Abstract
Introduction
One of the key promises of present-day data analytics is that by developing data-driven processes, organizations, and businesses can reduce reliance on intuition, hunch, and gut feeling. In stark contrast to these visions of rational, calm, and emotion-free decision-making, empirical studies from different creative industries imply that working with data analytics actually provokes a large spectrum of emotions (Ahva & Ovaska, 2023; Choroszewicz, 2022; Kennedy, 2016). When looking at the use of data analytics tools in game development, insightful empirical studies are hard to find. While the discourses around game industry data analytics have been recently studied (Egliston, 2024), we still know relatively little about how gameworkers feel when working with game data.
This article focuses on game industry data work and its emotional aspects. When we began a research project on the use of analytics in game studios, the affective side of the work was not our primary focus. However, when we began to conduct interviews with game industry professionals, this aspect was more present than we had initially thought: we asked about data and the informants started to talk about how they felt about it. We have previously shown how data-driven working methods create new practices and work cultures in the game industry (Sotamaa et al., 2023) and have analyzed the forms and dimensions of game data work (Tyni et al., 2025). The objective of this article is to understand how these changes are perceived on a personal level, and what sorts of emotions are associated with the new working practices related to data use.
Emotion design has long been a focus of game design (Isbister, 2017). However, given how game scholars have recently taken an interest in issues such as measuring emotional response (more closely tied to formative design practices like focus groups) (Croissant et al., 2023), affective labor in and around games (Anable, 2018; Welch, 2018), or transgressive aesthetics (Mortensen & Jørgensen, 2020), one could possibly argue for an emotional turn in game studies. At the same time, game developers’ perceptions of their work have seldom been analyzed from the perspective of emotions, and while the emotional labor associated with gamework has been discussed in a few pioneering studies (Bulut, 2020; DeWinter et al., 2017; Harvey, 2019; Harvey & Fisher, 2013), specific questions connected to the emotional side of game data work remain mostly unanswered. Prior studies have shown how algorithms have the power to make consumers uncomfortable, irritated, and angry, as well as satisfied and positively surprised (Bucher, 2017; Ruckenstein & Granroth, 2020). With this study, we want to explore how these kinds of emotions are also present in the production of algorithms and the datafied services built on them. By analyzing the reactions and experiences of game industry professionals, we can inaugurate novel insights into data work in general, and the use of game analytics in particular. Following D’Ignazio and Klein (2020), we believe in elevating emotions as a form of knowledge to better understand how data practices are situated in a particular context, place, and time.
The article is based on interviews with Finnish game industry professionals. Finland has a strong, albeit small, game industry scene that has been heavily focused on mobile game production. Success stories in the Finnish game industry have mostly been free-to-play mobile games (Sotamaa, 2021), and this is exactly the game industry sector in which data-driven design methods, performance marketing, and monetization design have become an essential part of game development processes (Kerr, 2017; Nieborg, 2016). The results of this study help to unearth emerging types of emotional work in data-intensive professions in general and contribute to a more detailed understanding of the emotional work generated by current-day game development processes. We also hope that our work can advocate a wider recognition of the demands associated with game data work and help to find more emotionally sustainable ways of navigating the game development space.
Gamework and Emotions
In her classic study, Hochschild (1983) highlighted how employee's management of emotions is central to the critical analysis of workplaces and organizations. This emotional labor perspective explains how emotions at work are both shaped by broader cultural norms and regulated by organizational guidelines. In this sense, emotions should not be considered as personal experiences only (c.f. Ahmed, 2004), but rather as structures that organize and create order in varying contexts of working life. Hochschild's original focus was on service work, but it seems clear that creative workers are also familiar with facing emotionally loaded situations and managing their feelings as part of their everyday work. In fact, while having a passionate and emotionally loaded relationship to work is common to various fields (Pollack et al., 2020), creative work is often seen to require specifically intensive emotional investments (Hesmondhalgh & Baker, 2008).
In the past years, with the increased overall interest in affect and affectivity within humanities and social sciences, the previously neglected role of emotion in media production has been gaining increasing academic attention (Siapera, 2019; Soronen, 2018). The datafication of media work and the emergence of more detailed audience metrics have not only created new requirements for media practitioners but also generated new forms of professional insecurity and moments in which practitioners need to process these uncertainties emotionally (Ahva & Ovaska, 2023; Petre, 2021). Similarly, game developers face moments in which they do not necessarily understand what the numbers mean, how the company data pipeline is built, or why the results of one data analytics tool differ from another. We are especially interested in how game developers gain pleasure working with data analytics, and how they adjust their behavior in situations of insecurity and conflicts with data.
