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Why Research also needs to research itself

Alessandra Gomiero

7 min read·<br>Apr 15, 2026

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We have been living in a paradox that is no longer new. Never have we had so much access to information and, at the same time, never have we been so exposed to misinformation. Biased reports, dashboards that impress them more by volume than by clarity, statistics without context or source. All this forces us to revisit a fundamental question: how can we trust the data we use to make decisions?<br>Press enter or click to view image in full size

Photo by Luke Chesser on UnsplashThis concern led me to revisit concepts of Metadesign, especially through the reflections of Caio Vassão, and to deepen my study of meta-research.<br>In simple terms, metadesign can be understood as the “design of design,” that is, a reflective process dedicated to improving the methods, tools, and conditions that allow design to take place. More than designing objects or services, it invites us to reflect on how we design systems, processes, and ecosystems of knowledge, especially in contexts marked by complexity, ambiguity, and constant change.<br>Meta-research – research about research<br>Like metadesign, meta-research is, in essence, research about research itself. It is a field dedicated to examining, critiquing, and improving the practices, processes, and policies that sustain the production of knowledge: how it is conceived, conducted, communicated, evaluated, replicated, and rewarded.<br>According to Stanford METRICS and the Meta-Research community at NYU Langone, meta-research operates across different dimensions of the scientific process. It analyzes methods, investigates how results are reported, discusses reproducibility, proposes alternatives to traditional peer review, and examines incentive systems that shape research behavior. In other words, science has only advanced because it created formal rituals to review its own practice.<br>Although these debates are relatively consolidated in the scientific field, they are still little explored in a systematic way in the corporate world, especially in market research, product research, user experience research, and consumer behavior studies.<br>That is what I mean.<br>When research stops being an investigative tool<br>Since 2018, I have been working primarily with qualitative research for companies across different industries and levels of maturity. In this experience, I have noticed a persistent pattern: a crystallization of methods. What I mean by this is that instead of starting from the problem to choose the most appropriate approach, many organizations start from an already established method and shape the problem around it.<br>The justification that “we’ve always done it this way” or “this model already works” usually relies on historical efficiency, especially in more traditional environments. The side effect is subtle but, in my view, highly relevant: research stops being an investigative tool and begins to operate as a standardized protocol, one that is not very sensitive to the nuances of context.<br>In practice, this translates into constant methodological conflicts. There have been cases in which I suggested leaner qualitative approaches capable of generating much deeper insights with greater agility, whether in satisfaction research, user experience studies, trend research, or consumer behavior studies. Even so, the recurring demand was for numbers, large quantitative datasets, and extensive dashboards, even when these visualizations revealed little about what people were actually experiencing, feeling, or facing in their journeys and in their relationships with products and services. The result is a type of analysis that prioritizes the description of quantitative data, but not necessarily a deeper understanding of the experiences that produce them.<br>In this context, resistance also manifested itself in the limited appreciation for the structural work that sustains research itself, such as the curation and continuous organization of data repositories. Even when a study is well planned and structured, the process during research does not necessarily unfold in a linear sequence across data collection, analysis, and final delivery. As fieldwork progresses, hypotheses shift, hidden data emerge, and new questions arise throughout the research process.<br>Data organization follows this movement and, for this reason, is not merely an operational step but a constitutive part of the research itself. Yet these activities, which are fundamental for ensuring consistency, speed, and reliability, are...