With a strategic analysis of the push to “democratize” research—to open the research practice to non-researchers—this paper charts a path to shape the future value and practice of ethnography. While democratization debates often focus on concerns that it will take jobs away from trained researchers, Nadine Levin focuses instead on what democratization looks like in practice and why it is appealing to organizations. She argues that democratization is a window into the changing knowledge practices, value systems, and organization of labor in modern tech environments. Through this lens, she lays out concrete ways ethnographers can have more agency and long-term influence in our evolving organizational and business contexts.
Confronting the Rise of Democratization
Applied ethnographers have often found themselves in a state of precarity, forced to contemplate change as their work context has evolved (Baxter, Courage, and Caine 2015; Mack and Squires 2014). For example, several EPIC papers have focused on corporate ethnography’s search for “product-market fit” relative to changes in the corporate world and the field of user experience research (Madsbjerg 2014; De Paula, Thomas, and Lang 2014; Cefkin 2009; Bandyopadhyay and Buck 2015; Flynn and Lovejoy 2014). Others have focused on applied ethnography’s preoccupation with distinguishing itself from academic ethnography and anthropology (Nafus and Anderson 2014). “One of the recurring themes has been the concerns surrounding how ethnography is defined in organisations and what that means for ethnography’s significance and relevance to business in the long term” (Badami and Goodman 2021).
So what are we grappling with in this current moment? The last few years have seen serious shifts in the state of UX research—and by extension, ethnography. Concerns about the economy and the rise of artificial intelligence have led to researcher layoffs and the tightening of design budgets in tech companies, culminating in what some have named “the UX research reckoning” (Antin 2023), in which research seems to have been hit particularly hard compared to other disciplines. This is a stark contrast to the “golden age of UX research” in the mid-2010s and early 2020s, which saw a rapid increase in research jobs and the hiring of many former academically trained researchers—many fleeing their own job crises tied to the decline of tenured positions in academia (Cultural Anthropology 2018).
As such, the nature and landscape of UX research at many companies is changing. Smaller research teams are asked to do more—and more quickly—as companies also ask adjacent disciplines like design and product to conduct research.[1] This is driven, in part, by questions about research’s ability to deliver value proportional to its monetary, labor, and time investments (Belt 2019)—something which is exacerbated in ethnography because of its longer timelines and horizons for impact. According to one survey, “43% of research teams have been asked to justify resources but despite that, 28% of teams are expected to do more research” (Bien 2022).
Cue calls to “democratize” research: to enable people without “formal” research training to participate in the research process, with the ultimate goal of increasing an organization’s capacity to be human-centered.[2] “Democratizing UX research,” according to one article, “makes research (collecting, storing, sharing, and accessing) accessible and possible for anyone within an organization, regardless of their role. It aims to break down traditional research barriers and hierarchies, allowing cross-functional teams to contribute to and benefit from user insights” (Ethnio 2023). Democratization, therefore, can happen at any and all levels of research, from planning research to analyzing data to using insights to make decisions (Ethnio 2023).
This raises important questions about the various forms democratization takes. What types and phases of research are democratized, and how much of the practice is entirely hands-off (Tang 2023)? In democratized environments, how are research responsibilities distributed differently, and how does this begin to change what counts as “research” in the first place?
Although the concept and practice of democratized research existed before the current “UX research reckoning” (Antin 2023), articles summarizing the UX research trends of 2023 and 2024 make it clear that democratization is now particularly in vogue (IAM Design Maker 2023). “Research is not just for UX researchers anymore; it’s a team sport,” says one article (Akhmedov 2023). Others push this even further, arguing that democratization is a core part of the modern researcher’s job (Ethnio 2023; Sirjani 2020).[3] This trend toward democratization is undoubtedly tied to the previously noted market forces, which caused companies to reduce the research function and try to do more with less. But it is also tied to long-standing perceptions that qualitative research is as simple as “talking to people” and therefore does not require extensive skill training.
Democratization, however, is not without its critics. One claim is that democratization harms the integrity of the discipline and puts jobs at risk (Balboni et al. 2023). “Researchers spend years studying, developing skills, and understanding the nuance of their profession, the same way developers and designers do for theirs” (“Wolstenholm 2023). What might happen to researchers and the research practice, some ask, if research activities are increasingly done by others? Will researchers lose their jobs, as appreciation of their expertise and role wanes? “It’s not shocking that UXRs are being laid off in droves after the whole ‘democratization’ trend kicked off. If everyone thinks they can do research (and they can’t), then there will be no jobs for dedicated researchers” (Balboni et al. 2023).
This criticism takes on a particular flavor when it comes to ethnography. While many ethnographers hail from academic environments, in which they are taught specific skills, methodologies, theories, and approaches, others learn in deeply embodied ways, when they are thrown into the field and must learn on the fly. Given the range of backgrounds and modes of learning associated with ethnography, is this concern—that democratization harms jobs and disciplinary rigor—legitimate, or simply a form of disciplinary gate-keeping?
Another claim is that democratization reduces research quality and impact and increases the burden of education and training (Balboni et al. 2023; Nash 2023). Conducting research studies is often a complex process, and decisions about methods and participants can significantly impact insights (de la Nuez 2019). Mistakes made by non-researchers, some argue, “can lead to confusion, bad decisions, and even legal problems” (Tang 2023). Training, others argue, is a unique skill that is separate from craft, and which requires time investment to plan and implement (Ronsen 2022). Moreover, democratization often assumes that partners are able and willing to learn research practices, which is not always the case in fast-paced corporate environments (Ethnio 2023; Soucy 2023).
Given these critiques, this paper approaches the topic of democratization from a different angle. Instead of focusing on the negative aspects of democratization or arguing that non-researchers should not be able to do research, I argue that democratization is a hallmark of important shifts in organizational structures and values—which should be examined critically and contextually.[4] How might we stop seeing the democratization of research as an existential threat or something binarily good or bad, and instead see it as a window into the changing knowledge practices, value systems, and organization of labor in modern tech environments?
If we approach democratization as a research question in and of itself (Thomas and Lang 2014; De Paula, Thomas, and Lang 2014), we might begin to ask: in places where democratization is the norm, how are certain types of knowledge and impact valued? What counts as expertise, and for whom? What kinds of labor, skill, and training are embodied in democratized research, and what does this mean for the future of ethnography? How can we imagine a better, more careful future where others are involved in research and ethnographic work, but in bounded ways?
Using my own team’s practices and experiences as data, I explore how democratization manifests in a particular civic tech context and highlight the unique questions and challenges that emerge. First, I argue that ethnographers are uniquely equipped to use their skills to interrogate the organization’s attempts to democratize research. By understanding what the organization seeks to achieve with democratization—and what the term “democratization” means in various contexts—ethnographers can help research deliver more value and more directly address organizational problems. In doing so, ethnographers can create feedback loops, redefining what “research” is and showing how it can have different types of impact.
Second, I argue that ethnographers can articulate new ways of working with and within organizations by approaching research through the lens of risk. Models of more “careful” democratization often advocate for drawing boundaries between tactical and strategic research, where non-researchers do carefully-scoped tactical research, leaving more strategic research as the domain of trained researchers. “Tactical evaluative research is the sweet spot for democratization. Every new feature should at least go through basic usability testing before launch” (Nash 2023). In this vein, strategic work is often seen as more complex and in keeping with traditional imaginings of ethnography, while tactical work is seen as a less skillful and impactful practice.
But this devalues the risk and impact that some tactical research entails. Talk to any usability expert, for example, and they will tell you how much skill and training is required to conduct impactful, high-quality usability studies! In this paper, I move beyond the tactical/strategic divide to show how ethnographers can identify the types of risk that might arise in various projects. This lens of risk can empower ethnographers to develop more contextual and varied working models for how and when to involve non-researchers.
Ultimately, this paper explores how ethnographic skills are needed in this moment of change and can act as a diagnostic and prognostic tool. How can ethnographic skills help us understand the root causes of efforts to democratize research, while also determining ways to intervene and shape what research is and does in the future? Democratization is happening, and, at least for the foreseeable future, is here to stay. Ethnography will not survive if we continue to see democratization as a threat rather than something that is “good to think with.” We have to grapple with trends like democratization seriously if ethnography—and the interpretive and relational value creation it foregrounds—is going to have a role in future organizations.
This paper asks: How can ethnography rearticulate the value and impact research brings and, in doing so, shift organizations towards a different model where democratization does not entail fundamental threats to the research practice and discipline? How can ethnographers have more agency in imagining their futures, which might entail different configurations of labor and practice as work environments themselves evolve?
A (Shortish) History of the Democratization of Research and the Opening up of Knowledge Practices
Where does this idea about democratizing knowledge come from? And what does it entail? Is it singularly tied to this moment of destabilization in the UX and ethnographic community, or is it part of something bigger and broader?
Tracing the history of democratization in organizational research and anthropology, one could argue it is tied to the emergence and growth of phenomena like user experience, design thinking, and agile product development over the last several decades. User experience, which emerged in the early 1990s as a way of pushing technology companies to think beyond computer interfaces and usability (Stevens 2019), advocated for involving users in the design process through iterative feedback.
Similarly, design thinking, which emerged in the early 2000s with an emphasis on empathy and human-centered problem-solving (Knemeyer 2015), contributed to the idea that anyone involved in product development could surface and engage with user needs. Moreover, design thinking opened up a space for research to become its own function, by driving a need for “insights” to help companies understand how designs impacted end users.
On the other hand, agile product development, with its focus on cross-functional collaboration and iterative testing, created a role for tightly-scoped research and insights to interface with engineering frameworks and timelines. Through its emphasis on “lean” product development, agile also gave rise to hybrid roles in which PMs were encouraged to own the entire product lifecycle—including research.
More recently, the push towards democratization has intersected with and been enabled by a variety of research tools that claim to increase research efficiency and speed. Such tools open up parts of the research process that have traditionally been managed by researchers—like participant recruitment and data analysis—giving easier access to non-researchers. For example, remote unmoderated testing platforms provide quick access to participants, while AI data analysis software empowers anyone to analyze research data. Driven by notions that research (particularly of a more academic nature) takes too much time and effort, these tools aim to automate certain aspects of the research process, delivering insights faster and cheaper. But as these emergent technologies make things that formerly required craft and expertise available to everyone, they do not necessarily “democratize” the interpretive skills required to use those technologies in meaningful ways.
One could also argue that the democratization of UX research is part of a broader movement to make knowledge practices more accessible and open (Levin and Leonelli 2017; Kelty et al. 2015). Take, for example, the recent open science movements in the United States and Europe: these aimed to make scientific data and papers more widely available, to increase the transparency of scientific research processes, and to make knowledge from publicly funded research available through open-access publishing. Similarly, the citizen science movement has tried to open up the very practice of science to non-experts over the last several decades. This has given rise to many collaborative scientific endeavors (Rosas et al. 2022; Polleri 2020; Grace-McCaskey et al. 2019), which have also been enabled by the rise of open technology platforms that enable crowdsourcing and collaboration.
This turn toward “openness” has also extended to the social sciences, as academics have sought to make research more participatory and to foreground the experiences of those who are often the “subjects” of research, particularly under the broad banner of “participatory action research” (Participatory Action Research, n.d.). Here, communities involved in and affected by research are considered experts and are encouraged to produce their own insights, foregrounding their own lived experiences. Take, for example, the EPIC paper “Empowering Communities: Future-Making through Citizen Ethnography,” which explores how the democratization of ethnography on a project dealing with high rates of youth suicide empowered local communities to generate, synthesize, and act upon findings (Badami and Goodman 2021). Drawing inspiration from feminist and post-modern studies, with their focus on structural inequality and power relations, these participatory approaches aim to decolonize knowledge and history. They question and challenge the power dynamics in more traditional and academic forms of knowledge production, arguing that these are extractive, giving little back to the communities from which knowledge is taken.
The turn to participation takes a particular flavor in design, with the rise of participatory design and co-design from the 1960s onward. These approaches aim to involve all stakeholders—employees, partners, customers, citizens, end users—in the design process, to ensure designs meet user needs and are usable (Wikipedia contributors 2024). Here, the public is invited to participate throughout the design process, from problem definition to design exploration (Bødker et al. 2022; Di Russo 2012; Asaro 2000). What stands out is how these movements to open up knowledge practices place less value on formal training and technical/academic expertise, often valuing and recognizing lived experience instead. However, in practice, more participatory approaches do not always break down the divide between stakeholders and end users; often, they simply end up enlarging the number of stakeholders exposed to the research and design practice.
Ultimately, democratization is not just about re-organizing how data is collected and interpreted. It reflects broader shifts in knowledge and value systems and the organization of labor. When it comes to the democratization of research more specifically, what does the push to redistribute certain types of power and agency say about the organization (Tang 2023)? How does the push toward democratization create different “epistemic cultures” (Cetina 1999) in different organizations? When research is democratized, what power is shared and what remains? What questions and outcomes are important, and for whom?
The Problem Space: A Reflection on the State of Democratized Research in the Author’s Organization
If we see the democratization of research as a generative moment, as an invitation to leverage ethnography to look into and interrogate the dynamics of organizations (Madsbjerg 2014; Flynn and Lovejoy 2014), what does democratization look like in practice? How does digging into the specifics of how democratization is enacted—the motives, dynamics, outcomes—reveal how democratization is varied and contextual as a practice?
I began working for the County and City of San Francisco in 2021, about a year before the large tech layoffs I referenced at the opening of this paper began. After 4.5 years at a large social media company, I left a team of 1000 researchers and joined a team of 1: me. On my new team, San Francisco Digital Services (colloquially referred to as “SF Digital Services”), I was responsible for leading the research practice for a team of 50 (made up of a design practice of two researchers including me, two service designers, and three UX designers). I had to ensure research contributed to the team’s mission to scale government digital services across the city through partnerships, consultations, and the building of new digital tools.
This move was a conscious career decision. I knew that leaving a highly-resourced environment where I was surrounded by many like-minded colleagues would involve a good deal of adjustment. To this end, one of my core responsibilities was building an inclusive research practice; however, my new role came with a limited number of templates, resources, research tools, and no dedicated research operations team or person. The researcher who had previously held my role had created some resources and practices, but these were not widely adopted by the team and were seen as providing limited value. As a result, I had to personally carry out (and create processes for) all aspects of the research process, from participant recruitment to data storage. In summary, the research practice was not set up in a way to facilitate the scale articulated in the team’s mission.
As I started to set up the research practice, one of the biggest adjustments I encountered was the push to “democratize” research in the organization. I was told (or perhaps strongly encouraged) to enable others, not just researchers, to lead and conduct research. This is a typical setup in civic and government organizations, which tend to be less well-resourced than private sector. However, this job marked the first time I had considered the term and concept. In academia, as I pursued a PhD in anthropology, knowledge was so specialized and resources were so guarded that the idea a non-academic could conduct research was preposterous. (After all, academia isn’t called the ivory tower for nothing.) In the private sector, the quality and rigor of knowledge were highly prized, largely because UX research was fighting to be taken seriously in a data-driven organization. Moreover, sensitive research had been leaked to the press, leading to debates about the rigor of conclusions and ultimately leading to more of a rigorous process around the production of knowledge. Given my background and previous work experiences, the idea that non-researchers should participate in research was understandably alien.
As I digested my new team’s democratization mandate, several alarm bells sounded in my head. Did non-researchers have the skills to navigate complex problem spaces, scope ambiguous research questions, and produce high-quality insights? Did they have the conceptual tools to generate impact and influence a complex organization like the government—a feat that even senior researchers would struggle to do? Moreover, why was research, not other disciplines, being pushed to democratize? Was research considered a low-skill activity if anyone could do it? What did that say about the value placed on research within the organization?
I was particularly sensitive to this line of thinking for two reasons. First, I had spent over ten years of my career studying and gaining academic accolades in ethnographic research. The suggestion that my expertise did not require training and skill was painful. Second, I had come from a private sector organization where ethnography and other qualitative research methodologies were often less valued and respected than quantitative methods (Levin 2019). Because qualitative research inherently deals with small sample sizes, it was not seen as “rigorous” or “objective” compared to quantitative methods like surveys or log data analysis. For example, when I tried to argue that insights from qualitative research could be generalized through careful sampling and by exploring underlying themes and factors, I received significant pushback from quantitative experts. They were so enmeshed in a statistical understanding of “representativeness” that they were affronted by the suggestion that qualitative research could extrapolate in certain, more inductive ways (Smith 2018).
I began to wrap my head around the idea of democratization at SF Digital Services—giving myself space to grapple with questions and concerns. I took stock of the previous research that had been done, to learn about the questions the team had asked, the type of impact they wanted to have and the ways they went about generating insights. A good chunk of the team’s research had been done through a rolling research program: the researcher before me had set up monthly research sessions to generate qualitative insights. In this model, the researcher had created templates and guidelines, but had left non-researchers responsible for everything from participant recruitment to conducting studies to reporting on insights. In other words, the researcher had empowered non-researchers to do their own research, but had not provided guidance or oversight on execution and analysis.
As I dug into the decks and presentations that had been created, and as I talked to teammates who had been involved in the program, I saw how this approach was problematic. Many of the resulting slide decks and reports contained misrepresented and imprecise insights. The team had not considered how participant recruitment and sampling might affect their work. They had reported on qualitative insights in a way that did not carefully engage with sample size and composition and did not focus on the “why” of the findings. They approached the data through a falsely quantitative lens, saying things like “⅖ users like X feature”. They also focused on user preferences instead of user behaviors. Ultimately, the team had not been coached on what qualitative research could and couldn’t do. Their access to research templates and tools had not resulted in rigorous work.
These errors and misconceptions are common throughout qualitative research and were not unique to my team. Less experienced qualitative research practitioners frequently try to make quantitative claims with small sample sizes, but because qualitative research does not try to and can never be statistically representative, such claims are not possible. In addition, less experienced qualitative practitioners often focus on user preferences (“I like design A better than design B”) without digging into the “why” behind such preferences. This focus on preferences (rather than behavior) can also create inaccurate data, because what people say and do are often not the same. Often what matters more in small-scale qualitative data—particularly in usability studies—is observations about how features do or do not enable people to complete tasks. In previous roles, I had seen how such errors or misconceptions had led to bad product decisions: when researchers used small-scale qualitative research to conclude that one prototype was more appealing than the other, instead of focusing on how and why certain aspects of each prototype worked well or poorly.
While the rigor of the qualitative research was certainly a problem, there were several other reasons the rolling research program was limited in its impact. The questions had been selected to fill rolling research slots, rather than through the lens of riskiness or potential for impact, and therefore focused on small and non-urgent questions. The insights were scoped only to singular features, rather than overall patterns and behaviors. The solutions focused on organizational pain points rather than user needs. As a result, non-robust research data had potentially led to poor product decisions and outcomes, making it harder to “surface and evangelize…accurate and fact-based about user needs, expectations, and behaviors” (Carey 2019).
It is common, especially in less mature teams, for people to lack coaching on the importance of examining the tradeoffs with doing research. This is problematic for research for a number of reasons: requests can outpace capacity (raising questions about whether research is a good use of the team’s time), and research effort may not always lead to impact. Moreover, research becomes a crutch for decision-making—something that teams turn to if they are unsure about the right path for a given product or project. In these cases, it can be beneficial to empower teams to say no to primary research and coach them instead on the range of approaches they can take to gain research-like insights, such as literature reviews, competitive/heuristic assessments, and stakeholder interviews.
