Harnessing Data and Intelligence for Collective Advantage 2026

Federated data architecture: Analysis without centralization FIGURE 3 Other toolsFederated collective dataParticipant n domain NGO case data and reports Data services and toolsData services and tools Data services and toolsParticipant 2 domain Participant 1 domainGovernment data Supply chain data n = any number of participants Note: The diagram illustrates the overall generalized infrastructure and its space for application innovation, while also showing the scope through the three universes of data, the heterogeneous nature of data within each universe, the presence of both shared and sector-specific tools and services, and the core principle of federated data that connects them. Source: Hewlett Packard Enterprise How it works –Federated technical architecture: Core applications and agreed protocols allow stakeholders to connect, share and collaborate on data or services without giving up autonomy or control. Instead of all or any data flowing into a single database, each participant maintains ownership of its data and systems while connecting through shared standards, interfaces and governance rules. –AI integration: AI functions as the intelligence layer of the federated network, connecting distributed datasets through shared standards and multilingual analysis. Rather than operating as a separate tool, AI is embedded throughout the system, linking information, identifying patterns and turning diverse data into shared insight while upholding privacy, transparency and accountability. –Retrieval-augmented generation (RAG): Enables large language models to generate more accurate and context-relevant responses by retrieving verified information from external knowledge sources before generating output. This approach ensures that responses reflect up-to-date and domain-specific context without requiring underlying data to be centralized or included in the model’s original training set.25 –Privacy-preserving technology: A suite of technologies enables participants to meet data protection and confidentiality requirements while collaborating safely. Examples include differential privacy, a form of anonymization, and federated learning, where AI models, not raw data, are shared to protect sensitive information. –Metadata standards: Global and regional frameworks provide a common understanding of insights across datasets. These standards improve data quality and interoperability and allow integration with AI and search technologies. –Multilingual agentic search: The architecture supports the interpretation of different languages while respecting context-specific terminology, resulting in increasing efficiency and accuracy. Harnessing Data and Intelligence for Collective Advantage: Ending Forced Labour in Global Supply Chains 14