Tag: Applied Ethnography

Rethinking Ethnography in Anthropology

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.

Photo credit #1: StockSnap at https://pixabay.com/photos/people-girls-women-students-2557396/

Photo credit #2: hosny_salah at https://pixabay.com/photos/woman-hijab-worker-factory-worker-5893942/

Photo credit #3: Jack Douglass at https://unsplash.com/photos/ouZAz-3vh7I

What Is the Difference between Anthropology and Ethnography?

(Feel free to check out my follow-up article to this one about rethinking the role of ethnography in anthropology as well.)

A friend recently asked me, “What’s the difference between anthropology and ethnography?” When I tell them I am an anthropologist, people have asked me this question – phrased in slightly different ways – enough times that I am writing this article to answer it for anyone who might be wondering what the difference is.

To situate his question, he explained how other anthropologists he had worked with would often contrast anthropological work with mere ethnography, but that he never understood the difference. That has generally been the experience of people I have talked to who have asked me this question: they have recently encountered anthropologists contrasting their work with other ethnographers, something which left them puzzled given how connected anthropology and ethnography has been in their experience.

Ethnography is anthropology’s “methodological baby,” and in my experience, the anthropology vs ethnography conversation is typically a way for anthropologists to process others’ increasing utilization of ethnography.  Thus, to those looking in from the outside like my friend, this discussion within anthropology about the differences can seem perplexing.

The Short-Answer

book page

The short answer is that anthropology is a discipline while ethnography is a methodology. Anthropology refers to the study of human cultures and humanity in general. Ethnography is a methodological approach to learning about a culture, setting, group, or other context by observing it yourself and/or piecing together the experiences of those there (this article provides an in-depth definition of ethnography).

The field of anthropology has many subdisciplines, ranging from archaeology to linguistics, but in this article, I will focus my discussion on cultural anthropology (the subdiscipline I am a part of). Of all its subdisciplines, cultural anthropology most directly relates to ethnography.

Cultural anthropologists seek to understand contemporary living cultures and societies. They have been instrumental in developing and employing ethnography to understand cultures and other social phenomena. Ethnography has become the most common (but not only) way cultural anthropologists have sought to conduct research.

Thus, the relationship between cultural anthropology and ethnography is that between a discipline and its primary tool that has defined what it means to practice that discipline, like proofs define the field of mathematics or experimentation for the hard sciences.

This sentence sums it up:

In general, cultural anthropologists use ethnography to understand cultures.

It illustrates cultural anthropology’s who, what, and how as a discipline and how each of these key components relates to others. 

There are exceptions to this. Cultural anthropologists do not only use ethnography nor does the word culture describe everything they analyze, but this describes the general relationship between cultural anthropology and ethnography.

This is the short explanation of the difference between anthropology and ethnography. Like textbook explanations, it is accurate but abstract and simplistic. It does not get to the heart of what an anthropologist might be really getting at on when they juxtapose the two. In my experience, when people compare the two, they are reflecting on what they consider anthropological ways of thinking and ethnographic ways of thinking. Hence, here is my long answer, which gets to the bottom of what people are really trying to say.

The Long Answer

There are two angles to consider for the long answer: obstinacy towards others outside anthropology using ethnography and the potential for anthropologists to move beyond traditional ethnography. The former is something we anthropologists must overcome and the latter a set of interesting and innovative prospects for both anthropology and ethnography.

Cultural anthropologists have had a unique relationship with ethnography. The discipline has been instrumental in designing, employing, and promoting the methodology, and with the help of anthropologists, the approach has become a valuable way to understand humans, cultures, and societies. At the same time, ethnography has become increasingly popular in other fields, both academic fields like sociology and political science, and in professional fields like UX research and design, marketing, and organizational management. I think this increasing use of the anthropological tool of ethnography has been marvelous, but multiple disciplines suddenly doing “our thing” has catalyzed identity conflict among some anthropologists.

