This contribution explores the fine line between overestimated expectations and underrepresented momentums of uncertainty that correlate with the prevalence of big data. Big data promises a multitude of innovative options to enhance decision-making by employing algorithmic power to gather worthy information out of large unstructured data sets. Datafication—the exploitation of raw data in many different contexts—can be seen as an attempt to tackle complexity and reduce uncertainty. Accordingly promising are the prospects for innovative applications to gain new insights and valuable knowledge in a variety of domains ranging from business strategy, security to health and medical research, etc.
However, big data also entails an increase in complexity that, together with growing automation, may trigger not merely uncertain but also unintended societal events. As a new source of networking power, big data has inherent risks to create new asymmetries and transform possibilities to probabilities that can inter alia
affect the autonomy of the individual. To reduce these risks, challenges ahead include improving data quality and interpretation supported by new modalities to allow for scrutiny and verifiability of big data analytics.
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