I carried these questions and observations into my new research role, paying attention to how my colleagues talked about the importance of research and approached the craft of qualitative methods. Over time, as I carried out and supported several research studies, I realized that the push to open up research to non-researchers glossed over critical skills that researchers brought, which could ultimately influence key decisions in the organization. Firstly, researchers cultivate the skills to frame problem spaces and wade through complexity and ambiguity through discovery and research design. We are taught the importance of finding hypotheses to test, and identifying where there is the greatest ratio of effort to impact. This is often done by treating research requests as research projects in and of themselves; spending extra time at the beginning of projects to ask clarifying questions often yields research that is more tightly scoped and more precise on the intended impact.
For example, several team members approached me for help scoping a research project on why editors of city websites weren’t adhering to guidelines and rules around content best practices when they produced new pages in a content management system. The question was a good one, but I sensed that it was scratching the surface of the problem, and that we would have better insights and more impact if we framed the problem differently. I asked my team members to elaborate on why they thought editors were behaving in certain ways; this revealed that problems that manifested in the creation of content in a content management system were influenced by many upstream things, like onboarding and training. As a result, we expanded the scope of the research to explore the major challenges and pain points editors were experiencing throughout their editing journey. As a result, the team realized that compliance was not really the issue, and that a misalignment between editor skills and values was creating conflict instead.
Another skill researchers bring, which often goes unremarked upon, is the ability to carefully select and tailor approaches and insights. Researchers use multiple inputs—questions to be answered, resources and time available, potential for impact—to gauge which method works best for a given project. Once the project is going, researchers are trained to recognize and respond to various types of bias, such as the selection of certain participants or the disconnect between what people say and what they do. After data has been collected, researchers use their knowledge of stakeholders and the organization to frame insights as a response to key questions and to showcase a strong point of view, which ultimately helps the research resonate and have more impact. Having tools available does not lead to insights (Mitra 2020), because “Anyone can collect data—it’s knowing how to collect it, what to do with that data, and synthesizing the results into valuable actions that’s the hard part” (Carey 2019).
For example, when our team was just beginning to think about a visual refresh for SF.gov, I started a research project to explore what makes a city website feel like a city. This research asked participants to compare their experience across several municipal websites, to reflect on what elements of each website reminded them of the city and enhanced or detracted from their overall experience. One path forward would have been to present the results in a straightforward way, outlining what was good and bad about each website and why. However, I knew the team needed other help and impact. They needed to understand what role aesthetics played in the overall experience of using a government website. For example, was usability more important than the look and feel of a website, and if so, what did that mean for future designs? Moreover, in the early stages of a redesign, the team needed high-level guidance about design values rather than insights at the feature level. Because I had the research skills not just to collect data, but to frame the insights in response to issues the organization was facing, the research had widespread impact.
Using Ethnography to Understand the Needs and Values Underlying Democratization
Given my concerns and fears, I initially resisted democratization. I doubled down on research quality and process, creating intake and review processes, as well as an 8-week “research curriculum” to teach non-researchers fundamental qualitative research concepts. But my resistance to democratization was met with resistance itself. My efforts to bring a focus on rigor and quality were met with skepticism and frustration. Team members didn’t see problems with what had been done before; instead, they saw me as a gatekeeper of research, as someone who was preventing the team from connecting and empathizing with users. As a result, my team members didn’t view me as a trusted expert. They felt forced to jump through seemingly unnecessary hoops when their process worked fine.
Pretty soon, I knew my approach—resisting democratization by pushing my team members to adopt my own standards and values around research rigor and quality—was not working. The overall volume of research decreased, and stakeholders stopped coming to me with questions. So I did what any researcher would do: I approached the phenomenon as a mini research project, using ethnography as a way to understand the root causes and themes, and to explore the historical and current research practice (Nash 2023; Knoll 2023; De Paula, Thomas, and Lang 2014; Thomas and Lang 2014; Bandyopadhyay and Buck 2015). What was the organization trying to achieve with democratization? What did it need and value? What was it optimizing for? How had research been conceptualized and leveraged in the past? What was the culture of decision-making like?
I began with informal interviews with key members of the team, making sure to speak to people from a variety of backgrounds. I wanted to know about their past experience was with research, and how it had been shaped by the context in which they had worked. One teammate, who came from another established civic tech organization, explained how research had been a “team sport.” For them, weekly team meetings had included video clips of user research, and widespread participation in research had increased the organization’s connection to its end users. Another teammate from a small tech startup explained that gathering quick feedback on early-stage features had been standard practice for product managers because researchers were not present on the team.
All of this pushed me to ask important questions. What did my colleagues’ desires around democratization reveal about their own needs, and the needs of the organization, when it came to research? My team wasn’t an academic institution or a large tech company, so did it matter if a teammate produced a sub-par research report, or none at all? Turning my questions inward, how had my past experiences shaped my views? What other compromises was I willing or needing to make? What expectations were reasonable to place on others, when it came to following process and reporting on insights?
As a start, I explored how the needs around democratization were different in my current organization compared to the private sector. My new team was not focused on increasing speed and efficiency, at least not at the outset. The desire to democratize research was not driven by a need for faster insights; rather, it was born from a recognition that one researcher would struggle to support a team of 50, much less an entire city government. If the team’s overall mission was one of scale, to provide internal tools and processes to level up city staff, how could one person support that? (There is also something meta here, in that the team’s overall mission is one of democratization in and of itself: SF Digital Services saw itself as providing the tools and frameworks to help other city staff deliver digital services themselves. Perhaps this contributed to the idea that knowledge production could and should be participatory.)
