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Big Data: analisi e proposte

Andrea Fedi * e Monica Riva **

Questo breve articolo ricapitola la nozione di Big Data e il rapporto difficile tra detti Big Data e le attuali regole sulla protezione dei dati personali (limitazione del trattamento, trasparenza, consenso informato, profilazione e decisioni automatizzate). A valle di tale sintesi, gli autori provano a indicare un cammino interpretativo per riconciliare l’uso massivo di ampie banche dati a fini di profilazione con i principi del GDPR.

Big Data: analysis and proposals

This short article recapitulates the notion of Big Data and the difficult match between Big Data and current data protection rules (purpose limitation, transparency, consent, profiling and automated decisions). After doing that, the Authors try to indicate an interpretative way to reconcile massive use of large data sets to profile individuals with the principles of the GDPR.

1. Introduction

The term “Big Data” has long since appeared in the jargon of practitioners interested in legal ramifications of new technologies. By this term it is common to address extremely large data sets that may be analysed computationally to extract inferences about data patterns, trends and correlations [1]. In other terms, the term refers to a phenomenon characterised by

i. a magnitude requirement (large data sets),

ii. a methodology (computational analysis, i.e., through a machine),

iii. a final goal (the extraction of knowledge from the data sets, as a miner extracts a mineral from a mine) [2].

The current technology has indeed opened doors to the possibility to collect and process huge amounts of data, and extract from their analysis new and predictive knowledge with great ‘velocity’, from large ‘volume’ databases containing a ‘variety’ of different data (so large and various that a natural person could not reasonably make such analysis), controlling their ‘veracity’ and, eventually, creating ‘value’ [3].

Indeed, the computational insight of data fields (also through artificial intelligence, AI) consents to find (often unexpected) correlations among data and draw consequences (forecasts) almost in [continua..]

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