2025
Tyni, Heikki; Sotamaa, Olli; Myöhänen, Taina
Emotions in Game Data Work Journal Article
In: Games and Culture, 2025, ISSN: 1555-4120 .
Abstract | Links | Tags: Data, Data-driven development, Emotions, Game industry
@article{nokey,
title = {Emotions in Game Data Work},
author = {Heikki Tyni and Olli Sotamaa and Taina Myöhänen},
url = {https://doi.org/10.1177/15554120251349872},
doi = {10.1177/15554120251349872},
issn = {1555-4120 },
year = {2025},
date = {2025-06-16},
journal = {Games and Culture},
abstract = {This study explores the emotional dimensions of data work within the game industry. Through interviews with Finnish game industry professionals, the research reveals that data-driven work practices evoke a wide range of emotions, ranging from pleasure and satisfaction to insecurity and frustration. Data can act both as a helpful workmate and as a source of professional conflict and frustration, impacting the worker's sense of professionalism. Data-driven work also compels workers to individually confront ethical questions. Our findings highlight the interplay between creative work processes and data analytics, emphasizing the emotional labor that game industry professionals need to handle in a data-driven work culture. Additionally, the research addresses ethical considerations and the emotional work required to reconcile public perceptions of data usage. The study contributes to the broader understanding of emotional work in new data-intensive professions and advocates for more emotionally sustainable practices in data-driven game development.},
keywords = {Data, Data-driven development, Emotions, Game industry},
pubstate = {published},
tppubtype = {article}
}
This study explores the emotional dimensions of data work within the game industry. Through interviews with Finnish game industry professionals, the research reveals that data-driven work practices evoke a wide range of emotions, ranging from pleasure and satisfaction to insecurity and frustration. Data can act both as a helpful workmate and as a source of professional conflict and frustration, impacting the worker's sense of professionalism. Data-driven work also compels workers to individually confront ethical questions. Our findings highlight the interplay between creative work processes and data analytics, emphasizing the emotional labor that game industry professionals need to handle in a data-driven work culture. Additionally, the research addresses ethical considerations and the emotional work required to reconcile public perceptions of data usage. The study contributes to the broader understanding of emotional work in new data-intensive professions and advocates for more emotionally sustainable practices in data-driven game development.
Tyni, Heikki; Sotamaa, Olli; Myöhänen, Taina
Understanding Game Data Work Journal Article
In: Big Data and Society, vol. 12, iss. 1, 2025, ISSN: 2053-9517.
Abstract | Links | Tags: Data, Data analytics, Digital gaming, Game industry
@article{nokey,
title = {Understanding Game Data Work},
author = {Heikki Tyni and Olli Sotamaa and Taina Myöhänen},
url = {https://doi.org/10.1177/20539517241309892},
doi = {10.1177/20539517241309892},
issn = {2053-9517},
year = {2025},
date = {2025-01-21},
journal = {Big Data and Society},
volume = {12},
issue = {1},
abstract = {The game industry's content production, maintenance of live games, and processes of acquiring production funding increasingly rely on various kinds of data and its rigorous analysis. These new needs and functions have generated emerging forms of work, such as those of the data analyst, data engineer, and data scientist. Through in-depth interviews with 20 Finnish game industry professionals and an analysis of game industry job advertisements, this paper examines the work and identity of game industry data workers. Drawing from scholarship focused on game production, game work, and data labour, this article argues that organisational practices surrounding data professionals reveal the centrality of high-level data work in game studios focused on live service games and that data work is now performed not just by data analysts, but by the entire staff and management. As a precursor to the wider creative industries, we argue that creative work and data work in game companies are gradually converging, due to the datafied work environment facilitating datafied game work and the work of data professionals increasingly intertwining with creative tasks. Complicating the previous game studio hierarchy is the data analyst's dual role as both a subservient support function and a central broker of data. Adding nuance to this, the article argues that an important aspect of the work of bespoke data professionals in game companies is communication, in contrast to the high-level quantitative tasks often associated with analysis.},
keywords = {Data, Data analytics, Digital gaming, Game industry},
pubstate = {published},
tppubtype = {article}
}
The game industry's content production, maintenance of live games, and processes of acquiring production funding increasingly rely on various kinds of data and its rigorous analysis. These new needs and functions have generated emerging forms of work, such as those of the data analyst, data engineer, and data scientist. Through in-depth interviews with 20 Finnish game industry professionals and an analysis of game industry job advertisements, this paper examines the work and identity of game industry data workers. Drawing from scholarship focused on game production, game work, and data labour, this article argues that organisational practices surrounding data professionals reveal the centrality of high-level data work in game studios focused on live service games and that data work is now performed not just by data analysts, but by the entire staff and management. As a precursor to the wider creative industries, we argue that creative work and data work in game companies are gradually converging, due to the datafied work environment facilitating datafied game work and the work of data professionals increasingly intertwining with creative tasks. Complicating the previous game studio hierarchy is the data analyst's dual role as both a subservient support function and a central broker of data. Adding nuance to this, the article argues that an important aspect of the work of bespoke data professionals in game companies is communication, in contrast to the high-level quantitative tasks often associated with analysis.