Prior studies on digital game production indicate that gamework is often associated with intensity, dedication, and passion. Kerr and Kelleher (2015) analyzed game industry job listings and found out that game industry employees are frequently expected to be passionate about the game, the company, and the player community. While passion work is often promoted to be meaningful and satisfying, the widely agreed requirement of having an impassioned attitude toward game production can also be arduous and harmful to individuals, as highlighted for example by Ozimek (2019, “hope labor”), Bulut (2020, “sacrificial labor”), or Keogh (2021, “self-exploitation”). Harvey and Shepherd (2017) further argue that characterizing work in digital games production as a form of passionate, affective labor renders some forms of gamework as credible and legitimate while relegating others to subordinate positions within hierarchies of production.
With the increasingly central role of mobile gaming and accessible digital distribution channels, the game-as-a-service model has risen in popularity. The focus of game production has shifted from boxing a game and moving to the next product to hooking up the players and developing the game for as long as it is profitable (Dubois & Weststar, 2021; Mäntymäki et al., 2020). Providing games as long-lasting online services requires continuous content development and the so-called live operations. These sorts of game production models rely heavily on collecting and analyzing data, and shaping the product accordingly. Developers have often found themselves balancing between creating a fun game, gaining revenue, and increasing the conversion rate (Alha et al., 2014; Dubois & Weststar, 2021). The increasing role of data analytics has affected the working methods of game designers, product managers, and CEOs, as well as created entirely new roles in the game industry such as data analyst, data scientist, performance marketer, and monetization specialist (Kerr, 2017; Van Roessel & Švelch, 2021).
Based on prior research, conflicts between the creative aspects of game making and the effective utilization of data analytics seem relatively common (Mäntymäki et al., 2020). Putting focus on data analytics can effectively limit the resources available for creative work and change the priorities of game development teams (Whitson, 2019). At the same time, the attitudes toward data analytics can change over time. Spending time in environments that favor communicating ideas through quantified data creates gameworkers who are increasingly fluent in “data talk” (Sotamaa et al., 2023), although that does not necessarily mean that they are more satisfied with the data-intensive working environment.
In his recent study of the promotional rhetorics around data analytics tools, Egliston (2024) has highlighted how the relationships between data and game developers are dominantly articulated in deterministic and commercially motivated terms. Data analytics are not only promised to attain greater control and autonomy over the game development process but also to save time in doing so. Using data analytics software is presented as being emancipatory, but the critical issues affecting work–life and well-being are rarely addressed. While these companies often frame the benefits of their tools in terms of ameliorating risk, their implications for addressing systemic issues like precarity, overwork, and the highly individualised entrepreneurial ethos of game work are limited (if not harmfully exacerbating these issues). (Egliston, 2024, p. 17).
Before we move on to discuss the methodological aspects of the study in more detail, a terminological note is required. When interviewing analysts and other industry professionals for this research project, we were open to the various meanings attached to the word “data,” regardless of its form or presentation. For this article, our understanding of data is inspired by Bucher (2017) and Seaver (2017), that is to say, we are particularly interested in how data can be argued to create a culture of its own, including broad patterns of meaning, emotions, and practice.
Methodology
The initial objective of this research project was to explore the impact of data-intensive development practices on the game studio work practices and cultures. Thematic interviews were deemed as a fitting approach for an explorative study that necessitated both flexibility and reflexivity (Hyvärinen et al., 2017). Through professional connections, we recruited 20 interviewees from Finnish game studios. The first author had over 15 years of experience in the Finnish game industry, and this helped a lot in gaining access to a diverse set of participants. The interviewed professionals represented different occupations in the game industry, and in terms of industry experience, they ranged from interns to seasoned industry veterans. The size of companies our interviewees worked for varied from some of the largest in Finland to tiny two-person startups. The final interview sample included an even number of females and males and two people who identified as nonbinary. The informants represented five different nationalities, with the majority being Finnish and one-quarter of the participants coming from abroad.
