Data analysis is often positioned as a neutral, technical process, yet it is deeply shaped by historical, political, and epistemic power structures. Contemporary data cultures frequently prioritise scale, speed, and prediction, reinforcing forms of abstraction that strip knowledge from its social and relational context. As Warren Neidich argues through the concept of the “Statisticon”, predictive technologies reduce future possibilities into probabilities, encouraging cultural habituation to standardised narratives that resist complexity and uncertainty (Neidich, 2018).
This logic mirrors extractivist models more commonly associated with land and resource exploitation. Walter Rodney’s analysis of colonial extractivism demonstrates how underdevelopment is produced through the systematic extraction of labour and resources for external gain (Rodney, 1972). When applied to research and data practices, extractivism describes situations in which knowledge is taken, decontextualised, and repurposed without reciprocity, disproportionately benefiting institutions or researchers while disempowering those from whom the knowledge originates.
Leanne Betasamosake Simpson extends this critique through the notion of cognitive extractivism, describing the extraction of Indigenous ideas into economic or symbolic capital while severing them from the relationships that give them meaning (Simpson, 2017). Similarly, Silvia Rivera Cusicanqui highlights how epistemic extractivism operates through academic citation economies, where Indigenous and decolonial knowledge is consumed in “regurgitated” forms that reinforce hierarchical structures of legitimacy (Rivera Cusicanqui, 2012).
Within data analysis, these dynamics are intensified by big data paradigms that privilege pattern recognition over situated meaning. As Giorgia Lupi notes, the dominance of quantitative abstraction risks producing data systems devoid of humanity, where uncertainty, subjectivity, and lived experience are systematically excluded (Lupi, 2017).
My work is positioned as a critical response to these extractive tendencies. Rather than treating data as a raw material to be mined, I approach it as relational and narrative, foregrounding emotional trajectories, positionality, and context. By resisting purely extractive logics, my practice seeks to reframe data analysis as an ethical, situated act of storytelling rather than a neutral process of reduction.
References
Lupi, G. (2017) Data Humanism. Available at: https://giorgialupi.com (Accessed: date).
Neidich, W. (2018) Glossary of Cognitive Activism. Berlin: Archive Books.
Rivera Cusicanqui, S. (2012) Ch’ixinakax utxiwa: On Decolonising Practices and Discourses. Buenos Aires: Tinta Limón.
Rodney, W. (1972) How Europe Underdeveloped Africa. London: Bogle-L’Ouverture Publications.
Simpson, L.B. (2017) As We Have Always Done: Indigenous Freedom through Radical Resistance. Minneapolis: University of Minnesota Press.
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