Similar to the needs around democratization, the team’s capacity for and approach to research was highly contextual and different from the private sector. In my previous role, stakeholders were often too busy to participate in (or did not place value on participating in) research studies. It was difficult to “bring people along” with the research, as I was constantly vying for their time and attention. On my team, because the pace of work was slower and more deliberate, stakeholders had more time to engage with and even conduct research. They respected that researchers were as busy as other team members, and did not expect researchers to always carry out studies. However, I noticed that when non-researchers came up with and initiated research projects, they tended to focus more on methods than questions. In other words, they more intrinsically saw the value in talking to users of their products (compared to the private sector, where research is often an afterthought compared to A/B testing), but didn’t necessarily know how to conduct or initiate research in an impactful way.
Another thing that emerged in informal interviews with colleagues was that not everyone had the same definition of “research.” For example, tensions arose when a product manager wanted to do research with users to explore reactions to an early-stage prototype of a new content management system.[5] Was this research? Who were they talking to, what hypothesis and problem were they exploring, and what changes would be made? In this case, overlapping roles and terminology made the definition of research murky. In another example, a content strategist and researcher both wanted to use a card-sorting tool, which is often construed as a “research tool,” but for different reasons. The researcher planned to use the tool to explore residents’ mental models around groups of city services, while the content strategist planned to use the tool to get buy-in from city staff. Tensions arose when both the researcher and content strategist presented the outcome as “findings.” Different levels of rigor had been applied to the methods and framing of the two projects, which created confusion around how each set of “insights” should be leveraged and used to make decisions.
“Research,” I realized, was thought of and used much more broadly than I or my research peers would have intended (Carey 2019). It was synonymous with gathering quick input or feedback, generating empathy, building consensus, and doing community engagement (Democratic Society 2023). This took the shape of comments like, “I got feedback from some colleagues about X” or “We did some quick research to validate Y” or “We need to hear from our users more.”
This is not to say that things like customer feedback aren’t useful or valuable activities; they are “great for building empathy, sparking product ideas, and providing real-world examples of product usage… [but are not] representative or rigorous” (Nash 2023; Mateljan 2022). But as Jen Pahlka artfully describes, user research and things like “public input” are not the same (Pahlka 2024). Feedback is often unconcerned with bias around sampling and self-described behavior, and is focused on “listening” rather than exploring problems or proving hypotheses.
Over several years, what I learned was: construing research with these activities created confusion over goals and outcomes. Missing from these so-called “research” activities was a focus on testing hypotheses, methodically and rigorously approaching problems rather than solutions, and centering the pain points of users over the organization. But for my colleagues, these considerations were not important, because the activities they called “research” ultimately had different goals, like building consensus and generating empathy. For my colleagues, it wasn’t important to be rigorous or to focus on quality, and as a result, my own emphasis on quality and rigor landed on deaf ears.
This was a pivotal moment for me. I realized that the organization didn’t just need more clarity on who should do research. It was at a state of maturity (Bandyopadhyay and Buck 2015; Metzler 2020) where it needed help with knowing how to make decisions and create clear priorities—for which research is one of several valuable tools (Mazur 2023; Blom 2023; Dombrowski 2021; Yost 2016). As a result, I needed to redefine and clarify what “research” was and how this research vision could better serve the organization’s problems, needs, and goals. And even more importantly, I needed to help the organization increase its capacity to make informed and careful decisions, while showing that research was one tool among many. Ultimately, my role transformed into one of organizational change, where I was attempting to exert influence on and change multiple levels of the organization, as a way to carve out a different role and set of practices around research.
Using the Lens of Risk to Deliver Impact with Care
As I engaged with this challenge—helping the organization improve its decision-making skills while also showing how research should and shouldn’t play a role—I decided to use the lens of risk. I have drawn on this concept throughout my career, as I have struggled to handle high volumes of research requests. Seen through the lens of risk, the question becomes not “Should we do this research?” but rather “What is the risk of doing or not doing this research?” (Cuciurean-Zapan and Hammel 2019; Lalley 2019; UX Guys 2016).
This reframes the conversation around tradeoffs and encourages people to project into the future, to consider the possible outcomes that research (or a lack thereof) can lead to. By opening up the logic around research decisions, stakeholders are empowered to become better decision-makers across the board, using risk as an input alongside impact and effort. Instead of feeling compelled to conduct research on features before launch, out of worry or a need for validation, stakeholders can assess the risk of decisions and courses of action, while also considering the various tools that are available to them to mitigate those risks (Belt 2019).
In this paper, I advocate for researchers and ethnographers to use the concept of risk not only to determine whether research should be done, but also how it should be done and by whom. The focus is on how opening up research can create different types of risk at various levels of the organization. This creates a clear set of considerations for researchers to guide how they include non-researchers in and scope research projects.