2024
Macey, Joseph; Hamari, Juho
Survey Esports Research Methodology Book Chapter
In: Jenny, Seth E.; Besombes, Nicolas; Brock, Tom; Cote, Amanda C.; Scholz, Tobias M. (Ed.): Routledge Handbook of Esports, pp. 135-147, Routledge, 2024, ISBN: 978-1-032-53150-2.
Abstract | Links | Tags: Data, Esports, Survey
@inbook{nokey,
title = {Survey Esports Research Methodology},
author = {Joseph Macey and Juho Hamari},
editor = {Seth E. Jenny and Nicolas Besombes and Tom Brock and Amanda C. Cote and Tobias M. Scholz},
url = {https://doi.org/10.4324/9781003410591-15
https://www.taylorfrancis.com/chapters/edit/10.4324/9781003410591-15/survey-esports-research-methodology-joseph-macey-juho-hamari},
doi = {10.4324/9781003410591-15},
isbn = {978-1-032-53150-2},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-24},
booktitle = {Routledge Handbook of Esports},
pages = {135-147},
publisher = {Routledge},
abstract = {Surveys are used across a wide range of contexts and areas of interest. They are commonly seen as being a cost-effective tool for gathering data, particularly in cases of large or diverse populations who may otherwise be hard to reach. The characterization of esports-engaged individuals as young and digitally aware means that online surveys, in particular, have formed the basis of many esports research studies. This chapter provides an overview of surveys as tools for collecting data, with specific reference to the ways in which they can be employed in esports-related research. Furthermore, the varying implementation options for administering surveys and how each approach can be used to address different research questions is also highlighted, while also offering practical advice on how esports survey research should be planned and conducted. Key highlights include: (1) advantages and disadvantages of using surveys as a research tool, with examples specifically related to the field of esports are discussed, (2) how surveys can be used to address different types of research (i.e., exploratory, descriptive, and causal) is examined, and (3) practical recommendations related to conducting esports survey research are offered, including issues relating to survey design, participant recruitment, and data analysis.},
keywords = {Data, Esports, Survey},
pubstate = {published},
tppubtype = {inbook}
}
Surveys are used across a wide range of contexts and areas of interest. They are commonly seen as being a cost-effective tool for gathering data, particularly in cases of large or diverse populations who may otherwise be hard to reach. The characterization of esports-engaged individuals as young and digitally aware means that online surveys, in particular, have formed the basis of many esports research studies. This chapter provides an overview of surveys as tools for collecting data, with specific reference to the ways in which they can be employed in esports-related research. Furthermore, the varying implementation options for administering surveys and how each approach can be used to address different research questions is also highlighted, while also offering practical advice on how esports survey research should be planned and conducted. Key highlights include: (1) advantages and disadvantages of using surveys as a research tool, with examples specifically related to the field of esports are discussed, (2) how surveys can be used to address different types of research (i.e., exploratory, descriptive, and causal) is examined, and (3) practical recommendations related to conducting esports survey research are offered, including issues relating to survey design, participant recruitment, and data analysis.
