Case Study: Secure Data Collaboration Across a Global Consortium
insightfactory.ai enabled a consortium of global competitors in the same industry to securely share anonymised, conformed performance data using agent-based federation, unlocking shared insights while preserving full data sovereignty and anonymity.
insightfactory.ai enabled a consortium of global companies to securely share anonymised, aggregated performance data using a federated architecture — allowing competitors to collaborate without compromising data sovereignty, security, or anonymity.
The Problem
A consortium of multinational companies operating across more than 100 countries set out to create a shared, aggregated view of industry performance, drawing on common data sets to generate insights that would benefit all participants. However, despite their shared interest, the companies faced a series of critical challenges. As direct competitors, any solution had to guarantee strict data anonymity and segregation, ensuring that no sensitive or identifying information could be exposed.
Complicating matters further, each organisation maintained its own distinct data models and systems for capturing the same data sets, making interoperability difficult. To deliver value, the consortium needed a conformed and consistent data structure that could bring together disparate sources without sacrificing accuracy or fidelity. The platform also needed to be flexible enough to support dynamic onboarding and offboarding of members over time, while still maintaining stability at scale.
Balancing these requirements meant that achieving trust, scalability, and interoperability across such a competitive and diverse landscape would demand a solution that was not only technically sophisticated but also highly secure and flexible by design.
The Solution
The consortium engaged insightfactory.ai to design and deliver a federated data-sharing solution, powered by the Insight Factory platform. This approach allowed participants to collaborate on shared insights while maintaining strict privacy and competitive boundaries.
The solution began with the deployment of Insight Factory agents within each consortium member’s secure environment. These agents were tailored to the unique system architecture of each organisation, developing conformed data models that standardised inputs at the source. On-site enrichment and transformation ensured data consistency before it ever left a member’s environment, reducing the risk of errors and strengthening trust.
insightfactory.ai then managed the centralised aggregation and anonymisation process, combining the cleansed and standardised data into a shared, industry-wide view. Crucially, sensitive and identifying information was stripped out, protecting each member’s confidentiality. The final enriched, anonymised data products were securely returned to participants, giving each organisation access to a robust, aggregated benchmark without exposing proprietary details.
To ensure long-term adaptability, a flexible governance structure was also embedded. The result was a secure, collaborative analytics ecosystem that delivered shared value without compromising data integrity or competitive boundaries.
The Value Delivered
The solution enabled the consortium to collaborate effectively across direct competitors without ever compromising sensitive business data. By maintaining data sovereignty — with all enrichment and processing performed locally within each member’s secure environment — organisations were able to participate with confidence, knowing their proprietary information remained protected.
Through the aggregated, anonymised data sets, the consortium generated industry-wide insights that reflected shared standards and practices, providing a benchmark that had previously been out of reach. The model also accelerated the delivery of insights, streamlining what had been a complex process while preserving the strict governance needed to sustain trust.
Perhaps most importantly, the federated framework established by insightfactory.ai created a scalable foundation for future cross-industry collaboration programs. It demonstrated that even in highly sensitive and competitive ecosystems, secure and governed data sharing is not only possible but capable of delivering significant value at scale.