Below, I outline the various risks that opening up research to non-researchers can create, and provide examples of how this manifested in specific projects.
Risk to the Product and End Users
If research is a crucial aspect of product decision-making, what happens if insights aren’t robust and lead to poor product decisions? If qualitative data is not interpreted with care, or if personal experiences and anecdotal observations are encoded as research, the wrong features might be selected and invested in. This may have adverse effects on end users, inadvertently making features harder to use, or creating features that do not solve actual user problems (and overall decreasing the value products provide to end users).
For example, in my previous role, I conducted research on settings for a social media app. At the time, it was customary for us to select a “random sample” of participants, which often included a mixture of age, gender, income, and occupation. This research often led to conclusions that participants wanted more control over their settings, which led the product team to create granular controls for various aspects of the settings experience. However, I prompted the team to think more carefully about sampling, and to consider how participants with lower digital skills could provide pivotal insights into the settings experience. When we included these participants in the research, we learned that granular settings created serious problems for people with lower digital skills. Participants had little awareness, knowledge, and experience with settings, and as a result, felt fearful and unempowered by granular controls, because these placed an increased burden on participants to learn about and manage complex user experiences—often through the language and framing of internal features.
Risk to the Research Practice
Given research teams are often small and understaffed, saying yes to one project almost invariably involves saying no to another. If non-researchers are not encouraged to use a rigorous process to determine whether research should proceed, they risk taking researchers’ time away from other more impactful projects. Over time, the research practice risks having less impact and becoming narrower and less foundational—focused on answering specific questions rather than driving strategy—unless non-researchers are trained and encouraged to look beyond tactical, evaluative work. Moreover, there is a risk that research becomes a panacea for everything, diminishing broader problem-solving skills that can leverage other forms of expertise and approaches to decision-making.
For example, a team member approached me with a research project to evaluate changes to the page where editors would log into the content management system for SF.gov. The team wanted to “make sure” that the changes were not going to have a negative impact, so they assumed research could help answer that question. However, when I took them through research intake, I encouraged them to think about how significant the change was, and what bad outcomes they thought might arise. As we talked, they realized the change was not large enough to significantly disrupt the page’s user experience. In the end, they decided not to do research, which freed up my time and their time to do other work.
Risk to Participants
A foundational part of training to be a researcher is learning about participant safety and ethics. This leads to practices like ongoing consent, verbal informed consent, anticipating questions that could bring up trauma, carefully asking about highly personal experiences, deleting data after a certain period of time, and more. We are often taught to put the safety and experience of the participant first, above all else, but what happens if we allow others without this training to do research with these populations? What potential harm can this cause to participants, and how might that reflect poorly on the team and the product?
Example: When our team began working with a Department that administered a permit for street vending, we decided to research the user experience of the permit form, because we knew that the people who needed the permit were from marginalized communities—according to the city partners and community organizations who helped applicants fill out the form, this group of people did not speak English, had lower rates of literacy, and also had lower digital skills. However, neither I nor the other researcher on the team were fluent in Spanish, so we had to figure out who would conduct the research. A non-researcher on the team was fluent in Spanish, so she assumed she would conduct the interviews. However, as researchers we were highly concerned about participant safety: was it ethical or appropriate for someone without training in research ethics or trauma-informed research to work with marginalized communities? What would happen if the participant disclosed something highly personal or if a question brought up past trauma—would the non-researcher know to pause the recording or switch from questioning to comforting?
Risk to the Organization
When we do research that spans a wide range of topics and scopes, there is always the potential that we will conduct research on things that are political or sensitive—and which might lead to negative press, lawsuits, or government inquiry. This might entail recruiting participants to interact with a form for a competitive grant application before the application is available to the broader public. It might also entail researching something that carries legal risk to the organization, where if that research was leaked, the organization could be subject to lawsuits or other public inquiry. This type of risk is often less visible to individual contributors, given their more limited view of the organization, but it is still important.
Putting it all Together: Leveraging Risk in Decision Making
So how do we as researchers navigate these risks to make decisions about how to support our teams with research? On some teams, researchers advocate for guardrails, and for reducing the scope of work by non-researchers to tactical research. But on my team, I created frameworks and processes that opened up the decision-making process to non-researchers and taught them how to assess tradeoffs around risk, impact, and effort.
Through a process of research intake, we asked non-researchers questions like: “What would happen if we didn’t do the research?” and “Will you be working with any sensitive populations?” and “What will change as a result?” We also assessed the positionality of non-researchers, exploring their past experiences with research and interviewing, their willingness and availability to learn, and their familiarity with and expertise in the problem space. This took the shape of a guided conversation, where non-researchers were asked to think through questions on their own, and then were encouraged to discuss them in the open later.
