The meaning of (big) data: A critical assessment of datafied norms and practices for equality and diversity in organisations
Data analytics and (big) data are getting more prominent within organisation and management studies, as well as for practitioners. While scholars consider how to use big data to assess organisational processes in a more holistic and fine granular approach compared to surveys or interviews (George et al. 2016), a critical examination of what increased datafication means for organisations, equality and diversity is missing. Datafication refers to the use of information which is transformed into a computable format, analysed and then utilised in a novel way (Mayer-Schönberger and Cukier 2013). These steps –collection, transformation and analysis– are accomplished by individuals and their outcomes are thus socially shaped (Merry 2016; O’Neil 2016). Data can therefore be seen as a socio-technological assemblage (Sassen 2017, 2006) that transcends locations and blurs the lines between consumers, suppliers and organisations. Considering that all organisations are hallmarked by an inequality regime that manifests in different forms and in different spheres (Acker 2006), it can be assumed that the way (big) data are utilised within and by organisations is no exception. Rather, one could suspect that existing inequality regimes get datafied and hence reified in algorithms and metrics.
Datafication varies by context, aim and actors. At the macro level, the socio-economic environment shapes the datafied organisation. Organisations located in territories with higher educational/technological investments might utilise data with different aims and tools than other organisations. At the meso level, the organisations’ overall business and diversity strategies might influence the aim and orientation of datafication. Practices such as risk assessment, an increase in market share, providing a more inclusive service/product or creating a more inclusive workplace can be influenced by data analytics. This manifests, amongst others, in the central or peripheral location of the data analytics team within organisations and their (missing) connections to other departments. At the micro level, social agents bring in their own norms, thinking and approaches towards their work with (big) data. Those agents co-shape the outcome of data analytics in deciding what questions to ask, what actors to include and how to process and communicate data. All three layers have an influence on what information can be derived from data analytics, what influences decision-making and what forms of dominations within organisations are implicitly reproduced.
To address the risks and benefits of (big) data, we invite scholars to assess the potential impact of datafication and the use of (big) data in organisations from a critical and multi-disciplinary perspective, especially but not limited to decolonial and intersectional approaches. This may include the following topics:
· Regional differences in access to and use of (big) data in organisations
· Challenges and opportunities of datafication for organisations
· The social and cultural impact of datafication for organisations
· Characteristics and purposes of contemporary datafication processes within organisations
· Effectiveness of datafication regulation in organisations
· Data analytics as a tool for equality, diversity and inclusion in organisations
· Data analytics and new divisions of labour
· Data analytics and new inequality regimes
· Ethical dilemmas of big data analytics as they pertain to employees and society
· Hidden agendas of datafication, including manipulation and co-optation
· Intended and unintended consequences of datafication for organisations
· Key organisational actors of datafication, their privileges and disadvantages
· Influences of individual identity and biases on the process of datafication
Please submit a 500 word abstract (excluding references, one page, Word document NOT PDF, single spaced, no header, footers or track changes) together with your contact information to firstname.lastname@example.org. The deadline for submission of abstracts is January 31st 2019, and we will notify you of our decision by the end of February.
Acker, Joan. 2006. “Inequality Regimes: Gender, Class, and Race in Organizations.” Gender & Society 20 (4): 441–64. https://doi.org/10.1177/0891243206289499.
George, Gerard, Ernst C. Osinga, Dovev Lavie, and Brent A. Scott. 2016. “Big Data and Data Science Methods for Management Research.” Academy of Management Journal 59 (5): 1493–1507. https://doi.org/10.5465/amj.2016.4005.
Mayer-Schönberger, Viktor, and Kenneth Cukier. 2013. Big Data: A Revolution That Will Transform How We Live, Work and Think. London: John Murray.
Merry, Sally Engle. 2016. The Seductions of Quantification : Measuring Human Rights, Gender Violence, and Sex Trafficking. The Chicago Series in Law and Society. Chicago, The University of Chicago Press, .
O’Neil, Cathy. 2016. Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy. London: Allen Lane.
Sassen, Saskia. 2006. Territory, Authority, Rights: From Medieval to Global Assemblages. Territory, Authority, Rights: From Medieval to Global Assemblages. Princeton University Press.
Sassen, Saskia.. 2017. “Predatory Formations Dressed in Wall Street Suits and Algorithmic Math.” Science, Technology and Society 22 (1): 6–20. https://doi.org/10.1177/0971721816682783.