Shared-data Governance Frameworks

Data has emerged as the cornerstone of the modern data-driven economy, facilitating the generation of new value, innovations, and societal benefits through the application of data analytics, machine learning, and artificial intelligence methodologies. However, the reliance on large datasets, historically sequestered within private silos by major corporations, poses a significant barrier to broader data access and the realization of its inherent value. The concept of data markets has been proposed as a means to surmount this obstacle, enabling diverse stakeholders to share and access requisite data. Yet, data sharing introduces novel challenges, as conventional data governance frameworks have primarily addressed intra-organizational scenarios, underscoring the imperative for robust governance structures tailored to interorganizational data sharing contexts. The governance of shared data presents formidable complexities, necessitating the collective commitment of multiple entities to ensure the appropriate collection, storage, processing, and disposal of data. Moreover, these responsibilities are contingent upon the nature of the data and the regulatory and ethical frameworks governing its utilization. In this paper, we explore these challenges, delineating the regulatory and ethical imperatives, with particular emphasis on the management of personal data. Subsequently, we propose a comprehensive governance framework for shared data aligned with widely endorsed principles.