In my experience, when anthropologists make a sharp distinction between anthropology and ethnography, they are primarily processing this identity conflict. For example, in the ensuring conversation with the person I mentioned in the introduction, I learned that he had recently heard some anthropologists condemn several ethnographies in the field of design where he works as “non-anthropological,” making him wonder what on earth the difference was between being “ethnographic” and “anthropological.” Hence, when I told him I was an anthropologist, he figured he would ask me.

Even if it is at best a historical oversimplification, here is a common narrative I will hear within anthropology: several decades ago, ethnography was the primary domain of anthropologists, but now it seems to be taking on a life of its own, with many others from other fields using it. Others deploying ethnography can have fantastic or horrifying results – and everything in between, but often the implicit and/or explicit assumption in the narrative is that people from other disciplines would generally fail to be able to do as good of a job as a trained anthropologist.

Discussions within anthropology of the similarities and differences between anthropology and ethnography – or between so-called anthropological ways of thinking vs ethnographic ways of thinking, anthropological approaches vs ethnographic approaches, or anthropologists vs ethnographers – have become a major staging ground for processing this seeming recent increase in the popularity of ethnography outside of anthropology.

A few notable perspectives have emerged from these discussions. Some cultural anthropologists promote other methodologies within the discipline either in addition to or instead of ethnographic inquiries (e.g. Arturo Escobar). Others emphasize what anthropologists specifically bring to ethnographic research that others who conduct ethnographic research supposedly cannot (e.g. Tim Ingold). Among the anthropologists I have talked to at least in both the academic and professional settings, I have found the latter to be the most common response: arguing that training in anthropology brings a superior way of thinking about society, cultures, and various social phenomena, which allows trained anthropologists to conduct ethnography better.

Exploring how ethnography might be changing as a wider variety of people use it and anthropologists reflecting on how their discipline has shaped ethnography and ethnography shaped their discipline are commendable. But, this particular way of trying to do both seems like a defensive, “us vs them” response.

In addition to fact that humans seem to very frequently tell themselves “us vs them” narratives, material resources are also at play here. By portraying anthropologists as the only people able to perform “authentic” or “quality” ethnographies, anthropologists can demand competitive resources from potential funders, clients, colleagues, organizations and/or students. This could range from funding for their academic department to being the ones who win the job or contract to conduct qualitative user research at a company.

Whatever factors reinforce this type of defensive response, I believe we anthropologists should instead celebrate the increasing flowering of ethnography and embrace how others might reformulate the methodology to meet their needs. It is an opportunity to crosspollinate and enliven what it means to do ethnography.

A final response by cultural anthropologists has been to rethink traditional ethnography and/or anthropological research itself. For example, Morten Axel Pedersen has argued for a reimagining of what ethnography is in a way that could incorporate data science and machine learning techniques into the ethnographic toolkit and anthropological research (something I have argued for here, here, and here as well). I believe this reassessment of traditional ethnography has a lot of potential for innovative, outside-the-box anthropological research.

Unfortunately, the former chest-pumping explanations of why non-anthropological ethnographies are inferior to our work has been more common than (what I, at least, would consider) this more fruitful conversation. Its bombastic thunder can drawn out the other perspectives.

Conclusion

I can certainly see how non-anthropologists seeking to understand (and maybe employ) ethnography could become confused when they encounter these debates among anthropologists.

To anyone who has been so confused, I hope this article provides – what I see as at least – the wider context for why anthropologists often juxtapose their discipline with ethnography. As anthropologists process how ethnography is increasingly flowering outside of their discipline, I also hope the negative aspects of our response will not turn you away from what is a powerful methodology to understand people, cultures, and societies.

Photo credit #1: Raquel Martínez at https://unsplash.com/photos/SQM0sS0htzw

Photo credit #2: Skitterphoto at https://www.pexels.com/photo/book-page-1005324/

Photo credit #3: klimkin at https://pixabay.com/photos/hand-gift-bouquet-congratulation-1549399/

Photo credit #4: PublicDomainPictures at https://pixabay.com/photos/garden-flowers-butterfly-monarch-17057/

Writing Ethnographic Findings as Software Specs

Photo Credit: Cuncon

When working as an ethnographer with software engineers, I have found formatting my write-up for any ethnographic inquiry I conducted as software specs incredibly valuable. In general, I prefer to incarnate any ethnographic report I make into the cultural context I am conducting the research for, and this is one example of how to do that.

