Purpose – In the context of growing interest in the use of data to create mathematical models that can predict future behaviors and assist in business decision making, this theoretical-empirical article aims to understand how realities are enacted through data, from the identification and description of the practices and knowledge of two professional groups that work with data.
Theoretical framework – Actor-Network Theory and Theory of Practice.
Design/methodology/approach – The research was developed under the methodological guidance of the Actor Network Theory, from November 2017 to September 2018, including interviews and observations of daily work.
Findings – Results suggest that the realities portrayed in the predictive models are constituted from arrangements of human and non-human, situational and emerging elements, contemplating data, technological potentialities and constraints and political choices that permeate these configurations.
Research, Practical & Social implications – The research demonstrates that knowledge and predictive models generated from data cannot be understood without taking into account their contexts of origin, which makes it possible to question their supposed neutrality and objectivity. Considering these compositions, these models can lead to process optimization, but also to unexpected effects, such as errors in scenario projection.
Originality/value – The research contributes by making visible the configurations of human and non-human, situational and emergent elements, through which organizations and realities are enacted, based on data practices.
Keywords - Professionals who work with data; predictive models; enacted realities; Actor-Network Theory; Theory of Practice.
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