Interviews were conducted online due to the COVID-19 pandemic by two authors. All except two were one-on-one interviews; in one interview, both interviewers were present, and one interview provided information about work group dynamics by having three interviewees from the same company present. While the original focus of the project was quite broad, at some point the first author started to pay attention to the emotions that the interviewees attached to game data work. When we asked about data use, the informants often started to talk about how they felt about this. Our open-ended questions focused mostly on the use of analytic tools and services, responsibilities and communication around data work, and ethical issues related to data-driven game development. The questions that ended up being most relevant for this article mapped interviewees’ perceptions of how analytics affected their game design practice, ethical problems related to collecting and using data, and how issues related to data work were communicated in their workplace. However, notions about data engagement and emotions were consistently raised during the interviews. When planning both the overall methodological approach and individual interview questions, we were aware that the topic was potentially sensitive and required special attention to creating a safe and trustworthy atmosphere. In addition to communicating the key aspects of the study with detailed by creating an extensive privacy notice (following both General Data Protection Regulation [GDPR], Wolford, 2024, and the Finnish Data Protection Act 1050/1080; Ministry of Justice, Finland, 2024), we also highlighted the opportunity for participants to withdraw from the study at any point and committed to a careful pseudonymization of data. These preparations may have contributed positively to the informants’ willingness to talk about the emotional aspects of work. At the same time, insecurities and annoyances with data analytics were something that game developers really wanted to talk about, and it felt as if some of the informants had clearly waited for an opportunity to share their experiences with someone.
The coding phase of the analysis relied on a positional reflexivity between researchers (Anderson et al., 2016) and inspired by the blended approach introduced by Graebner et al. (2012) our process included characteristics of both inductive and deductive coding. The authors first agreed on the basic principles of coding, after which two of them took responsibility for the coding process. The different backgrounds of coders were considered a strength, and they held regular debriefings to make sure that the process consistently followed a shared coding scheme. All three researchers reviewed the identified themes. Codes were derived both from prior research literature and from phrases used by interviewees. Eventually, our analytical process included both inductive and deductive elements. While certain early theoretical constructs discussed in the prior chapter provided initial inspiration for our coding, others frameworks were introduced in the latter phases of the process.
Emotional Work in Data-Intensive Game Development
In the following section, we will first discuss some of the positive emotional responses to the everyday use of data analytics. We will then move on to more critical aspects and show how forms of emotional work are shaped by various contextual factors.
Data as Your Helpful Workmate
Many of our informants found working with data to be beneficial for their work profile and professional skills at some level. For them, data analytics had become an everyday tool that they did not want to give away anymore. This shows how game work is now restructured in a way that data are often needed to make sense of the current game development. In the context of emotional work, data, and analytics act as tools to organize oneself and one's work, in this way alleviating anxiety and uncertainty toward marketing and design decisions—anxiety that the introduction of data-driven platform logic into game development (Kerr, 2017) arguably helped to create.
Informants who declared both needing and liking data took full advantage of it to succeed in their work and to develop in their profession. For many of them, data-driven design had become such a natural and internalized part of the work, that they found “I feel that it [data] improves me as a designer because it helps me see the perspective of an average audience member on the game, versus how you yourself feel. A pet peeve for us designers is always making games too difficult because we are experts at playing our own games. If we would design without data, all games would be extremely difficult.”
Moving beyond the benefits of data to one's personal work, the informants often connected their work to the wider role of data in F2P mobile game studios, where the goal of these companies is to make their games as achievable as possible, to keep players engaged, and through engagement, sell more. Clearly, data make the development process clearer and more pleasing, while at the same time directing the developer's attention to what seems to be most relevant about the live development of the game. Reflecting on these findings, in their study of video game developers, Mäntymäki et al. (2020) talk about analytics as a
There is also something seductive or alluring in the data. While our informants ensured they mostly followed reasonable daily working hours, some of them admitted that they also looked at data in their free time. “Sometimes you just can’t help but check,” stated Pasi, a senior data professional, who was otherwise very strict about sticking to his work hours. Martta, a senior-level marketing professional, explained: “[It is like] savoring the moment, like ‘oh, look at how many players we got’ and ‘oh, look at the numbers we’re getting here’. Those moments are more about celebrating your own work, being unable to wait until Monday to see the numbers. You feel compelled to check the numbers of the Friday event already on Saturday, to see how it went.”
