The need to protect and strengthen labor rights within corporate supply chains drives a great deal of Humanity United’s work. We recognize that integrated data could have a powerful and positive effect on that work, so since 2020 we’ve been exploring answers to two crucial questions:
- Could more open and integrated supply chain data lead to greater corporate remediation and action?
- What incentives (or disincentives) exist for corporations and NGOs to embrace such an approach?
The integrated data learning area at Humanity United was born out of a growing demand for labor rights data, particularly as the Working Capital Fund was launched in 2017. The fund aims to support companies developing tools that enabled brands, retailers, and suppliers to source responsibly while ensuring that workers could access and benefit from these solutions. However, as the responsible supply chain market expanded, a critical gap emerged— the availability of information that can inform responsible sourcing, grounded in a transparent, worker-centered data ecosystem.
Research in this area showed that unlike other mature technology markets, the supply chain sector lacked a broader data infrastructure to support open and integrated approaches. Key elements were missing, including shared corporate reporting taxonomies, interoperable data platforms, and frameworks that connected existing technologies. The industry was dominated by proprietary models that collected labor data on behalf of individual clients without sharing insights across the sector, so corporate-driven solutions often focused on risk mitigation rather than proactive engagement or remediation of labor abuses.
Meanwhile, emerging technologies such as blockchain, artificial intelligence, and worker-driven mobile polling tools offered new ways to gather, analyze, validate, and share supply chain data responsibly. These innovations had the potential to break down silos, increase transparency, and enhance impact, but only if they were built on quality, standardized, and ethical data models.
Five years after beginning this work, we see both promising developments and major challenges.
Seeding Foundations for Open Data
Our initial focus was strengthening data institutions — organizations that responsibly steward labor rights data. We worked with the Open Data Institute (ODI) to build a global register of data institutions, recognizing that good governance and clear mandates are essential to ensure that data is not only collected but used effectively for social impact. This led us to engage with the Open Apparel Registry, now Open Supply Hub (OSH), which expanded beyond apparel to become a neutral steward for supply chain transparency data across industries.
OSH demonstrated the power of multi-stakeholder governance structures in maintaining neutrality while serving a diverse range of users. It also highlighted the need for robust in-house technology development to ensure that open labor data tools remain accessible, relevant, and worker-centric.
Another early project involved supporting the Mosaic platform by the Business & Human Rights Resource Centre. This grant enhanced Mosaic’s capabilities by adding technical application programming interfaces (APIs) that allow other organizations to access and integrate its data more seamlessly. From this experience, we learned that different data users (corporations, investors, unions, and civil society) have vastly different needs, and aligning open data with investment reporting taxonomies can significantly enhance adoption and impact.
Testing a Networked Approach
As our support for individual organizations matured, we piloted a peer-learning network (PLN) in partnership with ODI. This model aimed to support more data institutions with both hands-on mentorship from ODI and insight from peer organizations across the supply chain and labor rights sectors.
Key Takeaways of the PLN
- Knowledge sharing accelerates innovation. By facilitating open discussions, the network sparked new collaborations, particularly in data utilization and access strategies.
- Worker organizations and unions remain underrepresented as data stewards. While corporate and civil society actors have led much of the open data conversation, there is a need for more worker-driven data models.
- Networking improves data quality. Peer engagement helps organizations better understand user needs, ensuring that labor rights data is structured and shared in a way that is actually useful for workers and advocates.
Challenges and Opportunities
- Funding and Sustainability Gaps
Open data institutions face sustainability challenges. Unlike companies backed by venture capital, neutral data stewards lack business models that generate sufficient income from paying customers to cover their operating costs. As a result, public/private partnerships, including government support, may be essential for these data stewards to thrive long-term.
- Data Fragmentation and Interoperability
Labor rights data remains siloed across organizations, with varying taxonomies and technical infrastructures. Efforts like OSH’s have made strides in standardizing data, but greater coordination across platforms is needed.
- Aligning Open Data with Policy and Corporate Accountability
New regulations, such as the Corporate Sustainability Due Diligence Directive, present an opportunity to push for mandatory labor rights disclosures. HU’s recent grant to Wikirate aims to explore whether increased corporate disclosure translates into meaningful behavior change or is simply a compliance checkbox.
- Worker-Centered Data Models
One of the biggest gaps is ensuring workers benefit from their own data. Emerging approaches, such as data co-ops and participatory data models, offer promising solutions but remain in their infancy. There is an urgent need to develop frameworks that empower workers to govern, access, and benefit from labor data.
Areas for Continued Learning
- Strengthening worker-driven data stewardship. How can unions and worker groups play a bigger role in collecting, managing, and leveraging supply chain data?
- Developing participatory data models. Can worker-owned data cooperatives shift power dynamics and ensure that data serves those most impacted by labor rights abuses?
- Expanding the role of integrated data in corporate accountability. How can better supply chain labor data lead to stronger corporate remediation mechanisms and policy enforcement?
For these efforts to be truly transformative, key players across sectors have much to gain by investing in stronger, interoperable labor data systems. Businesses that adopt open, standardized labor data models can move beyond reactive compliance toward risk mitigation, smarter supplier selection, and streamlined social compliance processes – ultimately positioning themselves to integrate labor and environmental data more efficiently and reduce costs over time. Rather than treating data ownership as a competitive advantage, data holders that leverage insights collaboratively can unlock new business value and market leadership Technology developers that build tools aligned with open data initiatives can expand their client base and help drive industry standards. Governments that fund public data infrastructure and mandate meaningful corporate disclosures can strengthen labor markets and support broader sustainability goals. And when unions and grassroots worker organizations are involved from the outset, they can ensure these systems are grounded in worker realities – advocating for worker-owned data models, using insights to organize more effectively. Together, these actions point toward a win-win future: one where transparency, accountability, and shared value reinforce one another.
