Building an Effective Supply Chain Data Ecosystem to Prevent Forced Labor

New laws will require a more integrated and reliable data and technology ecosystem to prioritize action against forced labor in global supply chains. BSR’s Collaborative Initiative Tech Against Trafficking (TAT) has recently issued seven principled recommendations for an effective supply chain data ecosystem, where data is shared at greater scale and for greater impact.

Foto: Photo by Smileus on iStock



Claudio Formisano, Alison Berthet, Lale Tekişalp, Jiajia Chen, and Laura Dugardin, BSR

Modern slavery is on the rise. Over 27 million people are estimated to be in situations of forced labor on any given day, and 86 percent of these cases occur in the private sector. Forced labor is widespread across global supply chains. The push from regulators worldwide to address this issue has formed a complex web between policymakers, enforcement authorities, businesses, third-party solution providers and civil society organizations either collecting, compiling or processing risk management data on labor rights and violations. 

Yet, despite the considerable amount of disparate data collected to highlight forced labor risks, its full impact and value remain unrealized. Significant data siloes persist between—and within—the corporate sector, civil society and the public sector. Reasons range from legal limitations (e.g., data protection laws or contractual restrictions) and technical barriers (e.g., lack of digital infrastructure) to behavioral challenges (e.g., commercial incentives and lack of trust between actors).

Tech Against Trafficking has recently launched seven principled recommendations to business, policy-makers and civil society to enable an effective supply chain data ecosystem where data is shared at greater scale and for greater impact. 

“Tech Against Trafficking’s goal is to contribute to shaping an environment where all actors—not just business—engaged with supply chains can focus on anti-slavery interventions, rather than data acquisition.”

Claudio Formisano, Global Lead, Forced Labor and Human Trafficking, BSR

The Importance of Data Sharing 

If purposefully done, data sharing can amplify the impact of anti-slavery policies and reduce the costs and duplication of data collection for business and other key actors. For example, evidence of forced labor at a factory will be of value not just to the factory owner, but also to its buyers seeking to understand risks linked to their products and to local authorities and civil society organizations working to address potentially systemic issues in the area. 

Effective data sharing also allows companies to resource more time and financials to take action, rather than constantly processing data. It can help governments target effective policies and enforcement, and civil society support greater insights into the prevalence of modern slavery across regions.

Enabling data to inform the actions of multiple actors to achieve the broadest potential impacts is also an important way of giving due credit and respect to the original data subject, and reduce the burden on victims having to re-tell their stories multiple times. However, it is critical that any data sharing is carried out in a way that protects the privacy and security of the affected rightsholder. 

“When it comes to promoting data sharing, perhaps it’s not about how can we convince self-interested parties to pool their data in a centralized place, but how can we give these parties the tools by which they establish the shared interests that compel them to share.”

Darren Edge, Senior Director, Microsoft Research Special Projects

Promising Technology 

TAT’s research also considers the potential for emerging technologies to facilitate data exchange, and highlights real-world examples of effective data sharing by identifying common success factors that may be replicated or scaled in other parts of the supply chain data ecosystem.

These include mechanisms that can preserve the confidentiality of buyer-supplier relationships, avoid auditing duplication, maintain the confidentiality of the source of information and identity of concerned individuals. 

Underlying factors which underpin the success of deploying technology solutions are the creation of a sense of community bound by shared values and objectives, collective ownership and governance of the data sharing process, the role of a trusted and expert intermediary and a user-friendly interface to upload or access shared data.

Recommendations for businesses, policymakers, and civil society

To achieve greater data interoperability and sharing, the report makes detailed recommendations to businesses, policymakers, and civil society to ensure the field can collect the right data, deploy the right resources (and do so equitably), and adopt the right behaviors to build trust between actors in the supply chain data ecosystem, including: 

1. Standardize Data Collection for Greater Interoperability. All actors that collect data related to instances of forced labor should seek greater alignment and harmonization in the way such data is collected.

2. Focus on Progress and Impact, Not Just Risk. The focus on risk too often leads businesses and solution providers to conflate the evaluation of risks of forced labor (and its adverse impacts on affected workers’ human rights) with risks to the business that would result from a connection to forced labor. 

3. Invest in Data Management. It is a precondition to an effective data ecosystem that data is “fit” for sharing. This starts with determining what valuable data your organization holds, for its use, and potential use by others in the supply chain data ecosystem. 

4. Share Costs of the Data Ecosystem Equitably. Collecting and sharing data has human, technical, and financial costs, which well-resourced companies and government agencies have more power to do, compared to smaller NGOs or companies, exploited workers and consumers. 

“I love the piece about the direction of risk [i.e. recommendation to focus on progress, not just risk], I don’t see a lot of business do that.”

Terri Johnson, Head of Human Rights Due Diligence Development, Hitachi Europe

In the coming months, Tech Against Trafficking will focus on implementing action to contribute to the creation of an effective federated data ecosystem over the long term, through three principal outputs in line with the recommendations above:

1. Standardizing effective qualitative and quantitative datapoints to identify forced labor risk (building on the ILO’s forced labor indicators).

2. Designing a cost effective, accessible, and scalable model for a federated data ecosystem.

3. Enhancing public-private sector dialogue to inform effective policies and regulations, and initiatives to develop public databases on forced labor.

We call on businesses, civil society and governments to join our efforts to overcome the fragmentation and siloes that exist across the supply chain data ecosystem and share data more purposefully and effectively

If you are interested in getting involved, please get in touch at

With thanks to all those individuals and organization who contributed to this research, who participated in our launch event in London on 18 March 2024 (recording available here).



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