When data are extensively used to provide answers in terms of one's work and foster positive emotions about collaboration, its role can evolve from a mere sense-making tool to resembling a colleague. For some of our interviewees, data seemed to have almost become a living entity, with the algorithm perceived as an agent in the production environment, possessing a mind of its own. This kind of close relationship with data was reflected in the ways that interviewees discussed data, analytics, and algorithms. They used phrases such as “let's ask data” and “the algorithm is like a bit of a silly colleague” and shared stories about algorithms seemingly making personal decisions such as “the advertising algorithm takes a look [at the player] and goes like ‘this poor sap is not going to buy anything’.” One informant in a management position relayed a story about another game studio replacing a worker who had a relatively simple job with an algorithm. This kind of rhetoric that blurs the boundaries between humans and machines is becoming increasingly common, with examples ranging from social situations like computerized customer service to casual chats with Siri, Alexa, or ChatGPT (Guzman & Lewis, 2020; Laaksonen et al., 2020; Paasonen et al., 2023). In the game studio context, this development is visible in various situations. In the following section, we will discuss in more detail the impact that data and data-driven processes can have on one's professional identity.
Questioning Your Professionality
Boosted by quickly growing machine learning capabilities, it seems that in many game studios data and algorithms have become normalized everyday actors with decision-making power. There is a fine line between taking such automated decisions for granted, and them becoming intrusive or limiting to human workers. For workers who find themselves at odds with algorithmic decision-making, coming to terms with any human vs. technology conflicts might become emotionally taxing. Astrid, a producer in a mobile game company, described her feelings like this: “I always have strong opinions about everything, and data often proves my opinions wrong. And I often jokingly say that clearly, based on the data, I should make a game that I personally hate, and it would become a success. […] For example, something like choosing a character—my top choice, the character I would use versus the character most players actually choose—I sometimes go like ‘excuse me, what!?’ Or take for instance some problem-solving issue, something that has been very obvious to us designers, [something like] ‘of course the player presses this and goes there’. And then we look at the player data, and they do the exact opposite. You learn a lot about people that way, too.”
In addition to underscoring the developer frustration over not being able to harmonize technology with human intuition, such concerns reflect previous studies that have highlighted game developer concerns about losing creativity when using data (Alha et al., 2014; Mäntymäki et al., 2020; see also Clark, 2014). Observing how the data-driven game development model embraces flexibility and the ability to quickly respond to player metrics, Weststar and Dubois (2022, pp. 9–10) argue that the model also seems to reduce creative autonomy and actually
Again, there were also contrasting views to these concerns, with some informants arguing that there is no need to follow data—but “why wouldn’t you”? Michael, a product owner in a small mobile games company, added: “Data guides decision making, to some degree. It doesn’t dictate decision making, but it guides it.” These informants had clearly come to terms with balancing design intuition with algorithms and analytics. Like Michael, many of them had entered the industry only when it had already been almost fully eclipsed by the data-driven production logic (Kerr, 2017). However, it is clear that for some participants this balancing act was emotionally more laborious, both when they were considering their own work as well as the work of the industry as a whole. Kai, a game designer working on a networked premium game outside the F2P-model, tried to come to terms with and bring together the two different kinds of game design approaches—the design intuition-driven approach and the data-driven model: “I prefer the kind of design method [where] we have some kind of a vision we want to convey and [we] then make a game based on that. Whereas I think it's kind of an opposite approach to have some framework into which we want to design the game, then letting the analytics and data guide the game design process and what results from that. So, these are two somewhat opposite approaches, and well, both can apparently work. But yeah, that kind of analytics-based approach—when you look at those mobile game numbers—you’ve got to admit it's effective.”
