Harnessing Data and Intelligence for Collective Advantage 2026

The data ecosystem for forced labour prevention BOX 5 Within the Partnership, five broad categories of actor contribute to and benefit from data collaboration: Workers: Individuals and communities whose safety and empowerment depend on the insights that lead to better protection and remedy. They benefit when shared data translates into earlier intervention, safer recruitment and access to remedy. Data providers: Organizations that generate data on risk or prevalence, such as government labour inspections, corporate audits, grievance mechanisms, recruitment agency records, worker surveys and NGO case documentation. Their contributions build the evidence base that makes risk visible and drives collective accountability. Data users: Entities that apply insights for decision-making, such as companies and employers integrating risk data into due-diligence systems; government ministries targeting inspections based on trends; international organizations coordinating action; donors or investors directing resources towards prevention; and even workers themselves. These users rely on visibility to hire responsibly, meet buyer expectations and strengthen compliance across supply chains. Data stewards: Actors responsible for data quality, privacy and governance, including technology providers, national data authorities and academic or research institutions that validate analytical integrity. They ensure that data remains accurate, secure and ethically managed throughout the system. Intermediaries: Organizations that translate and connect data across systems, for example, federated infrastructure partners, multistakeholder industry platforms and civil society intermediaries that enable collaboration between sectors. They make interoperability possible, ensuring that insights flow across boundaries and reach those who can act on them. The Partnership’s theory of change explains how better data collaboration can address the systemic barriers outlined in Section 1 of this paper: fragmented incentives, disconnected information systems and limited capacity for coordinated action. It brings together three interdependent dimensions – incentives, technology and impact – that together create the conditions for collective impact. Incentive alignment –Stakeholders face rising expectations for transparency and ethical conduct. The Partnership helps them meet these expectations more efficiently by sharing evidence rather than duplicating effort. –Governments gain clearer visibility to target enforcement and allocate inspection resources. –Businesses strengthen compliance with forced labour laws and trade requirements while improving the credibility of due-diligence reporting. –Civil society organizations and unions amplify worker voice through integration of grievance and case-management data, strengthening advocacy and accountability. –Investors and donors obtain more reliable indicators of performance and measurable results.Technology enablement –Advances in federated data systems, privacy-preserving technology and AI make collaboration on data technically feasible and legally compliant. –Queries are executed at the data’s place of residence, with only aggregated, anonymized insights exchanged across the network. –This federated design removes the need for a central database while maintaining analytical power and traceability across multiple data owners. Impact generation –When data sources connect safely, patterns of risk, prevalence and root causes become visible and actionable. –Collective intelligence supports earlier intervention, targeted remediation and more efficient use of resources. –The intended result is measurable improvement in worker protection, policy targeting and systemic accountability.2.2 The theory of change Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains 11