Many find the academic essay prose style stifling and unintelligible, so why limit yourself to that format like most ethnographic write-ups tend to be when conducting work for and with other parties? Like Schaun Wheeler said in this interview, in the professional world, pdf reports are often where thoughts go to die.

Photo Credit: Mimi Thian

Most often when I am conducting ethnographic research with software engineers, I am doing some kind of user research on a potential or actual software product: trying to understand how users engage with a software or set of softwares to help engineers improve the design to better meet users’ needs. When doing this, I most often bullet my findings by topic and suggested change, ordering them based on importance and complexity. This allows software engineers to easily transfer the insights into actionable ideas for how to improve the software design.

For example, a software company asked me to conduct ethnography to understand how users engaged with a beta version of an app. For this project, I broke down ethnographic insights into advantages of the app and common pitfalls encountered. I illustrated each item on the list with stories and quotes from users. I ordered the points based on importance and difficulty addressing (aka as either important and easy to fix, not important but easy to fix, important but not easy to fix, and not important but not easy to fix). On each list, I focused on the item itself, but sometimes I might also mention potential solutions, particularly when users proposed specific ideas for how to resolve something they encountered. Only occasionally did I give my own suggestions. This allowed software engineers to think through the ethnographic findings and translate them into software specs. They liked the report formatting so much the CTO of the company came to me personally to tell me I had the most profound and useful documentation he had seen. 

I have found describing ethnographic findings as design specs has been incredibly helpful in the tech world. It allows the immersion of ethnographic insights into engineering contexts and facilitates the development of actional insights and designs. Instead of defaulting to a long essay or manuscript, ethnographers should think carefully about the best way to format their findings to make sure it is approachable, relatable, and useful for the audience(s) that will look at and use it.

Maker Anthropologist in the Tech Field: Interview with Astrid Countee (Interview #1 in the Interview Series)

For my first interview in the Interview Series, I interviewed Astrid Countee. She is a business anthropologist and technologist with a background in anthropology, software engineering, and data science. She currently works as a user researcher at the peer-to-peer distributed company Holo, as a research associate at The Plenary, as an arts and education nonprofit, and as a co-founder of Missing Link Studios which distributes the This Anthro Life podcast. 

If the audio does not play on your computer, you can download it here:

Astrid Interview AudioDownload

Over our conversation, we discussed the following:

  1. Astrid’s work as a technologist and anthropology
  2. Strategies for how to develop programming and data skills as an anthropologist
  3. Astrid’s experiences developing and using statistical and data science tools in her work
  4. The importance of maker anthropology

Our conversation touched on a variety of exciting topics, which I hope to follow-up on in more detail in the coming months. I hope you enjoy.

To learn more about Astrid and her work, see her LinkedIn page: https://www.linkedin.com/in/astridcountee

Here are the various items that she mentioned during the conversation:

Thanks, Astrid, for being willing to share your insights.

Next Interview in the Interview Series: https://ethno-data.com/schaun-interview-1/

Data Scientist, Anthropologist, and Entrepreneur: Interview with Schaun Wheeler (Interview #2 in the Interview Series)

For my second interview in the Interview Series, I interviewed Schaun Wheeler. Schaun is co-founder of Aampe, a startup that embeds an active learning system into mobile apps to turn push notifications into part of the app’s user interface. Before he co-founded Aampe, Schaun was the data science lead for the award-winning Consumer Graph intelligence product at Valassis, a U.S. ad-tech firm. And before that he founded and directed the data science team at Success Academy Charter Schools in New York City. Then before that, Schaun was one of the first people to champion the use of statistical inference to understand massive unstructured data at the United States Department of the Army. Schaun has a Ph.D. in Cultural Anthropology from the University of Connecticut.

