Data Collaboration for the Common Good

The report argues that while public-private data collaboration can accelerate disaster response and global development, a systemic 'trust deficit' hinders its scale. It introduces a five-part framework—stakeholder alignment, responsible data governance, insight generation, insight adoption, and economic sustainability—to balance the imperative to innovate with the need to protect vulnerable populations from data misuse.

• Public-private data collaborations are categorized into three primary clusters: humanitarian action and crisis response, global development, and official statistics to support the Sustainable Development Goals (SDGs).
• A significant 'trust deficit' among individuals, institutions, and industry threatens the potential of using private sector data for the common good, creating a tension between the risks of data misuse and the 'opportunity-cost' of 'missed use' of data.
• The World Economic Forum identifies six dimensions of a trustworthy data system: security, accountability, transparency, auditability, equity, and ethics.
• Effective stakeholder alignment requires balancing diverse incentives and conducting rigorous due diligence to ensure commitment, particularly regarding the needs of 'digital invisibles' and vulnerable populations.
• Responsible data governance must extend beyond legal compliance to include risk-based assessments of 'group harms' and the use of demographically identifiable information (DII), as anonymized data can often be re-identified.
• Trust in insight generation is challenged by algorithmic bias and the 'black box' nature of AI/ML; the report suggests using 'helper algorithms' and interpretability measures to ensure outputs are explainable.
• Insight adoption is often hindered by a lack of 'data bilinguals'—individuals possessing both deep domain knowledge and data-science skills to bridge the gap between technical experts and senior decision-makers.
• Economic sustainability for data collaborations varies by use-case, ranging from donor-subsidized models for crisis response to subscription, 'freemium', or commercial models for long-term development services.
• The report outlines three technology archetypes for data collaboration: Centralized (consolidated data in one location), Federated (data remains with holders and is accessed via APIs), and Distributed (utilizing blockchain for a single source of truth).

APA

World Economic Forum (2019). Data Collaboration for the Common Good. http://www3.weforum.org/docs/WEF_Data_Collaboration_for_the_Common_Good.pdf

Chicago

World Economic Forum. Data Collaboration for the Common Good. 2019. http://www3.weforum.org/docs/WEF_Data_Collaboration_for_the_Common_Good.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Data Collaboration for the Common Good |date=April 2019 |url=http://www3.weforum.org/docs/WEF_Data_Collaboration_for_the_Common_Good.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2019data, author = {{World Economic Forum}}, title = {{Data Collaboration for the Common Good}}, institution = {World Economic Forum}, year = {2019}, month = apr, url = {http://www3.weforum.org/docs/WEF_Data_Collaboration_for_the_Common_Good.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }