【加拿大国际治理创新中心CIGI】2024数据治理在释放数据价值中的作用报告_104页_6mb

报告摘要

Data governance faces challenges including fragmented frameworks, limited protections, capacity constraints, and difficulties in balancing privacy with innovation, particularly in the AI landscape where governance disconnects exist.
Foundational principles remain underdeveloped, and there's an urgent need for inclusive, multi-stakeholder approaches across jurisdictions to address data sovereignty, free flow, and common frameworks.

Data value is determined by context and its use as a representation, influenced by the supply chain of relationships connecting creation to use.
Value perspectives vary from individual privacy rights to collective rights, group privacy, human-derived data importance, and data as a strategic resource or relational construct.
Economic and social value stems from data sharing and combining diverse datasets, demanding ethical and fair frameworks for distribution.

Frameworks focus on competition policy, data transparency, digital public infrastructure (DPI), and innovative models like data wealth funds.
Market-based mechanisms for data valuation remain elusive due to opacity and lack of trust, while robust competition policy is vital.
DPI offers an alternative to private monopolies with public oversight to foster trust and responsible innovation; a data wealth fund has been proposed to incentivize data sharing and leverage its value.

Multi-stakeholder approaches, such as those explored by GPGN and CONNIE, are pivotal for connecting fragmented governance efforts and building whole-of-society solutions.
Inclusive governance, rethinking trade agreements on data flows, and global standards harmonization are critical to ensuring effective data governance environmentally, economically, and socially.
This essay series provides a more substantive and timely analysis of data governance challenges than its 2018 predecessor, advocating innovative strategies to navigate the complexities of the data-driven era.

展开完整摘要