Do more data equal more truth? Toward a cybernetic approach to data. Issue 2 (31st May 2021)

• Record Type: Journal Article
• Authors: Bell, Genevieve; Gould, Maia; Martin, Brenda; McLennan, Amy; O'Brien, Ellen
• Is Part Of: Australian journal of social issues. Volume 56:Issue 2(2021)
• Page Start: 213
• Page End: 222
• Publication Date: 2021-05-31
• Subjects: cybernetics -- data -- digital technology -- disadvantaged groups -- inequality; Australia -- Social policy -- Periodicals; Social service -- Australia -- Periodicals; Australia -- Social conditions -- Periodicals; Social conditions; Social service; Social policy; Australia; Periodicals; 362.99405
• DOI: 10.1002/ajs4.168
• Languages: English
• ISSNs: 0157-6321

Abstract

Abstract : In Australia, as in other places in the world, we accumulate and curate Big Data – in the public and private spheres; as consumers and citizens; for profit, for proof and for the promises it holds. Yet, data are only ever a partial snapshot of the lives of humans, communities, environments and places. Data has its place, but in order to use data to effect change, we need to understand which questions to ask, which assumptions to suspend, which data to collect and how to interpret them, we need knowledge of the broader context. Taking a systems‐level view of data allows us to ask questions about it as a technical and sense making apparatus, as well as about the consequences of data collection, storage and use. This article advocates for a cybernetic approach to thinking about and using data. Cybernetics can be broadly understood as the study of feedback, communication and control in a system. A cybernetic approach to Big Data means de‐centring data to focus on a broader system of cultural, technological and environmental dimensions. In this approach, the system itself is the core unit of analysis, and also a vantage point or an approach through which to encounter and re/structure such systems. Employing a cybernetic approach allows us to move beyond attempts to order and reorder information, and instead see it as making implicit knowledge explicit, and then knowing that the hard work is what to do with that knowledge. Through this we are able to conceptualise more complex inputs, outputs and interactions, than simply those captured in a dataset. The insights drawn from a particular dataset or datasets, or from a particular model or algorithm (however well parameterised) never represent the whole truth, and a cybernetics framing enables practitioners to say, if that is the case, what now? A cybernetic approach suggests we should preference deeper understanding, not simply more truth.