Why your team should invest in atomic research
Firstly, atomic research goes hand in hand with creating and maintaining a research repository. Therefore, team members can easily find research insights and nuggets by filtering on specific tags or categories.
New nuggets are easily added to the repository to create a single source of truth for valuable user research data. In a way, this is the research version of a design system in UI design.
Second, due to its simple nature, nuggets go against personal bias. They're broken down and stored in a raw form, which makes them easier to understand. Because you can review the nugget without dependencies, it is far less likely to cause bias. This is important in UX research.
The method explained
Two influential UX researchers, Tomer Sharon and Daniel Pidcock, discovered that the need for management in research could be decreased if user research were divided into four smaller parts.
The ideal process is built on experiments, facts, insights, and recommendations. In atomic research, these are called 'nuggets.' Here's an example that shows all four nuggets in action.
Experiments: "We did a usability test to validate the next release of our product"
Facts: "... and we noticed that users tend to spend more time on the second page looking at tooltips in the form."
Insights: "This makes us think that certain labels are unclear in the form."
Recommendations: "Therefore, we recommend making the labels on page two more specific."
The first part of atomic research describes the actual experiment conducted. It consists of information that captures the high level of the research: the type, the methods used, research techniques, and more.
Here's another example.
"The design team prepared and facilitated a design thinking workshop to turn a design challenge into a validated concept."
The second nugget is on the factual level. The facts reported are linked to data with evidence, including videos, quotes, images, and survey responses.
If we continue with the example we used before, here's what a factual nugget could look like.
"During the workshop, we found that users struggle with finding their data in the current design."
Insights are extracted from the facts. Each insight should answer questions the team posed at the start of the research.
Here's our next example based on the experiment and facts nuggets.
"We suspect that users prefer to view their data in one overview rather than multiple smaller lists."
The last piece of the puzzle is recommendations based on the facts and insights. The more evidence you have, the stronger your recommendations will be.
In the example below, you can see what a recommendation can look like.
"Based on our results, we recommend using longer lists instead of the collapsible smaller lists used in the current design."
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