Pure Logical Thinking cannot yield us any knowledge of the empirical world; all knowledge of reality starts from experience and ends in it.” (Einstein, A. Ideas and Opinions (p.271). Crown Publishing Group.
Anthropology, at its core, is known for its work in places that seem exotic and unknown. Yet, at present, there has been a surge of anthropologists championing the understanding of human nature in business settings.
Right at this moment within the business world, we are swimming in an ocean of data. Data is everywhere. According to popular opinion, every day we are producing the same amount of data that it took from the dawn of civilization until 2003. Internet of Things (IOT) has already become a reality and as more and more physical objects become connected to the internet, we will enter the Brontobyte Era. It is widely known at present that computational systems are out there in the wild working like a black box. A promise towards “fast results” has given data scientists a good reputation. This is why, except for mature organizations, it is hard for mixed methods researchers to champion the power of context which makes up the meaning for the data. Sometimes, the reason is more political, than intellectual. At the core, I believe, ethnographic methods, along with the inferences from anthropology helps to solidify the sociocultural implications of data science work in business.
What I do is called ‘people analytics’. I work with employee data, where data is gathered in organizations with the intention to identify, measure, and design workplace behavior. These data are sourced and analyzed to acquire better talents, implement employee well-being, identify valid and reliable key performance indicators, as well as finding reasons behind many hidden organizational drawbacks that can help foster an effective organizational engagement among employees. By practice, I am both a business anthropologist and a data analyst, but for the argument I will keep these two in separate worlds and talk about how we can make them dance together.
The Business Anthropologist
In fast-paced business settings, we do the fieldwork just like a traditional anthropologist would do. We need to empty our cup every day to adjust to the business dynamics. Provided the given context and cultural understanding, we need to provide insight within a short-term framework in order to increase the value of the business. We draw our inferences from anthropological worlds, mix it with other appropriate social sciences when needed. We work in the worlds of user research and design, systems development, change management, market research and consumer behavior, and many others. In the business world life histories are named, “customer/employee journeys”, ethnography is sometimes called by others as just “interviews”, and where we tell our stories visually, and businesspeople identify them as a “case study”. In our secret world, we are all business anthropologists, but to the rest, we are just different roles in different organizations.
An innovation strategy, or even a problem-solving one, that is seen through socio-cultural terms and informed by ethnographic thinking, helps organizations communicate with audiences about how can processes blend with everyday lives. Quantitative data analysis can help find patterns, maybe with proper models take us close to induction, but the deduction part of it needs a solid understanding of the values, habits, behaviors, experiences, cultural norms of groups as well as ecological factors that influence the consumers. Mack and Squires (2011) point to the shift of many practicing ethnographers from the field to management as their career develops because they embed their decision making insights rooted in ethnographic thinking.
A business anthropologist looks at the social world of business, as dynamically evolving, emergent systems. They are emergent systems because people reflexively respond to the present and past, and the responses shape what they do in the future. Years of building ethnography from this core have generated both analytical techniques and a body of knowledge about socio-cultural realities, most recently in business. A major example is shared by Professor Julia Gluesing, where thirty teams were able to function again to the company’s expectations only when they arrived at an awareness of the defintion of ‘market research’ was different for teams in different countries. No measures of data analysis can infer such subjective rationale.
The Data Analyst
Data analytics, across its variety of forms, is rooted in statistical calculations— involving both the technical knowledge and skill to assess the validity and applicability of large models, as well as to implement software or programming functions that execute these models. Within the applications of statistical calculations lie assumptions of a) systematic structures and their dynamics, and b) whether a group’s variability can be measured within that structure; to separate the signal trends from the noise, thereby leading us to be less wrong about the insight we are searching for.
Historically, these skill sets and conceptions of reality have been most heavily utilized in scientific inquiry as well as in finance, insurance, business operations research (e.g., supply chain management and resource allocation). More recently, data analytics has expanded into a much larger set of domains: marketing, medicine, entertainment, education, law, etc.
However, at this point, I will borrow the term “ethnographic analytics” from Chad Maxwell where we combine anthropological thinking and ethnography with data analytics and try to make them dance together.
Dance Partners: The Business Anthropologist & The Data Analyst
Conceptually, we regularly interpret data analytics and ethnography as polar ends of a research spectrum—one as sifting through colossal data sets, the other as a slow sense-making of experiential immersion. To point out how useful combining these practices are, Chad Maxwell exemplifies a recent San Francisco app called “SceneTap,” mentioned in the book, “Advancing Ethnography in Corporate Environments”. Chicago bars were using visual pattern recognition technologies with this app and smartphone users could readily connect to the bar scene data by in-bar cameras. With facial detection analytics, the app posted information such as the average age of a crowd and the ratio of men to women to help the bar hoppers decide on where to go. The question is, is this an invasion of privacy? In San Francisco, the epicenter of technology, the app was not received well by the public, especially women.
If “man is an animal suspended in webs of significance he himself has spun” (Geertz), it’s not just ethnography that proves to be of valuable skill, data analytics is also important. Because in today’s world, when we systematically trace out the logic of those webs we examine how those webs are structured. The scope is large, and time is short. That’s where a dance of a business anthropologist and data analyst is needed.
A difference is that in many data analytics scenarios, the available data has already been collected, whereas most ethnographic projects require field research to gather new data. This difference in perspectives leads to different attribution models for the results. Data analysts will readily recognize that they made decisions throughout the project that impacted the results, but will often characterize these decisions as being determined by the data analyses. But for a business anthropologist, the data is present and the context is live. Furthermore, you are also aware of all the other contingent factors involved in the data you collected at that moment. However, we have to be explicitly reflective and critical of how our social position influenced the results. We have to take a cold hard look at these issues of context and social positions as part of the analysis process.
While both areas have a core set of expectations, they both have to extend beyond their core in order to deal with data about social life—data that has very real social consequences. This is all the more true in industry contexts, where we often have to make social decisions, or design decisions, which impacts a lot of lives, regardless of expertise.
Therefore, the value ultimately bears down to the concept of “trained judgment”, and how one can accommodate the best processes along to provide it. The relative value of a business anthropologist and a data analyst lies in the fact that they can draw on to steer a set of transformations (be it data manipulations or valid and reliable inferences) towards a better, more robust and in-depth set of outputs.
Trained judgment is determined from which aggregation function, or classification method, best transforms data into appropriate patterns. Analogously, each ethnographer also has their way of going from field notes or verbatim speech to a higher-level pattern that says something about the research question. This conceptualization of speeches and stories is also an “aggregation function” in a way, but the aggregation happens through identifying shared qualities in the data. And that is a matter of trained judgment. Including them both, only increases the depth, putting a justified deadline intensifies it.
Ultimately, both processes are not a linear set of steps. But a key difference is that ethnographic work critically assesses the role of the researchers as an explicit, expected part of the research process. If data science projects were truly determined by the data alone, then repeated analyses about people should yield identical results. Do they? There is unquestionable value in using statistical models as lenses to interpret and forecast socio-cultural trends—both to business and academia. But that value is entirely dependent on the quality of alignment between the statistical model and the sociocultural system it is built for.