Forced and Frustrated
Operating in the free-to-play business model necessitates the use of data, often even before you have a game in the market. As Wilma, a CEO of a start-up company, pondered, “if you don't collect that data, you won't get investments, and how do you then pay the salaries?.” One facet of emotional work repeated in the interviews was the frustration of having to do too much data work. This was evident especially among the managers of smaller companies and owners of early-stage start-ups, who while feeling the pressure to measure and analyze data, often lacked the resources and time to do so. These companies were typically not large enough to hire a data analyst or other such professional, with the CEO or a person in some other management role having to analyze data instead. Peter, one of the founders of his company, described the problem this way: “We do not do data-driven design. […] That process—where we would follow the metrics that should be improved, then make improvements and measure whether the metrics improved—we've noticed it's so slow and laborious. At this stage of our company, it is just better to focus on developing the game rather than stopping to measure everything. And rather than measure the player behaviour [using analytics], listen to them more qualitatively at the forums. […] Stalking the dashboards is boring. There are so many moving blocks in the equation and doing the analysis through the dashboards is quite slow, so it is overall a pretty frustrating operation.”
Among our informants, there were also some who completely despised data-driven design, claiming that it only resulted in mediocre games. Karl, a CEO of a small but successful casual games studio, described his reluctance to use data as follows: “I understand the use of analytics in game development. I understand that. I don't necessarily do it myself, because, in my opinion, the use of analytics in game development leads specifically to the tyranny of the masses. If you want to make a good Big Mac, you can do it with analytics. But you don't do anything better that way.”
Finally, there were some participants who, on the surface, had a very practically oriented stance toward data, describing how data were steering the development process in a non-negotiable way, whether they liked it or not. Jan, an early-stage start-up founder, described: “It's not that we're going to go [out there] and succeed with our first publications, they are more for data collection. [–] Once we get the data from these [products], we will be able to use it to move in the right direction and we’ll see if that works. Of course, there is a possibility that we will have to completely change what we are doing if we notice it's not working. So, data plays a really important part.”
Trying to Reconcile the “Evil Company Syndrome”
Manovich (2011) has argued that as a key scientific and economic approach in societies, the explosion of data and the emergence of data analysis are sorting people into ‘data classes’, that is, dividing them into
When asked whether they had any ethical considerations related to data or its collection, several informants started to talk about the ethicality of data in an agitated way, as if to address lingering accusations of unethical behavior. Wilma, CEO of a mobile game start-up company, responded: “Yes, I strongly see them [data and ethical issues] as being related. And it is a difficult question because you see the situation both from the consumer and the business side. So, we're there, the ‘evil company’, scouring Facebook for players who’d spend money. But how we as a company see it: we don’t know who those people are. We are unable to identify them. We’re just putting out our creative assets, logos, and videos, putting them on air and it costs us thousands of euros a day. That's it from our side. Then if we look at it from within the game, it's: ‘users came in and spent money, and okay’. But the consumer sees it in a really personal way, like: ‘wow, I’m now being targeted and this feels really wrong’.”
However, some participants “[T]here is a potential risk of the data ending up in the wrong hands or in the hands of immoral people. Because, if you think about the kind of data I’m able to see in player profiles—[and let's say] I decide to become some kind of a criminal hacker—I see their IP and email addresses and which city they are in, and frankly all kinds of things. So if, for example, you couldn’t trust me to not do anything bad… well, there are potential risks and problems.”
When asked about possible company guidelines or rules for using and utilizing data, most of the participants admitted that there were no detailed policies or guidelines in their company. Ethical issues concerning data collection were discussed on an individual level with colleagues, either in or outside the company. Kennedy (2016) has noted how social media data workers bring their own ethical codes to work. Similarly in the game industry, most discussions of ethical stances seem to be based on employees’ personal considerations. From the GDPR perspective, employees do not own the data they use, rather their companies do (see, e.g., the analysis of data control inequalities by Cinnamon, 2020), and therefore the responsibility for making key decisions surrounding ethical data use and clearly communicating this to employees should happen at the company level. If the studios mostly worry about the practical dimension of handling data, then addressing the emotional work associated with these issues is mostly left to individuals.
Discussion and Conclusions
The increasing use of data analytics has, in many ways, drawn game industry professionals into new sensitive and emotionally charged sense-making processes. In this respect, they are not too dissimilar from other media workers who are required to manage the data analytics–related requirements of their work and to negotiate the role of analytics without sacrificing their sense of professionalism and professional integrity (Ahva & Ovaska, 2023, p. 155). Our study shows that instead of any one-sided effect, the use of data in game studios has multifaceted ramifications on how everyday work feels like.
