
• Give these groups a name
• Log findings and insights from each group^[2]

Decide early on in the process how you’ll name your data files. Ensuring that the naming process is consistent will make your data easy to scan, find, and help avoid any oversights. For example, you may include hyphens in your file names while another team member does not. This will give rise to two sets of files, one of which will go undiscovered when you’re running a search.

The easiest way to avoid this is by drafting a file naming guide where you clearly mention all the dos and don’ts of naming files. Once drafted, share it with all team members and ensure it is accessible to them at all times.

Once you’ve organized your data broadly based on themes, the next crucial step is to identify key insights. They essentially represent the potential answers to your original research questions and objectives.

For instance:

• If your research question is about why users do or don't buy your product, key insights might include users' pain points and their perceived value of your product.
• If you want to know what users think could be improved about your product, key insights may reveal the features that are working well and those that need improvement.
• If you are interested in understanding when users are most likely to buy your product, key insights could provide valuable information about their behaviors and daily schedules.

To derive these key insights, look for common denominators in user interview answers, focus group studies, diary entries, and other relevant sources. This process will help you further organize your data into manageable and scannable chunks, making it easier to revisit and utilize the insights later.

A user research repository is a database that can hold all your research-related information in an accessible way.

A user research repository can include:

• Inputs such as your initial research question, guides, research methodology structures, and research goals
• Outputs such as research data and insights from your studies
• Any actions that you have taken based on this data and the status of such actions^[3]

Having a repository makes it easy to locate information from past studies and use it in current studies. It can also help you find patterns across multiple studies and share data with stakeholders and team members.

More importantly, it is a safe place where no information ever gets lost. This means you can return to this database for more detailed information that research reports and summaries don’t include due to time and space constraints.

Check out this template of a simple user research repository to start with.