User research is one of the core practices of UX. Yet many modern UX teams conduct research in ways that capture only the most obvious insights from users. Surveys, quick interviews, and rapid usability sessions can help validate ideas, but they often miss the deeper patterns that shape how people actually interact with technology.
In a recent paper, Sanaa Strickland and I argue that the most important insights in UX research often come from something called tacit data. Tacit data refers to the habitual, intuitive, and often subconscious behaviors that influence how users engage with products and systems. Because these behaviors are so ingrained, users rarely articulate them directly when asked about their experiences.
Understanding how to uncover this type of information is one of the most important UX research skills practitioners can develop.
The Problem With Surface-Level Research
Modern UX research practices frequently prioritize speed and efficiency. Product teams working within Lean or Agile environments often conduct quick validation studies to confirm whether a design direction is acceptable to users.
While these approaches can be useful, they rarely reveal the deeper motivations that shape user behavior. Many contemporary UX methods emphasize explicit feedback such as what users say they want or what problems they claim to encounter. What gets lost in this process is the contextual knowledge embedded in users’ everyday routines.
Earlier UX approaches such as contextual inquiry were specifically designed to capture this deeper layer of insight. Researchers would observe users in their real environments and discuss their actions in real time. The goal was not simply to ask questions but to understand how technology fit into a person’s daily activities.
This kind of immersive observation remains one of the most valuable UX research skills available to practitioners.
Why Tacit Data Matters
Tacit data provides insight into how users actually behave rather than how they describe their behavior. For example, users might say that a system is easy to use, yet repeatedly struggle with specific interactions during observation. In other cases, users may not even recognize the workarounds they have developed to compensate for poor design.
Getto and Strickland describe tacit data as the habitual and instinctual aspects of user behavior that emerge during real interaction with a system. Because these behaviors occur automatically, they are difficult to capture through traditional research instruments such as surveys or structured interviews.
Developing UX research skills that can uncover these patterns requires researchers to spend time observing users in context and asking open-ended questions that encourage reflection on real activities rather than hypothetical scenarios.
Triangulating Methods to Improve UX Research Skills
One of the key recommendations in the paper is that researchers should combine multiple research methods to uncover tacit data. No single method is likely to reveal the full picture of how users interact with a product.
Instead, effective UX research skills involve triangulation. Researchers might begin with heuristic evaluation to identify potential usability issues. They might then conduct surveys to identify patterns across a larger population of users. Finally, interviews and usability testing can provide deeper insight into how those issues affect real user behavior.
By layering qualitative and quantitative approaches, researchers can begin to uncover patterns that would remain invisible if only one method were used.
This kind of methodological flexibility is increasingly important as UX research becomes more integrated into fast-moving product development environments.
Expanding UX Research Skills for the Future
The authors also point to emerging technologies that may help UX professionals uncover tacit data more effectively. Tools such as eye tracking, facial expression analysis, and other biometric methods can reveal emotional and cognitive responses that users may not consciously report.
Artificial intelligence may also play a role. Machine learning systems can analyze large collections of qualitative data such as transcripts or behavioral recordings to detect patterns that signal frustration, confusion, or mastery.
These tools do not replace traditional UX research skills, but they may expand the toolkit researchers use to understand users at a deeper level.
Reclaiming the Depth of User Research
The broader argument of the paper is that UX research should not become merely a validation exercise. When research focuses only on confirming assumptions, teams risk overlooking the deeper insights that lead to meaningful design improvements.
Strong UX research skills involve more than collecting feedback. They require immersing oneself in the user’s environment, observing real behavior, and synthesizing insights that connect user needs with design decisions.
Uncovering tacit data helps ensure that products align not just with what users say they want, but with the realities of how they actually live and work.
Cite the Article
Getto, G., & Strickland, S. (2025). Uncovering tacit data in UX research: Balancing depth and efficiency through immersive methods. 2025 IEEE International Professional Communication Conference (ProComm), 49–55. https://doi.org/10.1109/ProComm64814.2025.00018