The opportunity of big data processing resides in its vast scope and the potential applications it opens up. Vast amounts of behavioural data are collected and subjected to data mining and machine learning techniques, which extract knowledge and behavioural patterns. As big data practices become more widespread, a number of implications arise, methodologically and ethically. While the use of such computational approaches provide a competitive advantage and invaluable asset for strategy- making, we argue that the insights extracted may lack the rich detail of qualitative approaches and occlude ethical issues of mining digital traces of human behaviour.
Therefore, I will start with a discussion around the hubris and opportunities around big data processing in the field of consumer research; secondly, I will present a protocol for the processing of such vast data sets that draws on business, computational social sciences and data mining research; lastly, I would like to highlight initial applications of this iterative protocol and findings.
Questions explored at the intersection of business research, critical data science and data mining:
• How can big data be meaningfully conceptualised considering the hubris and opportunities surrounding it?
• What are some possible ways to improve current ‘big data’ methodologies, by integrating traditional qualitative methods and narratives of behaviours in the context of consumer research?
• How do we uncover social nuances and ethics in data sets?
The developed protocol was applied to an initial case study that addressed Sullivan and Gershuny’s  concern about contemporary consumption behaviours, consumption modalities, as well as markets: in societies belonging to liberal markets, such as the UK, leisure time is shortest for those who have the most to spend. Addressing this relationship, we explored the behaviour of time- poor and time- rich consumers, in the context of convenience shopping.
Based on the findings of this initial case study, we then looked into a novel way of modelling daily shopping behaviours, with the use of dimensionality reduction techniques and clustering individuals into homogenous behaviour spaces; ‘windows’ on to the data were created to uncover social nuances and potential ethical issues. A final case study is looking into the interface between consumers' attitudes and behaviours and how habits and traits are hijacking our responses when it comes to food consumption. In the end, these case studies are expected to contribute to the refinement of the ‘Data ethnography’ protocol and feedback from marketing executives, sociologists and data mining practitioners is projected.
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This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/G037574/1) and by the RCUK’s Horizon Digital Economy Research Institute (RCUK Grant No. EP/G065802/1).