If the audio does not play on your computer, you can download it here:

Schaun-Interview-AudioDownload

Over our conversation, we discussed the following:

  • Schaun’s experiences as both a data scientist and anthropologist
  • His utilization of anthropology within data science to decipher the right problem before launching into data science solutions
  • Recommendations for how anthropologists can develop data science and programming skills
  • His experiences starting a new data science consumer and market-research based company

To learn more about Schaun Wheeler and Aampe, check these out:

LinkedIn (the best way to contact him): https://www.linkedin.com/in/schaunwheeler/

Medium: https://medium.com/@schaun.wheeler

Twitter: https://twitter.com/schaunw

Aampe website: https://www.aampe.com/

Aampe blog: https://www.aampe.com/blog

A User Story, The Data Science Children’s Book: https://www.aampe.com/blog/a-user-story

More Detailed Walkthrough: Clip #1: https://www.youtube.com/playlist?list=PL03WDMCL2PHjRd8Y8USzvVkcIyQM57FMU and Clip #2: https://youtu.be/kwk_Ot8orPY

Previous Interview in the Interview Series: https://ethno-data.com/astrid-interview-1/

Anthropologist in Fintech: Interview with Priyanka Dass Saharia (Interview #3 in the Interview Series)

For my third interview in the Interview Series, I interviewed Priyanka Dass Saharia. Priyanka Dass Saharia is an anthropologist working in tech startups, mostly seed to early venture stage in their product development. These companies broadly fall under the rubric of tech-based initiatives that aim to accelerate positive social, environmental and governance change. To do this, she routinely employs a variety of qualitative and quantitative techniques. In addition to anthropology, she has also studied economics and sociology in both India, where she grew up, and the United Kingdom, where she currently resides.

Over our conversation, we discussed the following:

  • Priyanka’s experiences as an anthropologist in tech, including her current work in fintech
  • Strategies for socially and environmentally equitable entrepreneurial investment
  • Skills anthropologists might need when working in tech and suggestions for how to develop them
  • Recommendations for how anthropologists can cultivate their quantitative thinking

To learn more about Priyanka Dass Saharia, check these out:

Anti-Corruption Anthropologist in Kazakhstan: Interview with Olga Shiyan (Interview #7 in the Interview Series)

I interviewed Olga Shiyan as part of my Interview Series. In it, she discusses her anti-corruption work in Kazakhstan with Transparency International. In particular, she highlights various projects that have integrated anthropology with data science and statistics. 

Olga Shiyan is the Executive Director of the Transparency International’s chapter in Kazakhstan. She specializes in advocacy, legislation and draft laws, and democratic training programs. For this, she has developed research methods that combine anthropology and data science and statistics. In 2019, the Kazakhstan Geographic Society awarder for a medal for anti-corruption work.

To learn more about Olga, feel free to check out the following:

1. Monitoring the state of corruption in Kazakhstan for 2020, presentation

2. Presentation of the research in the media, speaking on a TV show, talk TV show

3. Monitoring the state of corruption in Kazakhstan for 2019

4. The index of civic participation and influence on lawmaking in Kazakhstan

5. 13 stories about lawmaking in Kazakhstan                                             

6. Development of local self-government in Kazakhstan: analysis of fourth-level budgets

7. Ethno-confessional monitoring. Kazakhstan. 2018.

8. Customs corruption in Kazakhstan: mirror analysis of trade

9. Opportunities for Civil Control in Kazakhstan: Experience of Ethnological Research

10. The thorny path of labor migrants to Russia: the experience of participant observation

11. Anthropological approach to the study of the influence of gift exchange on informal socio-economic relations

12. Anthropological approach in the interdisciplinary study of the phenomenon of corruption

13. Summer anti-corruption school of Transparency Kazakhstan

14. Transparency Kazakhstan School of Investigative Journalism

EPIC Data Scientists + Ethnographers Group

I recently organized a professional group called EPIC Data Scientists + Ethnographers along with a few others who are both data scientists and ethnographers. Our goal is to form a virtual community to discuss ways to incorporate ethnography and data science, just like I strive to do on this website.

