A Blueprint for Trustworthy Data Ecosystems
A new systematic review provides a critical roadmap for building data platforms that operate across multiple organizations. As data becomes a central asset in digital economies, the challenge of securely storing, analyzing, and sharing information between distinct entities has grown. The research synthesizes existing literature to identify five key architectural solutions for these multi-organizational data platforms. By examining core technological building blocks and validating findings with practical use cases, the study culminates in a comprehensive, adaptable reference architecture designed to foster secure and trustworthy data collaboration.
Why it might matter to you: For data scientists and engineers working with complex, distributed data sources, this reference architecture offers a validated framework for designing robust ETL pipelines and data lakes. It directly addresses the practical challenges of data governance, privacy, and secure sharing that are central to modern data engineering and MLOps workflows. Implementing such architectures can enhance the reproducibility and ethical management of data in multi-stakeholder projects, from cloud computing environments to federated learning initiatives.
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