Atomic research explained: Applications for modern UX teams

User insights are everywhere—buried in reports, spread across multiple tools, hidden in Slack conversations, stored in your UX team’s minds. So when it comes to making decisions, wading through all the data to find relevant insights can feel impossible.

Atomic research changes that. It turns big amounts of data into small, reusable ‘research nuggets.’ These nuggets are clear, easy to share, and actionable.

Let’s explore the evolution of atomic research, applications in UX, and how to store (and share) those all-important research nuggets.

Atomic research involves breaking down user insights into their smallest, most basic components—similar to atoms in chemistry. You can then use these research nuggets throughout product development to spread understanding and inform decisions.

Developed by Daniel Pidcock, this approach allows researchers to gain a granular and nuanced understanding of user behavior, preferences, and motivations.

According to Daniel, atomic research is based on:

(We’ll cover this process in detail later on–jump ahead now.)

Atomic research nuggets are the smallest indivisible units of user research data. They’re like the atoms of user experience research, representing a single observation, quote, or behavior captured during research activities such as user interviews, usability tests, or surveys.

Each nugget typically consists of three main components:

Unlike UX research reports, which provide a complete overview of a research project, atomic research nuggets focus on specific, granular details. Here’s a quick look at the differences:

Atomic research in UX started as a solution to a common problem: research findings needed to be easier to manage and use. Traditional research often created long reports that no one had time to read, leaving important insights unused. To fix this, UX teams began breaking research into smaller, simpler pieces that were easier to organize and share.

The idea comes from the atomic UX design framework, introduced by Brad Frost in 2013. Atomic design breaks user interfaces into small, reusable parts—like buttons or input fields. Atomic research applies the same idea to insights, breaking them into small, valuable pieces that teams can build up to use again and again.

In 2017, Tomer Sharon at WeWork introduced Polaris, a system for storing research findings. Polaris focused on ‘nuggets’—small, tagged pieces of research that were easy to discover and came from evidence-based thinking. Around the same time, Daniel Pidcock shared a similar idea. He suggested organizing research into experiments, facts, insights, and recommendations.

And thus, the foundations of atomic research were laid.

Atomic research helps you turn insights into real action—here’s how.

Atomic research isn’t for every organization, but it becomes invaluable in certain scenarios. Tomer Sharon outlined three types of organizations that stand to gain the most:

Like any approach, atomic research comes with its own set of advantages and challenges. To decide if it’s right for your organization, it’s important to weigh the benefits against the potential drawbacks. Let’s break it down.

Daniel Pidcock, a leading voice in atomic research, outlines a simple way to turn research into actionable knowledge. This process organizes findings so teams can make decisions faster and learn continuously. Let’s look at the steps in the research model.

The first step in atomic research is running experiments to collect reliable raw data. This involves choosing the right research methods, such as usability testing, surveys, or moderated interviews, to observe how users interact with your product. You can learn more about structured research techniques from UX research resources.

Start with a clear objective. Ask yourself: What do we need to learn? Whether you’re trying to understand why users abandon a sign-up form or you’re testing a new feature, defining your research objective keeps the project focused.

Recruiting the right participants is another critical step. Your participants should represent your actual users to ensure the data reflects real-world behavior. Creating effective user personas can help you identify the right audience.

Utilize a user research tool, like Maze, to make the recruitment and UX research process smoother. You can access a Panel of over three million B2B and B2C participants across more than 130 countries. This diverse pool allows you to apply over 400 filters to target specific demographics, ensuring your research reaches the right audience.

Document everything in detail. Record raw data such as task completion rates, user quotes, or session videos. Use a research repository to store and organize your findings. This ensures your data remains accessible and usable as you move through the research process.

Remember, stick to observing what users do and say. This stage is about collecting data, not jumping to conclusions.

Once you’ve gathered raw data, the next step is to pull out the facts. Facts are unbiased, evidence-based observations that describe what happened during your experiments. This stage focuses on documenting what you know for certain without adding interpretations or opinions.

