Measuring reality is one the ways people are trying to understand the world. Since the early development of science researches were looking for the ways to describe the phenomena’s that surrounded them. We know a lot about how to collect data, how to analyse them but most importantly, we also know what kind of knowledge we can build through the use of different research methods.
In the context of discussion on how computational turn may influence research, danah boyd and Kate Crawford, have outlined six provocations for the Big Data phenomenon: its limits and biases, and present the critical reflection on the consequences of blind use of Big Data in research methodologies (boyd and Crawford, 2012). One of the interesting examples they present is the change in describing social relations, from the moment when sociologists and anthropologist were among those who researched these and conducted their research through surveys, interviews in order to describe notion, quality and values of personal networks, to the moment when our relations started to leave the digital trace in transmission data: who do we contact, how wide is our social network, what are the schemes of our movement. Does necessarily setting up “See first” when following someone on Facebook means that we are in close relation with this person? Quantified data provide us different type of information, and therefore we can’t say that it’s contradictory to the previous one, it’s just different. Therefor researchers came up with the concepts of ‘articulated networks’ and ‘behavioral networks’, Big Data gave birth to these concepts and allows for researching them. As authors say: “‘Change the instruments, and you will change the entire social theory that goes with them,’ Latour reminds us”.
Tricia Wang comes up with another perspective on the topic. Her 2013 article “Why Big Data Needs Thick Data” started a discussion on the benefits on complementing Big Data with qualitative data (Wang, 2013). She works on quantification bias and points out the problems that comes up when industries use only information generated by quantitative data for their market and development strategies. According to her, it’s not enough, as it doesn’t represent the whole spectrum of social and cultural relations that take places and are performed in relation to every market product. Thick Data is data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories, and models of their world. Big Data can provide patterns, but they often lack explanation or, completely opposite, Big Data analyses generates knowledge gaps that can’t be solved by providing more quantitative data. That’s the moment when ethnographic research can bring great value.
Calling back to researchers’ ethics and responsibility, there is no objectivity neither in the tools we use for our research or in the data analysis methods. It’s important to take into account what is the interest of our research funders (business, academia, social organisations) and reflect on our own political responsibility when deploying our research results.
boyd, d. and Crawford, K. (2012) ‘Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon’, Information Communication and Society, 15(5), pp. 662–679. doi: 10.1080/1369118X.2012.678878.
Wang, T. (2013) ‘Big Data Needs Thick Data’, Ethnography Matters, http://ethnographymatters.net/blog/2013/05/13/big-data-needs-thick-data/
Author: Agata Jałosińska