From observation to insights

‍Reflections on interpreting design research findings

Working with design research, I am often confronted with large volumes of empirical material: interview transcripts, observational notes, survey responses, and usability findings. While this material is rich, it rarely speaks for itself. Without a deliberate interpretive process, research outcomes tend to remain descriptive, making it difficult to connect them meaningfully to design decisions.

Over time, I have adopted a simple analytical progression that helps me move from research material toward insights. I do not treat this as a formal framework, but rather as a set of reflective prompts that guide my thinking: what I observed, how I interpret it, and why it is relevant. These questions help slow down my reasoning and make explicit the steps between raw data and insight formation. Recommendations, in my practice, follow later as a separate and distinct step.

What I observed

My analysis usually begins with close attention to what actually occurred during research activities. At this stage, I focus on describing participant behaviour, statements, and contextual conditions as they appear in the data, deliberately resisting the urge to explain them too early.

This often involves noting repeated actions, moments of hesitation or interruption, and patterns in how participants respond to particular tasks or questions. I also pay attention to situational factors that may shape behaviour, such as time pressure, unfamiliarity with the system, or environmental constraints.

For example, in one study I observed that several participants paused for extended periods or exited the onboarding process during the document upload step. At this point, I avoid drawing conclusions. My aim is simply to articulate what happened in a way that remains recognisable to others reviewing the same material.

How I interpret it

Once observations are clearly articulated, I begin the interpretive work. This involves looking across the data to identify patterns and considering what might account for them. I treat interpretation as provisional rather than definitive, recognising that alternative explanations are often possible.

In practice, this step requires moving back and forth between the data and relevant conceptual lenses, such as trust, perceived risk, or cognitive effort. Interpretations gain strength when they are supported by multiple observations rather than isolated instances.

Returning to the onboarding example, the repeated pauses and exits during document upload led me to consider whether participants were uncertain about how their data would be handled, or concerned about making mistakes they could not easily undo. This interpretation emerged not from a single comment, but from the convergence of behaviour, timing, and remarks across several sessions.

Why it is relevant

Interpretation alone does not yet constitute an insight. To reach that point, I reflect on why a particular pattern matters and what its implications might be.

This involves considering how the issue could affect user experience over time, including confidence, willingness to proceed, or overall engagement. Depending on the context, it may also raise broader organisational or ethical questions.

In the onboarding case, uncertainty at an early stage appeared likely to delay completion or lead to disengagement altogether. From a longer-term perspective, this pointed to questions about early trust formation and its potential impact on adoption.

At this stage, an insight becomes visible: not simply that users pause, but that early moments of uncertainty can shape trust and commitment to the product.

Recommendations as a fourth step

I deliberately treat recommendations as a separate, fourth step that follows insight formation. I have found that recommendations are more grounded when they are built on clearly articulated observations, interpretations, and relevance, rather than introduced prematurely.

Rather than attempting to resolve surface-level symptoms, I aim to respond to the underlying insight identified through analysis. I also treat recommendations as provisional, assuming they will need to be tested, refined, or even discarded as further evidence emerges.

In this example, improving transparency around data handling and allowing users to pause and resume onboarding appeared to be reasonable directions to explore, given the insight developed through the research.

Reflections on the approach

The distinction between observation, interpretation, and implication is not unique to my work. Similar concerns appear in human-centred design guidance from the International Organization for Standardization, as well as in applied research discussions from the Nielsen Norman Group and IDEO.

What I find valuable about making these steps explicit is not that it removes subjectivity, but that it makes my reasoning more transparent. This has been particularly helpful when working with multidisciplinary teams and clients, as it invites them to follow the logic of the analysis, question assumptions, and engage in discussion around the insights rather than only reacting to solutions.

Concluding reflection

In my experience, design research rarely provides clear answers. Its contribution lies in supporting more informed judgement. By moving deliberately from observation to interpretation and then to relevance, I am better able to articulate insights that can later inform recommendations, while remaining open to revision and learning.

References

• International Organization for Standardization. ISO 9241-210: Human-centred design for interactive systems.
• https://www.iso.org/standard/77520.html
• Nielsen Norman Group. UX Research Methods Overview.
• https://www.nngroup.com/articles/ux-research-cheat-sheet/
• IDEO. Human-Centered Design Toolkit.
• https://www.ideo.com/tools/design-thinking

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