Speaker
Description
The progressing data landscape and the evolving requirements of the modern information ecosystems demand that National Statistical Organizations (NSOs) transition from rigid ""stove-pipe"" systems toward standardized, flexible and modular architectures. This business transformation is increasingly powered by Free and Open Source Software (FOSS), which provides the technical and cultural foundation for transparency, efficiency, and international collaboration. The recent adoption of seven fundamental statistical open source software guiding principles by the UNECE Conference of Official Statisticians (CES) provides an excellent starting point for further maturity growth.
Applied to the field of Generative AI (GenAI) and Machine Learning (ML) the creation of FOSS building blocks that externalize domain knowledge through standardized configuration parameters is still a challenge. Statistical standards can play important role but we need broader collaboration that goes beyond the community of Official Statistics. Innovation in this field is moving fast and re-use of the principles in this relatively new application area is profitable for all. By utilizing truly independent software modules that support core statistical process steps and leveraging semantic interoperability using open standards, NSOs can reuse and share data across various contexts. Moreover, statistical quality profits from standardized building blocks built by many together.
In this presentation, we explore a number of examples in this field, both existing solutions as well as potential additions to the landscape, with criteria for success in mind, such as favoring generic chainable building blocks and working in the open. We conclude with recommendations for leveraging FOSS architectures, open standards and the power of communities to ensure long-term organizational agility and methodological transparency.