If you are interested in working with others on this or simply interested in learning more, feel free to join. Whether you are both a data scientist and ethnographer, only one of them, or neither, we would love to hear your perspective.

Thank you, EPIC, for helping to develop this and giving us a platform.

Photo credit: deepak pal at https://www.flickr.com/photos/158301585@N08/46085930481/

Breaking into Tech: A Career Workshop

code projected over woman
Photo by ThisIsEngineering on Pexels.com

Earlier this week, Matt Artz, Astrid Countee, and I ran a workshop at the American Anthropological Association’s 2020 annual conference entitled “Breaking into Tech.” We discussed strategies for anthropologists interested in working in the tech world.

Here is the presentation for anyone who might find it useful but could not attend:

Download [6.86 MB]

Thank you, Astrid and Matt, for your help in developing and running this workshop.  

Four Innovative Projects that Integrated Data Science and Ethnography

In a previous article, I have discussed the value of integrating data science and ethnography. On LinkedIn, people commented that they were interested and wanted to hear more detail on potential ways to do this. I replied, “I have found explaining how to conduct studies that integrate the two practically is easier to demonstrate through example than abstractly since the details of how to do it vary based on the specific needs of each project.”

In this article, I intend to do exactly that: analyze four innovative projects that in some way integrated data science and ethnography. I hope these will spur your creative juices to help think through how to creatively combine them for whatever project you are working on.

Synopsis:

Project:How It Integrated Data Science and Ethnography:Link to Learn More:
No Show ModelUsed ethnography to design machine learning softwarehttps://ethno-data.com/show-rate-predictor/
Cybersensitivity StudyUsed machine learning to scale up the scope of an ethnographic inquiry to a larger populationhttps://ethno-data.com/masters-practicum-summary/
Facebook Newsfeed Folk TheoriesUsed ethnography to understand how users make sense of and behave towards a machine learning system they encounter and how this, in turn, shapes the development of the machine learning algorithm(s)https://dl.acm.org/doi/10.1145/2858036.2858494
Thing EthnographyUsed machine learning to incorporate objects’ interactions into ethnographic researchhttps://dl.acm.org/doi/10.1145/2901790.2901905 and https://www.semanticscholar.org/paper/Things-Making-Things%3A-An-Ethnography-of-the-Giaccardi-Speed/2db5feac9cc743767fd23aeded3aa555ec8683a4?p2df

Project 1: No Show Model

A medical clinic at a hospital system in New York City asked me to use machine learning to build a show rate predictor in order to inform an improve its scheduling practices. During the initial construction phase, I used ethnography to both understand in more depth understand the scheduling problem the clinic faced and determine an appropriate interface design.

Through an ethnographic inquiry, I discovered the most important question(s) schedulers ask when scheduling their appointments. This was, “Of the people scheduled for a given doctor on a particular day, how many of them are likely to actually show up?” I then built a machine learning model to answer this exact question. My ethnographic inquiry provided me the design requirements for the data science project.  

In addition, I used my ethnographic inquiries to design the interface. I observed how schedulers interacted with their current scheduling software, which gave me a sense for what kind of visualizations would work or not work for my app.

This project exemplifies how ethnography can be helpful both in the development stage of a machine learning project to determine machine learning algorithm(s) needs and on the frontend when communicating the algorithm(s) to and assessing its successfulness with its users.

As both an ethnographer and a data scientist, I was able to translate my ethnographic insights seamlessly into machine learning modeling and API specifications and also conducted follow-up ethnographic inquiries to ensure that what I was building would meet their needs.

Project 2: Cybersensitivity Study

I conducted this project with Indicia Consulting. Its goal was to explore potential connections between individuals’ energy consumption and their relationship with new technology. This is an example of using ethnography to explore and determine potential social and cultural patterns in-depth with a few people and then using data science to analyze those patterns across a large population.

We started the project by observing and interviewing about thirty participants, but as the study progressed, we needed to develop a scalable method to analyze the patterns across whole communities, counties, and even states.

