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Data governance

Data is generally defined as the ‘oil’ of the 21st century economy: companies that successfully collect and process a large amount of data (i.e., Big Data) manage to provide more personalized services to their customers and reduce their production costs, by thus becoming more competitive. During the past decade, online platforms have proved successful in this regard, by collecting, combining, and extracting useful information from the personal data provided by their users. The advent of the Internet of Things (IoT) is expected to lead to an exponential grow of the collected data: sensors installed in different electronic devices are collecting an increasing amount of data, including a vast amount of non-personal data, concerning the environment where the devices operate as well as diagnostic info. The increased amount of collected data is essential to train the algorithms, at the core of AI technology. The new role of data as a business asset has shifted the way how firms and organisations approach and manage their dataset. An efficient use, transferring and sharing of data can improve several industries, increasing innovation and socio-economic prosperity. For this reason, the development of data governance frameworks has become a strategic initiative in different industries, from transport to the telecommunications and advertising industries, as well as health and smart cities.  

Having this in mind, this research topic examines the development of suitable data governance models, looking at the legal, economic and technology aspects involved in this process from a multidisciplinary research approach, which comprises different research areas, such as data protection and intellectual property law, open data and innovation, as well as data engineering.  

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