2022
Lu, Chien; Peltonen, Jaakko
Gaussian Copula Embeddings Proceedings Article
In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
Abstract | Links | Tags: Data, Gaussian copula embedding model, Machine learning, Vectorial representations
@inproceedings{Lu2022b,
title = {Gaussian Copula Embeddings},
author = {Chien Lu and Jaakko Peltonen},
url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/8ae260afda41b45ed77be58358a6c519-Paper-Conference.pdf},
year = {2022},
date = {2022-11-28},
urldate = {2022-11-28},
booktitle = {36th Conference on Neural Information Processing Systems (NeurIPS 2022)},
abstract = {Learning latent vector representations via embedding models has been shown
promising in machine learning. However, most of the embedding models are still
limited to a single type of observed data. We propose a Gaussian copula embedding
model to learn latent vectorial representations of items in a heterogeneous-data
setting. The proposed model can effectively incorporate different types of observed
data and, at the same time, yield robust embeddings. We demonstrate that the
proposed model can effectively learn in many different scenarios, outperforming
competing models in modeling quality and task performance.},
keywords = {Data, Gaussian copula embedding model, Machine learning, Vectorial representations},
pubstate = {published},
tppubtype = {inproceedings}
}
Learning latent vector representations via embedding models has been shown
promising in machine learning. However, most of the embedding models are still
limited to a single type of observed data. We propose a Gaussian copula embedding
model to learn latent vectorial representations of items in a heterogeneous-data
setting. The proposed model can effectively incorporate different types of observed
data and, at the same time, yield robust embeddings. We demonstrate that the
proposed model can effectively learn in many different scenarios, outperforming
competing models in modeling quality and task performance.
promising in machine learning. However, most of the embedding models are still
limited to a single type of observed data. We propose a Gaussian copula embedding
model to learn latent vectorial representations of items in a heterogeneous-data
setting. The proposed model can effectively incorporate different types of observed
data and, at the same time, yield robust embeddings. We demonstrate that the
proposed model can effectively learn in many different scenarios, outperforming
competing models in modeling quality and task performance.
2021
Karhulahti, Veli-Matti
Want the Games Industry to Share Data? Share Yours Journal Article
In: Nature, vol. 592, iss. 26, pp. 26, 2021, ISSN: 0028-0836.
Links | Tags: Data, Games industry
@article{Karhulahti2021f,
title = {Want the Games Industry to Share Data? Share Yours},
author = {Veli-Matti Karhulahti},
url = {https://www.nature.com/articles/d41586-021-00860-z},
doi = {10.1038/d41586-021-00860-z},
issn = {0028-0836},
year = {2021},
date = {2021-03-30},
journal = {Nature},
volume = {592},
issue = {26},
pages = {26},
keywords = {Data, Games industry},
pubstate = {published},
tppubtype = {article}
}
2019
Suominen, Jaakko
Kuinka lakkasin olemasta huolissani ja opin rakastamaan dataa Journal Article
In: Tieteessä tapahtuu, vol. 37, iss. 5, pp. 46-48, 2019, ISSN: 0781-7916.