With facts in hand, the next step is to connect them to uncover insights. Insights are the “aha!” moments that explain what your facts mean and why they matter. They don’t stand alone—they emerge by identifying patterns, linking related facts, and interpreting the bigger picture.

For example, if multiple facts show users clicking the wrong button or abandoning a process, the insight might be: “Users don’t understand the button’s purpose, possibly due to unclear labeling.” Insights like this are evidence-based conclusions that highlight what’s happening and hint at possible solutions.

This step requires careful analysis. Facts from different experiments, such as usability tests and surveys, can complement each other and form stronger insights. The key is to stay grounded in the data and avoid jumping to unsupported conclusions.

Once you’ve identified these insights, it’s time to turn them into actionable recommendations. Recommendations answer the question, “What should we do about this?”

Start by linking each recommendation directly to an insight. For example, if your insight reveals that users struggle with unclear button labels, the recommendation might be: “Update button labels to include action-oriented text, like ‘Submit Form’ instead of ‘Submit.’”

Recommendations should be specific and achievable. Instead of vague suggestions like “Improve navigation,” provide concrete actions such as “Reorganize the navigation menu into three clear categories based on user feedback.” This clarity helps teams prioritize and act efficiently.

It’s also helpful to prioritize recommendations. Use evidence, such as the number of users affected or the impact on key metrics, to determine which changes should come first. Clear prioritization ensures the most critical issues are addressed without overwhelming your team.

Testing your recommendations is where ideas meet reality. This step ensures that your solutions address the user problems identified during the research process.

Start by defining success. Identify measurable outcomes that will tell you if your recommendation worked. For example, if you recommended changing a button label to make it clearer, measure whether more users complete the action or spend less time deciding what to click.

Testing methods vary depending on the recommendation. Usability tests can reveal immediate feedback on design changes, while A/B testing works well when comparing two versions of a feature. Live experiments, such as deploying updates to a subset of users, can provide real-world data to validate your changes.

Sometimes, tests won’t go as planned—and that’s okay. If your recommendation doesn’t achieve the desired result, revisit and refine your insights.

Now that you’ve refined your recommendations, the next step is to ensure your research lives on as a valuable resource for your team.

If your findings aren’t stored and shared properly, they might as well be locked in a dusty filing cabinet labeled ‘Lost Forever.’ Let’s make sure that doesn’t happen.

Maze is paving the way in redefining how user research impacts decisions across organizations. With its vision for Atomic Evidence, Maze is focused on turning granular data points into powerful research narratives.

Here’s how you can use Maze to turn your data into actionable evidence:

Whether you’re running usability tests, user interviews, A/B tests, or gathering feedback on prototypes created in design tools like Figma, Maze offers a comprehensive set of UX research methods. With pre-made templates, an intuitive drag-and-drop interface, screen recording, and AI-powered follow-up questions, your research study can quickly capture the data you need.

With Maze Panel, you can access a pool of over three million testers worldwide—filter participants by age, location, profession, or other demographics to match your target audience. Already have your own testers in mind? Maze Reach allows you to invite your participants directly, offering the flexibility to use internal teams, loyal customers, or niche groups for more tailored feedback.

Once your research is live, Maze automatically collects the results and generates detailed reports. These reports provide usability scores, heatmaps, and even mission completion rates.

Use Maze’s AI-powered themes feature to group open-ended responses into meaningful categories. For instance, if testers frequently mention “confusing button labels,” the tool highlights this as a recurring theme. This feature saves hours of manual sorting and helps you focus on acting on your experiments.

Once your findings are ready, share them using Maze’s presentation mode or export them as PDF reports. Include usability scores, screenshots, and key recommendations in a format that is easy for teams and stakeholders to understand. You can back decisions with clear, visualized data in your next product meeting.

Maze doesn’t just improve research methods—it rethinks how teams capture, analyze, and act on user insights. This ensures user research becomes a critical driver of business and product strategies.

Although Atomic Evidence is still in early development, Maze’s powerful suite of user research tools can already help you capture atomic nuggets. Use these nuggets to inform your decision-making, and watch this space for updates on building dynamic, interconnected artifacts within and outside Maze.