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We begin by summarizing the relevance and importance of inductive analyticsbased on the geometry and topology of data and information. Contemporary issuesare then discussed. These include how sampling data for representativity isincreasingly to be questioned. While we can always avail of analytics from a"bag of tools and techniques", in the application of machine learning andpredictive analytics, nonetheless we present the case for Bourdieu andBenz\'ecri-based science of data, as follows. This is to construct bridgesbetween data sources and position-taking, and decision-making. There is summarypresentation of a few case studies, illustrating and exemplifying applicationdomains.

The Geometry and Topology of Data and Information for Analytics of  Processes and Behaviours: Building on Bourdieu and Addressing New Societal  Challenges