This encouraged the team to spend more time at the beginning of a research project than they otherwise would have, as an investment into thinking through potential positive and negative outcomes. It also increased their decision-making skills and empowered them to assess the best course of action—increasing the organization’s overall skills. Over time, I saw my teammates’ frustrations with what they had formerly perceived as gate-keeping fade, as they were exposed to the logic that led to nuanced decisions. Conversations shifted from “no you can’t do this” to “here’s what could happen if you do this.” I also saw my teammates start to understand and empathize with the decision to have trained researchers conduct studies with marginalized populations or highly ambiguous problem spaces. Through the process of intake, they were exposed to the importance of experiences with ethics and trauma-informed research—and the potential harm that could come to participants. They were also exposed to the complexity of scoping and prioritizing impact for nebulous, ill-defined projects.
Ultimately, the process of research intake, and the surrounding conversations it generated, made clear the decisions about:
- Whether the research should proceed, or whether we should leverage other decision-making tools, like relying on external research or doing an expert evaluation
- Who should lead the research, and how closely the researcher should collaborate on various aspects of the work
- How narrowly or broadly the research should be scoped
- Any precautions that need to be taken or check-ins we need to have to go over specific topics or practices.
This is not a perfect approach, but it has allowed us to open up the research practice in a way that helps the organization increase its ability to make decisions while mitigating various types of risks. What this requires of us as researchers, however, is an investment in being present through the entire research process, not just at the beginning—as both players and coaches. We must learn to say no in a way that resonates and provides other options, and we must invest in continued relationship building with team members (often outside of the research realm, by embedding ourselves in teams), which buys us the goodwill to shape the outcomes of projects we do not always own.
Conclusion: Can Ethnography Save Itself?
As ethnographic practitioners, we are inherently familiar with the concept of democratization. Through the very act of doing ethnography, we accept that our expertise is complementary and often secondary to the expertise of participants, whose lifeworld we seek to elevate. Less familiar, however, is the attempt to democratize our own expertise. We reject (often in a knee-jerk sort of way) the idea that our stakeholders have equal experience and expertise with research ideas, methods, and practices.
So what happens, then, when we turn this phenomenon—this insistence that anyone can do research—into an object of fascination and inquiry? Ethnography is uniquely suited to this task. How might our skills help us navigate this moment of institutional and disciplinary precarity?
Ethnography isn’t going anywhere. The title above is surely sensationalist. But my point is: simply pushing back on democratization, by insisting on the primacy of our own expertise, is self-defeating. Doing so does not address why democratization is often so appealing. As such, this paper encourages ethnographers to put aside our pride, to push against our urge to protect our discipline, and to instead see ourselves as organizational change leaders. The goal of this paper is not to argue for or against democratization, but rather to use the push for democratization as a way to interrogate an organization’s needs and figure out ways for research to have more impact by satisfying those needs. “Research has been done, and will continue to be done, by people who don’t have “researcher” in their title. It’s imperative that we improve the quality of their work, rather than pretend that it doesn’t exist” (Sirjani 2020).
Why does this matter? Why should we bother? The stakes here are bigger than our job security. If we do not insert ourselves into the democratization debate, shaping how it plays out in practice, research will have less impact. Poor decisions will be made. People might be harmed. What counts as research will not be shaped by those who have the most power to make sure research is impactfully and carefully done. How might we turn our fear into (guarded) curiosity by leveraging our ethnographic skills to ask questions like: how is “research” getting redefined and why? What counts as training and expertise, and why? What problems are truly being solved here, and why?
So what does “good” democratized research look like? Where is the most impact to be had as a researcher, and how—when considering how to intervene in democratized contexts? Through the lens of risk, researchers can make better decisions about where to invest their time and energy and where to push back against stakeholder notions of research topics and practice. Researchers should be empowered to define democratization on their own terms, based on what the organization and end users need. As such, this paper is an invitation to consider how to leverage ethnography to intentionally shift and redistribute power throughout the research practice and process.
About the Author
Nadine is an anthropologist and UX researcher, with a focus on strategic research, improving equitable access to technology, and combining ethnography and quantitative research. She currently leads the design and research team at the City and County of San Francisco’s Digital and Data Services team, whose work focuses on improving the digital experience with permitting and forms, as well as helping the city centralize and scale its digital resources on SF.gov. She has previously worked at Meta, UCLA, and the University of Exeter, after completing a DPhil in Social Anthropology at Oxford.
Notes
Many thanks to Kate Zykowski, Matthew Bernius, Cyd Harrell, Taylor Nelms, Jennifer Ng, and Andrew Symington for their thoughtful feedback and insights on this paper.
[1] A turn which is, in some ways, reminiscent of how research was done in organizations before the “professionalization” of user research.
[2] The term “human-centered” refers to the idea that people (customers, users, etc.) are at the center of business and creative processes. With a human-centered approach, teams are empowered to “design products, services, systems, and experiences that address the core needs of those who experience a problem” (DC Design 2017).
[3] According to one survey, over two-thirds of designers and half of PMs do research, with that research skewing toward evaluative and qualitative work (Akhmedov 2023).
[4] Here, I follow a similar approach as this paper (Cefkin, Anya, and Moore 2014), which takes the rise of more open and distributed work, and questions the conditions which give rise to it as well as the impact it has on work relations.
[5] This product manager had come from a company where their responsibilities were porous—something that is common in smaller, less well-resourced organizations, where people must function as “jack-of-all-trades.”
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