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/

Why Business Anthropologists Should Reconsider Machine Learning

high angle photo of robot
Photo by Alex Knight on Pexels.com

This article is a follow-up to my previous article – “Integrating Ethnography and Data Science” – written specifically for anthropologists and other ethnographers.

As an anthropologist and data scientist, I often feel caught in the middle two distinct warring factions. Anthropologists and data scientists inherited a historic debate between quantitative and qualitative methodologies in social research within modern Western societies. At its core, this debate has centered on the difference between objective, prescriptive, top-downtechniques and subjective, sitautional, flexible, descritpive bottom-up approaches.[i] In this ensuing conflict, quantative research has been demarcated into the top-down faction and qualitative research within the bottom-up faction to the detriment of understanding both properly.

In my experience on both “sides,” I have seen a tendency among anthropologists to lump all quantitative social research as proscriptive and top-down and thus miss the important subtleties within data science and other quantitative techniques. Machine learning techniques within the field are a partial shift towards bottom-up, situational and iterative quantitative analysis, and business anthropologists should explore what data scientists do as a chance to redevelop their relationship with quantitative analysis.

Shifts in Machine Learning

Text Box: Data science is in a uniquely formative and adolescent period.

Shifts within machine learning algorithm development give impetus for incorporating quantitative techniques that are local and interpretive. The debate between top-down vs. bottom-up knowledge production does not need – or at least may no longer need– to divide quantitative and qualitative techniques. Machine learning algorithms “leave open the possibility of situated knowledge production, entangled with narrative,” a clear parallel to qualitative ethnographic techniques.[ii]

At the same time, this shift towards iterative and flexible machine learning techniques is not total within data science: aspects of top-down frameworks remain, in terms of personnel, objectives, habits, strategies, and evaluation criteria. But, seeds of bottom-up thinking definitely exist prominently within data science, with the potential to significantly reshape data science and possibly quantitative analysis in general.

As a discipline, data science is in a uniquely formative and adolescent period, developing into its “standard” practices. This leads to significant fluctuations as the data scientist community defines its methodology. The set of standard practices that we now typically call “traditional” or “standard” statistics, generally taught in introductory statistics courses, developed over a several decade period in the late nineteenth and early twentieth century, especially in Britain.[iii] Connected with recent computer technology, data science is in a similarly formative period right now – developing its standard techniques and ways of thinking. This formative period is a strategic time for anthropologists to encourage bottom-up quantative techniques.

Conclusion

Business anthropologists could and should be instrumental in helping to develop and innovatively utilize these situational and iterative machine learning techniques. This is a strategic time for business anthropologists to do the following:

  1. Immerse themselves into data science and encourage and cultivate bottom-up quantative machine learning techniques within data science
  2. Cultivate and incorporate (when applicable) situational and iterative machine learning approaches in its ethnographies

For both, anthropologists should use the strengths of ethnographic and anthropological thinking to help develop bottom-up machine learning that is grounded in flexible to specific local contexts. Each requires business anthropologists to reexplore their relationship with data science and machine learning instead of treating it as part of an opposing “methodological clan.” [iv]


[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12

[ii] Ibid, 15-17.

[iii] Mackenzie, D. (1981). Statistics in Britain 1865–1930: The Social Construction of Scientific Knowledge. Edinburgh: Edinburgh University Press.

[iv] Seaver, N. (2015). Bastard Algebra. In T. Boellstorff, & B. Maurer, Data, Now Bigger and Better (pp. 27-46). Chicago: Prickly Paradigm Press, 39.

Writing Ethnographic Findings as Software Specs

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.

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.

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 1 of 3)

As part of my Season 2, I interviewed Matt Artz, a design anthropologist who has been recently working as a product manager in the tech space. In Part 1, he discussed his experiences making innovative software products as an anthropologist and product manager.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

For more context on my interview series in general, click here.


Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 2 of 3)

This is the second part of three in our conversation. In it, he described his work developing data science-based recommendation systems using the concepts of design anthropology, participatory research, and design thinking, and then how he uses his skills as an anthropologist to visualize and communicate results and then plan what to do going forward with stakeholders.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

Please also see Part 1 of the interview.

For more context on my interview series in general, click here.


Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 3 of 3)

This is the third and final part of three in our conversation. In Part 3, he discussed why he decided to study anthropology for his business work and how that helped give him the skills for the work he does today.

Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.

Previous Parts:

  1. Part 1
  2. Part 2

For more context on my interview series in general, click here.

Resources we mentioned or other additional resources:

  • My website – https://www.mattartz.me/
  • LinkedIn – https://www.linkedin.com/in/matt-artz-anthropology/
  • Anthropology in Business podcast – https://www.mattartz.me/podcasts/anthropology-in-business-with-matt-artz/
  • Anthro to UX podcast – http://anthropologytoux.com/
  • Venn Diagram – https://www.ideou.com/pages/design-thinking
  • Book – https://www.ideo.com/post/design-kit

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 1 of 3)

I spoke with Gemma Galdon-Clavell, founder of Eticas Foundation and Eticas Consulting about the social implications of artificial intelligence technologies. In this first part, we discussed the policy strategies for ensuring that our data and artificial intelligence systems built on our data are good quality, safe, and accountable.

Here are Part 2 and Part 3 of the interview.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 2 of 3)

Here is the second part of three in my conversation with Gemma Clavell. We compared various corporate models – good and bad – for artificial intelligence and how to foster responsible corporate practices in this field.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

Here is Part 1 and Part 3 of our interview.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.

The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 3 of 3)

This is the third and final part of three in our conversation. In Part 3, she described the skills and types of people necessary to build and assess artificial intelligence teams.

Dr. Gemma Galdon-Clavell is a leading voice on technology ethics and algorithmic accountability. She is the founder and CEO of Eticas, where her multidisciplinary background in the social, ethical, and legal impact of data-intensive technology allows her and her team to design and implement practical solutions to data protection, ethics, explainability, and bias challenges in AI. She has conceived and architected the Algorithmic Audit Framework which now serves as the foundation for Eticas’s flagship product, the Algorithmic Audit.

Here is Part 1 and Part 2 of our interview.

To learn more about Gemma’s and Eticas’s work:

For more context on my interview series in general, click here.

Back in Action

Photo credit: https://unsplash.com/photos/6dW3xyQvcYE

Sorry everyone that I have had a delay over the last several months. I had some personal issues come up that had prevented me from being to take the time to work on the blog.

As of now, they seem to be resolved, and I plan to return to blogging. Over the next few weeks, I intend to resume my schedule of posting every other week.

Thank you for your patience during this time.