All organizations, including game studios, create particular emotional cultures. Their nuances are connected to larger expectations of what sorts of behavior are accepted and favored in the current-day platformized media industry. Our study shows that these affective atmospheres are often built of paradoxical elements: while gameworkers are vulnerable to the pressures that a data focus brings, they also recognize how data analytics can potentially help some aspects of everyday work. Workers have to emotionally reconcile the complex interplay between human creativity and the intensified use of data analytics. When data question the professionalism of a gameworker, the individual needs to build structures to survive this conflict with their existing knowledge and skills. Paasonen et al. (2023) have shown how social media influencers feel frustration, stress, and confusion when the algorithm makes a decision to not show the content they produce. Influencers have also acknowledged that seeking success in the data-driven media environment easily leads to situations where the need to please data becomes the guideline for how decisions at work are made (Ruckenstein, 2023). Our findings verify that these sorts of reasonings are also common in the game industry. But at the same time, the resources needed to navigate this landscape vary between individuals. Whereas senior gameworkers persistently continued to test new combinations that would get greenlighted by data and had learned their ways to balance their negative emotions, they were openly worried about younger workers and what a constant rejection of their ideas would do to their professional pride.
Contemporary game work can also require taking a stance concerning ethical dilemmas related to data usage. While data workers may have a good idea of how algorithms are built and how data are used, they do not necessarily have the power to control how the data for them are collected. Gameworkers also need to emotionally deal with the prejudices directed toward data analytics, and try to balance them with their personal ethical considerations. In addition to developing processes that support data work and effectively utilize data in game marketing and sales, studio management should also critically consider the ethical and emotional aspects that using data for work can provoke in gameworkers. With the advent of recent AI-supported tools and other advanced methods, data work is bound to become even more common and central to game production, and therefore, active work toward making data-focused processes more transparent and associated emotional cultures more visible and sustainable is needed.
An often-repeated observation when studying work and precarity in gaming industries is that game studios aim to hire people with a “passion” toward games, which usually results in problematic work cultures (see e.g., Chia, 2019; Consalvo, 2008; Jackson, 2023). However, our research focused on work the data-driven digital game industries and hints at such ‘passion work’ (McRobbie, 2015) gradually giving way to more ‘normal’ relationships toward work (Sotamaa et al., 2023). It is increasingly common to encounter people working in the game industry who feel they work a regular 9-to-5 job. This transition away from ‘passion work’ has been largely enabled by the data-driven gaming industry's move to a production model that includes many new roles, such as jobs focused on collecting, organizing, and processing data, alongside the regular core skills of game development, programming, game design, and art (Tyni et al., 2025). These new roles have allowed people without a strong passion toward gaming to work in game studios, gradually shifting the workplace culture toward a more ‘normal’ work environment.
As the funding mechanisms in data-driven game development have become increasingly reliant on outside investors who expect a steadily growing profit curve, studios have started to transition game work into more predictable and steady modes of operation. Games are now developed more as engineered services backed by sales and player data, rather than as creative endeavors based on professional design intuition. This study has revealed that while there might be less evidence of (admittedly emotionally charged) ‘passion work’ in data-driven gamework, the passion work of the yesteryears has been replaced with new forms of emotional work. Our informants were struggling with frustration, self-doubt, fear, and anger related to data-driven development methods. Indeed, we argue that the emotional dimension is very much still present in gamework, only it now becomes visible in various newly emerging and dispersed instances. People doing gamework are passionate, creative individuals, and while they do not necessarily need to be passionate about
Data-driven production models have a clear impact on what is prioritized in game development, and as ‘game data work’ permeates all tasks in game studios, it becomes the ‘new normal’. Prior research has shown that a transition from the older publishing model to the data-driven platform model revolving around live services affects the professional identity of gameworkers (Dubois & Weststar, 2021; Kerr, 2017). As the use of data-driven development tools slowly replaces the role of intuition in the creative industries, data arouse feelings of insecurity, but also positive emotions such as—paradoxically—the feeling of security. These feelings are not exclusive and can overlap, which strongly points to a transitional phase in gameworkers’ professional identities. Future studies should pay more attention to this ongoing change in professional identities in the creative sectors particularly in game development, and how this transition affects the well-being of gameworkers; something that is directly connected to the quality of their work.
Footnotes
Declaration of Conflicting Interests
Funding
Ethical Approval and Informed Consent Statements
ORCID iDs
