Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior. For the next installment of my Interview Series, we discuss ways to use game design and UX design to develop usable and useful machine learning products and their experiences transitioning from design into data science. In this first part, we discuss the connections between data science and game design.
Here is the third part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses how the interconnections he has found between data science and garbology.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
Here is the second part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes garbology is and what kind of work he does as a data scientist garbologist.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
I interviewed Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses his mission to combine garbology, anthropology, and data science to better understand humanity and the trash we leave behind. In this first part, he describes the connections he has found between these various fields.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
Here is the second part of three in my conversation with Tanner Greene. He discusses his strategies for transitioning from graduate school to UX research and his recommendations for any fellow student seeking to do the same.
Tanner Greene is a UX Researcher and Ph.D. Candidate at the University of Virginia, where he’s finishing a dissertation on the history of vaporwave, a music genre created on social media platforms. Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.
For my next installment in my Interview Series, I interviewed Tanner Greene. He recently received his doctorate from the University of Virginia for his research on the digital music genre, vapor wave. He primarily used qualitative means but has also taught himself Python to be able to employ quantitative textual analysis into his project. It is a good example of how to integrate qualitative digital ethnographic techniques with quantitative natural language processing.
In this first part, he discusses why he decided to study the vapor wave community and his experiences learning Python to conduct statistical analysis with.
Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.
Hello, my name is Stephen Paff. I am a data scientist and an ethnographer. The goal of this blog is to explore the integration of data science and ethnography as an exciting and innovative way to understand people, whether consumers, users, fellow employees, or anyone else.
I want to think publicly. Ideas worth having develop in
conversation, and through this blog, I hope to present my integrative vision so
that others can potentially use it to develop their own visions and in turn
help shape mine.
Please Note: Because my blog straddles two technical areas, I will split my posts based on how in-depth they go into each technical expertise. Many posts I will write for a general audience. I will write some posts, though, for data scientists discussing technical matters within that field, and other posts will focus on technical topics withn ethnography for anthropologists and other ethnographers. At the top of each post, I will provide the following disclaimers:
As a data scientist and ethnographer, I have worked on many types of research projects. In professional and business settings, I am excited by the enormous growth in both data science and ethnography but have been frustrated by how, despite recent developments that make them more similar, their respective teams seem to be growing apart and competitively against each other.
Within academia, quantitative and qualitative research methods have developed historically as distinct and competing approaches as if one has to choose which direction to take when doing research: departments or individual researchers specialize in one or the other and fight over scarce research funding. One major justification for this division has been the perception that quantitative approaches tend to be prescriptive and top-down compared with qualitative approaches which tend to be to descriptive and bottom-up. That many professional research contexts have inherited this division is unfortunate.
Recent developments in data science draw parallels with qualitative research and if anything, could be a starting point for collaborative intermingling. What has developed as “traditional” statistics taught in introductory statistics courses is generally top-down, assuming that data follows a prescribed, ideal model and asking regimented questions based on that ideal model. Within the development of machine learning been a shift towards models uniquely tailored to the data and context in question, developed and refined iteratively.[i] These trends may show signs of breaking down the top-down nature of traditional statistics work.
If there was ever a time to integrate quantitative data science and qualitative ethnographic research, it is now. In the increasingly important “data economy,” understanding users/consumers is vital to developing strategic business practices. In the business world, both socially-oriented data scientists and ethnographers are experts in understanding users/consumers, but separating them into competing groups only prevents true synthesis of their insights. Integrating the two should not just include combining the respective research teams and their projects but also encouraging researchers to develop expertise in both instead of simply specializing in one or the other. New creative energy could burst forth when we no longer treat these as distinct methodologies or specialties.
[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12.
Many people who seek to integrate ethnography and data science have told me that they feel alienated or sidelined: like they are “lone wolves” having to figure out how to combine them without anyone or any resources to help them. Those who are interested in learning how to connect the two, in particular, tell me that they feel stuck without sufficient resources or support to explore connections between them. I do not think either anthropology and qualitative research or data science (two fields I am a part of) do a very good job understanding the other.
