By Astrid Countee

When people think of quantitative analytics, big data, and statistics, they rarely picture an anthropologist. The truth is that although our discipline is well known for gathering qualitative data, anthropologists are trained to understand all kinds of data. In many graduate anthropology programs, the required research methods course includes lots of statistics and methods for making sense of quantitative data.

While anthropology may be more social science than science, most of us learn how to implement the scientific method, gather both qualitative and quantitative data, and perform mixed-methods analysis. Even if your anthropological study falls more on the qualitative side, it is inevitable that your dataset is still a mix of both qualitative and quantitative data. Yet many of those calling ourselves practicing anthropologists tend to associate our skills as best fit for qualitative research only, forgetting that we are also trained in quantitative data analysis. Just as we continue to improve upon on qualitative research methods, there is a need for more expertise in our quantitative research methods. In a world where corporations and governments are looking to make data driven decisions, who better than an anthropologist to put this data in context?

Anthropology and Data Analysis

I learned about the need for data analysis in context partly by accident. I pursued my anthropology graduate degree while working full time. It was 2006 when I started my job as a data coordinator for international subcontractors (I got as close to cultural relevance as I could). The economy wasn’t that great, so I thought it prudent to hold on to my job when I started my graduate studies. As a result, I spent my days working on building databases, analyzing large amount of quantitative data and my nights learning about statistics, research methods, and ethnographic field studies.

As I met new people, I would always be asked what I was going to school for. The assumption was that I was pursuing something IT related or maybe an MBA. Anthropology seemed to be inconsistent with the work that as I was doing as a data coordinator, and later as a data analyst. This simply was not true. I found that I was actively bringing my anthropological studies into my job and applying my data analysis skills to my graduate work. The two supported and complemented one another.

Anthropology itself is unique in that it is such a broad discipline. We study everything from folklore to genetics. Our work involves a lot of cross-disciplinary study and application. But what unites us as anthropologists are several key characteristics. Our work is holistic, placing whatever phenomenon we are studying in context. Our discipline is fundamentally comparative. Our theories are presented as a sort of tool kit for analysis, based on empirically collected data from the field (Strange 2009, 1-3).

We are working in a time when the world is being transformed by data. New disciplines are emerging, like data science, in order to adequately make use of all the data that is being generated from our computers, mobile devices, and increasing networked societies. The goal of the data scientist is to use mathematical models to analyze data, create narratives or visualizations to explain it, and then suggest how to use the information to make decisions. Companies are finding that just having the hard data points isn’t enough to take action. They need context, an understanding of what the data implies, and a plan for how to strategically use those implications to move forward. Sounds suspiciously similar to anthropological training, doesn’t it?

Computational Anthropology

I am not the only one making the correlation between data science and anthropology. Subfields are popping up that help bring together data analysis, computation, and anthropology. The most visible development is computational anthropology, which involves the process of using cultural anthropology techniques to create context for large aggregates of data. The need for context is characterized by the need to put the data collected into a context that includes domain information relevant to the business, meta data, and additional factors that give the collected data significance and meaning. Merely acquiring the information doesn’t allow for a true understanding of it. Data needs to be seen through the lens of the circumstances that created it. It needs to be humanized so that its meaning can be fully understood, what anthropologists often call thick data.

This trend of using data mining techniques on large datasets to help reveal underlying behavioral and cultural significance is the main catalyst behind the emerging field of computational anthropology. The prevalence of massive amounts of data across several industries has lead to an increased need for thick data. For example, a group at MIT has used this technique to find who the most important people in history are, by culture. Using quantitative methods of data collection to derive cultural significance is not a new concept; there have been pockets of practitioners lobbying for more inclusion of computational anthropology since the 1990s. The goal of computational anthropology is to uncover systems of knowledge, shared identities, and underlying cultural markers that are wrapped up in the larger datasets.

The mobile revolution is well underway and changing the way that we see ourselves. The World Bank recently estimated that over three quarters of the world’s population now has access to a mobile phone. That translates into massive amounts of data in the form of geolocation, possible connections to the internet through smart phones, and more likely use of social media. This allows for an accumulation of information on human behavior that we didn’t have access to in the past.

In order to leverage this information, more attention is being paid to the study of behavior and the pattern of travel via location-based apps. There is considerable interest in understanding human mobility patterns so that application developers and data scientists can assist in predicting future patterns of behavior. A Chinese company name Zimo has partnered with Microsoft Research to look at the differing travel patterns of locals vs tourists in Beijing. They are interested in using this location-based app to put user behavior in context. Zimo’s app separates the daily travel patterns of locals from tourists by analyzing mobile check-ins. This data shows the mobility patterns of both groups and gives insight into how people are traveling around the city based on their designation as a local or tourist. By grouping the data by patterns, the research will allow prediction of traffic patterns, location based advertising, even disease control.