Abstract | Links | Tags: Data, Digitalisaatio
@article{Suominen2019c,
title = {Kuinka lakkasin olemasta huolissani ja opin rakastamaan dataa},
author = {Jaakko Suominen},
url = {https://journal.fi/tt/issue/view/5803},
issn = {0781-7916},
year = {2019},
date = {2019-09-19},
journal = {Tieteessä tapahtuu},
volume = {37},
issue = {5},
pages = {46-48},
abstract = {Suomen Akatemian ja opetus- ja kulttuuriministeriön järjestämässä Tiedefoorumissa kesäkuussa käy-tiin keskustelu ”Rakasta dataa”. Siihen osallistuivat Jaakko Suominen, Reijo Sund ja Maria Kopsakan-gas-Savolainen. Tieteessä tapahtuu -lehti pyysi heiltä aiheesta kirjoituksia. Niissä vastataan kysymyk-siin: Mikä on dataa, mistä se on tullut ja miten sitä on käsitelty? Mitä tulevaisuuden trendejä ja rajoi-tuksia on datan käytölle? Miten data mahdollistaa uudenlaiset energiamarkkinat?},
keywords = {Data, Digitalisaatio},
pubstate = {published},
tppubtype = {article}
}
Suomen Akatemian ja opetus- ja kulttuuriministeriön järjestämässä Tiedefoorumissa kesäkuussa käy-tiin keskustelu ”Rakasta dataa”. Siihen osallistuivat Jaakko Suominen, Reijo Sund ja Maria Kopsakan-gas-Savolainen. Tieteessä tapahtuu -lehti pyysi heiltä aiheesta kirjoituksia. Niissä vastataan kysymyk-siin: Mikä on dataa, mistä se on tullut ja miten sitä on käsitelty? Mitä tulevaisuuden trendejä ja rajoi-tuksia on datan käytölle? Miten data mahdollistaa uudenlaiset energiamarkkinat?
Nansen, Bjørn; Nicoll, Benjamin; Apperley, Thomas H.
Postdigitality in Children’s Crossmedia Play: A Case Study of Nintendo’s Amiibo Figurines Book Section
In: Mascheroni, Giovanna; Holloway, Donell (Ed.): The Internet of Toys: Practices, Affordances and the Political Economy of Children’s Smart Play, pp. 89-108, Springer Nature, 2019, ISBN: 978-3030108977.
Abstract | Links | Tags: Amiibo, Data, Nintendo, Play, Postdigital
@incollection{Nansen2019,
title = {Postdigitality in Children’s Crossmedia Play: A Case Study of Nintendo’s Amiibo Figurines},
author = {Bjørn Nansen and Benjamin Nicoll and Thomas H. Apperley},
editor = {Giovanna Mascheroni and Donell Holloway},
doi = {10.1007/978-3-030-10898-4_5},
isbn = {978-3030108977},
year = {2019},
date = {2019-02-21},
urldate = {2019-02-21},
booktitle = {The Internet of Toys: Practices, Affordances and the Political Economy of Children’s Smart Play},
pages = {89-108},
publisher = {Springer Nature},
abstract = {In this chapter, we use Nintendo Amiibo to interrogate the evolving intersection of crossmedia products and children’s cultures of play. Amiibo figurines are based on characters from various Nintendo franchises, such as Super Mario Bros., and use NFC tags to connect wirelessly to Nintendo’s Switch, 3DS and Wii U platforms. In their production, promotion and everyday use, the figurines solicit playful practices that cut across physical objects and digital spaces. Drawing on interface analysis, promotional discourses and videos of play on YouTube, this analysis highlights how Amiibos are framed as a means to envelop children in Nintendo’s crossmedia ecosystem by reinforcing a physical connection between child, toy, software, platform and intellectual property. Informed by the concept of postdigital play, we account for this reciprocal dynamic between children’s everyday play and the branded world of IoT products, and we point to emerging questions around children’s data literacies.},
keywords = {Amiibo, Data, Nintendo, Play, Postdigital},
pubstate = {published},
tppubtype = {incollection}
}
In this chapter, we use Nintendo Amiibo to interrogate the evolving intersection of crossmedia products and children’s cultures of play. Amiibo figurines are based on characters from various Nintendo franchises, such as Super Mario Bros., and use NFC tags to connect wirelessly to Nintendo’s Switch, 3DS and Wii U platforms. In their production, promotion and everyday use, the figurines solicit playful practices that cut across physical objects and digital spaces. Drawing on interface analysis, promotional discourses and videos of play on YouTube, this analysis highlights how Amiibos are framed as a means to envelop children in Nintendo’s crossmedia ecosystem by reinforcing a physical connection between child, toy, software, platform and intellectual property. Informed by the concept of postdigital play, we account for this reciprocal dynamic between children’s everyday play and the branded world of IoT products, and we point to emerging questions around children’s data literacies.