Thus, I started this interview series to highlight the experiences of those who in some way integrate ethnography with data science. My intent is to catalog their stories and ideas to help those considering doing similar work. I will interview people from a variety of backgrounds and industries (including anthropologists, data scientists, artists, software engineers, and so on) discussing what is most significant for them in their field. In general, though, my interviews will focus on the following questions or themes:
1. What is it like integrating data science and ethnography (or whatever other related activities or methodologies that particular person uses)?
2. What skills and abilities have you found helpful for such work, and what recommendations do you have for cultivating those skills?
3. How would you recommend education institutions and employers teach and/or foster these skills?
4. How has your joint background influenced how you view and approach the world, data science, ethnography, and/or the disciplines or fields you are a part of?
I started conducting these interviews in the fall of 2021. After a brief break, I started what I am calling my “Season 2” of interviews in the fall of 2022 with an ever evolving list of professionals. I am thankful to talk with everyone I have, and I hope you enjoy them as well.
If you would like to be interviewed or know someone else doing this type of work who you would recommend I interview, please reach out to me on my Contact Me page. Feel free to also reach out if you have any particular burning questions that you would like to see me ask.
This is a follow-up on my previous article about the difference between anthropology and ethnography. In this article, I discuss recent trends within anthropology to either revitalize ethnography and/or rethink its status as the primary research methodology within the discipline.
We anthropologists should consider expanding beyond the ethnographic toolkit. That could involve redefining what it means to conduct ethnography in such a way that includes other types of practices outside of the traditional ethnographic toolkit and/or rethinking the role of ethnography as our primary methodology.
For context, ethnography has been the primary tool within the discipline for the last several decades. I would define ethnography as a methodological approach that seeks to holistically understand and express the lived experiences of those in a particular sociocultural context(s) (see this article and this paper). Ethnography conventionally entails a specific set of qualitative methodologies that help to understand and analyze these lived experiences, including participant observation, interviews, qualitative coding, and so on. Anthropologists and other ethnographers have built this set of practices because they are excellent at capturing people’s lived experiences, and I agree that they are powerful for that.
I do not, however, believe that these are the only potential ways to do that. For me, ethnography is an orientation, an approach that seeks to make sense of the social world by focusing on the lived experiences of others, not necessarily some collection of qualitative methods. Seeing ethnography as an orientation, for example, would enable ethnographers to use data science and machine learning tools within ethnographies (see this and this).
My perspective here exemplifies the first way some anthropologists have sought to expand beyond the traditional ethnographic toolkit: by redefining ethnography. For us, viewing ethnography as a specific set of qualitative research techniques pigeonholes what ethnography can be. Although these techniques are powerful and useful, their exclusive deployment within anthropology stifles what ethnography can become.
Other anthropologists will seek to expand beyond this toolkit by advocating for non-ethnographic anthropological research. For them, anthropologists should cultivate other research practices in addition to or sometimes instead of ethnography. I am passionate about applying this specifically to data science and machine learning, and Morten Axel Pedersen is a counterpart to me who in this specific area. He thinks anthropologists should move beyond ethnographic research, which could include incorporating data science and machine learning research (see his talk as an example). Similar to me, he wants to see more utilization of data science and machine learning within anthropology, but he presents this as an alternative to doing ethnographic research not as a potential part of ethnographic research like I do.
The difference between the two approaches is subtle: the first advocates for reimaging ethnography and the second for reimagining anthropology and anthropological research while potentially keeping ethnography the same. On a practical level, though, they are not that different. Not only are they not mutually exclusive: one can seek to redefine ethnography and ethnography’s hold within anthropology. But they each also have their place in seeking when encouraging the expansion of the anthropological toolkit. In some situations, the promotion of redefining ethnography beyond its traditional qualitative practices is most beneficial, and other times, advocating for non-ethnographic forms of research would be.