How You Can Get In On the Action

Advances in technology are actively changing the way that industries work and create products and services. As anthropologists, we are equipped to adapt and understand the significance of these changes. If you are a practicing anthropologist you can take some small steps to bring more quantitative analysis into your toolkit.

1. Brush up on your data analysis skills. Most anthropologists are familiar with SPSS. But what about STATA or R? These statistical packages and software are used more broadly in academia, and have vast abilities. Adding a skill like this to your resume will make you a desirable candidate for analysis and visualization. Concise analysis and data visualization are universal tools that businesses of all kinds use to make decisions.
2. Consider learning about database management. This could mean becoming more adept at Microsoft Excel, or moving into access and SQL databases. The reality is that. as stated earlier, most companies have a data problem. Being able to manage data in a database is becoming a minimum requirement for a research analyst, even if you are working that bachelors level. Adding a skill like this is also a compliment to qualitative research analysis, since it allows you to easily store data and query your results.
3. Learn more about digital marketing. Marketing has often aligned easily with anthropological training. These days a good marketing plan involves more analytics than before. Learning about SEO, web analytics and basic functioning of websites can help give you a leg up. If you are already leaning towards a UX role in the digital space, picking up these skills is a natural progression and can make you a unique team member.
4. Pick up some programming fundamentals. It is not necessary to know how to build out full software applications. But learning some basics of programming can help you go very far. The most popular language for scientific research is Python, which has statistical based libraries to help with data analysis. If you are interested in learning to code, even a little, I would start here. The ability to write up a short program to run analysis is very valuable, not to mention the ability to read code which is an overlooked asset when working in cross-functional and multi-disciplinary teams.

Picking up any one of these skills will set you a part as a researcher and help you to become more involved in the data driven organizations that so desperately need an anthropological perspective. The best part is that learning one of these skills does not require that you go back to school and obtain another degree. There are many online programs and tutorials that will walk you through the attainment of this knowledge. Not to mention the meet-ups and professional organizations around the world that will help introduce you to these skills and how they are used in practice. You can pick up something small, over time, and easily add it to your current skill set.

There is an evolving understanding in the data science community that the social sciences are integral to understanding of the data big picture. This need extends beyond better understanding of human behavior and includes being an agent for social good. In this article about the future of social science research by Urban Wire, there is a description of how data science is being used by cities. Places like Chicago are using data science to target homes with children that contain lead paint. Boston is using data science to provide restaurant health inspections to the public, and even detect health epidemics like Ebola.

We are living in a time when science and technology are being used as agents of change in the way that we live and work. As anthropologists, we are uniquely equipped to not only study these changes, but to be actively involved in them. The world of the future should not be designed and built only by engineers. This is a great time to stretch our skills and allow the breadth of our discipline to be an advantage in the midst of social and structural upheaval.

Astrid is a digital strategist, software engineer and data analyst. She started out with plans to be a doctor, but switched majors and received her BA in Psychology from Baylor University. Realizing that she wanted to be in the field more than in the lab, she pursued a MA in Anthropology from University of Houston, where she focused on medical anthropology and chronic disease. Since then, Astrid has developed a strong interest in technology, analysis and social identity. Her curiosity led her to attend The Iron Yard Academy and learn to become a software developer. Now she enjoys mixing and matching her anthropological training with her tech skills to create products and experiences that resonate with others. She is a co-founder of Prenovate, a digital personal nutrition program to manage chronic diseases. She is an organizer for Rails Girls, a workshop that teaches girls and women how to code. Astrid is currently attending University of Houston-Victoria pursuing a masters in computer science and math. When she is not studying or working, she enjoys writing at her blog ianthro.com. She also loves to hang out on twitter, and LinkedIn.

Works Cited

Bhatia, Loveleen (2014). “Data Science as an emerging career.” Deccan Herald. http://www.deccanherald.com/content/414438/data-science-emerging-career.html

Computational Anthropology Section (1998). Human Complex Systems. http://hcs.ucla.edu/companth/cas-aaa.html

Engler, Alex C. (2015). “The future of social science research: A data science perspective.” Urban Institute. http://www.urban.org/urban-wire/future-social-science-research-data-science-perspective

Emerging Technology From the arXiv (2014) “The emerging science of computational anthropology.” MIT Technology Review. http://www.technologyreview.com/view/528216/the-emerging-science-of-computational-anthropology/

Emerging Technology From the arXiv (2015). “Computational Anthropology reveals the most important people in History.” MIT Technology Review. http://www.technologyreview.com/view/535356/computational-anthropology-reveals-how-the-most-important-people-in-history-vary-by/

Strange, Victoria (2009). What Anthropologists Do. New York City: Berg

Wade, Nicolas (2010). “Anthropology a Science? Statement deepens a rift.” New York Times. http://www.nytimes.com/2010/12/10/science/10anthropology.html?_r=0

World Bank (2012). “Mobile Phone access reaches three quarters of the world.” World Bank. www.worldbank.org/en/news/press-release/2012/07/17/mobile-phone-access-reaches-three-quarters-planets-population