Ethnography is a great tool for exploring a phenomenon in-depth and for developing initial patterns, but it is resource-intensive and thus difficult to conduct on a large group of people. It is not practical for saying analyzing thousands of people. Data science, on the other hand, can easily test the validity across an entire population of patterns noticed in smaller ethnographic studies, yet because it often lacks the granularity of ethnography, would often miss intricate patterns.

Ethnography is also great on the back end for determining whether the implemented machine learning models and their resulting insights make sense on the ground. This forms a type of iterative feedback loop, where data science scales up ethnographic insights and ethnography contextualizes data science models.

Thus, ethnography and data science cover each other’s weaknesses well, forming a great methodological duo for projects centered around trying to understand customers, users, colleagues, or other users in-depth.

Project 3: Facebook Newsfeed Folk Theories

In their study, Motahhare Eslami and her team of researchers conducted an ethnographic inquiry into how various Facebook users conceived of how the Facebook Newsfeed selects which posts/stories rise to the top of their feeds. They analyze several different “folk theories” or working theories by everyday people for the criteria this machine learning system uses to select top stories.

How users think the overall system works influences how they respond to the newsfeed. Users who believe, for example, that the algorithm will prioritize the posts of friends for whom they have liked in the past will often intentionally like the posts of their closest friends and family so that they can see more of their posts.

Users’ perspectives on how the Newsfeed algorithm works influences how they respond to it, which, in turn, affects the very data the algorithm learns from and thus how the algorithm develops. This creates a cyclic feedback loop that influences the development of the machine learning algorithmic systems over time.

Their research exemplifies the importance of understanding how people think about, respond to, and more broadly relate with machine learning-based software systems. Ethnographies into people’s interactions with such systems is a crucial way to develop this understanding.

In a way, many machine learning algorithms are very social in nature: they – or at least the overall software system in which they exist – often succeed or fail based on how humans interact with them. In such cases, no matter how technically robust a machine learning algorithm is, if potential users cannot positively and productively relate to it, then it will fail.

Ethnographies into the “social life” of machine learning software systems (by which I mean how they become a part of – or in some cases fail to become a part of – individuals’ lives) helps understand how the algorithm is developing or learning and determine whether they are successful in what we intended them to do. Such ethnographies require not only in-depth expertise in ethnographic methodology but also an in-depth understanding how machine learning algorithms work to in turn understand how social behavior might be influencing their internal development.

Project 4: Thing Ethnography

Elise Giaccardi and her research team have been pioneering the utilization of data science and machine learning to understand and incorporate the perspective of things into ethnographies. With the development of the internet of things (IOT), she suggests that the data from object sensors could provide fresh insights in ethnographies of how humans relate to their environment by helping to describe how these objects relate to each other. She calls this thing ethnography.

This experimental approach exemplifies one way to use machine learning algorithms within ethnographies as social processes/interactions in of themselves. This could be an innovative way to analyze the social role of these IOT objects in daily life within ethnographic studies. If Eslami’s work exemplifies a way to graft ethnographic analysis into the design cycle of machine learning algorithms, Giaccardi’s research illustrates one way to incorporate data science and machine learning analysis into ethnographies.

Conclusion

Here are four examples of innovative projects that involve integrating data science and ethnography to meet their respective goals. I do not intend these to be the complete or exhaustive account of how to integrate these methodologies but as food for thought to spur further creative thinking into how to connect them.

For those who, when they hear the idea of integrating data science and ethnography, ask the reasonable question, “Interesting but what would that look like practically?”, here are four examples of how it could look. Hopefully, they are helpful in developing your own ideas for how to combine them in whatever project you are working on, even if its details are completely different.

Photo credit #1: StartupStockPhotos at https://pixabay.com/photos/startup-meeting-brainstorming-594090/

Photo credit #2: DarkoStojanovicat at https://pixabay.com/photos/medical-appointment-doctor-563427/  

Photo credit #3: NASA at https://unsplash.com/photos/Q1p7bh3SHj8  

Photo credit #4: Kon Karampelas at https://unsplash.com/photos/HUBofEFQ6CA

Photo credit #5: Pixabay at https://www.pexels.com/photo/app-business-connection-device-221185/