White Paper Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows

The document argues that while data flows are essential for the modern economy and societal functions, they are increasingly hindered by divergent national regulations and data localization requirements. It introduces the 'Osaka Track' as a collective governance process to achieve 'Data Free Flow with Trust' (DFFT). The paper outlines a four-pillar architecture for data governance—transfer mechanisms, legal and regulatory cooperation, technical standards, and international trade rules—and provides specific recommendations for governments and businesses to enhance interoperability and trust, particularly regarding personal and non-personal data.

• Data restrictions and localization requirements impose significant economic costs, with some measures lowering a country's GDP by up to 0.4% for general restrictions and 1.7% for localization.
• Cross-border data flows provided a substantial boost to the global economy in 2014, adding $2.8 trillion (or 3.5%) to world GDP, which exceeded the impact of global goods trade.
• Data localization measures specifically targeting IoT and M2M applications in South America, South-East Asia, and Africa could reduce productivity and revenue gains by 59-68%.
• The 'Osaka Track' architecture for data governance consists of four complementary pillars: transfer mechanisms, legal and regulatory cooperation, technical standards and industrial cooperation, and international trade rules.
• A significant gap in global data governance exists regarding non-personal information due to differing definitions of metadata and mixed data sets, which can lead to the application of privacy laws to non-personal data.
• The document recommends that trade agreements, such as the WTO Joint Statement Initiative (JSI), should prohibit data localization, the mandatory disclosure of source code or algorithms, and customs duties on electronic transmissions.
• Data-driven technologies are critical for addressing the challenges of ageing societies in Europe and East Asia, with AI applications potentially saving $150 billion annually in the US by 2026.
• The document advocates for a 'human-centric approach' to data governance that is agile and risk-based to keep pace with rapid technological innovation.
• Developing economies face significant gaps in data governance, with only 66% of 107 countries having enacted privacy laws and 56% of 125 countries having online consumer protection laws.

APA

World Economic Forum (2020). White Paper Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows. http://www3.weforum.org/docs/WEF_Paths_Towards_Free_and_Trusted_Data%20_Flows_2020.pdf

Chicago

World Economic Forum. White Paper Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows. 2020. http://www3.weforum.org/docs/WEF_Paths_Towards_Free_and_Trusted_Data%20_Flows_2020.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=White Paper Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows |date=May 2020 |url=http://www3.weforum.org/docs/WEF_Paths_Towards_Free_and_Trusted_Data%20_Flows_2020.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2020white, author = {{World Economic Forum}}, title = {{White Paper Data Free Flow with Trust (DFFT): Paths towards Free and Trusted Data Flows}}, institution = {World Economic Forum}, year = {2020}, month = may, url = {http://www3.weforum.org/docs/WEF_Paths_Towards_Free_and_Trusted_Data%20_Flows_2020.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }