AI techniques introduce 'data fetchers' and 'research assistants' that allow for easy discovery, retrieval, and sharing of relevant data through natural language interactions.
Initial intuitions for the 2025-2030 strategy
The community is in the early stages of developing its new strategy for 2025-2030 cycle. This process will benefit from extensive engagement by leveraging the diverse perspectives and expertise of our members, partners, and the broader community, enhancing our strategic vision and incorporating practical insights.
The information shared below provides a few preliminary insights (concepts) gained from the past months, particularly through Community workshops and wider fora, which have highlighted key priorities such as the concept of “making our data AI ready and enabling the data mesh”.
The Community will undertake a collaborative strategy formulation process over the coming months, aiming for approval by the Community Strategic Level Group (SLG) in June 2025.
We believe these strategic concepts represent a holistic and forward-thinking approach to evolving the statistical data landscape, with a keen eye on the future and a commitment to innovation, collaboration, and excellence.
1
ConceptNo code data pipeline automation
A strong emphasis on being metadata-driven, recognising the crucial role metadata plays not just in the description of data, but in powering automation, enhancing data discoverability, and ensuring consistency across diverse data sets. This should lead to harnessing advanced metadata management practices to enable seamless interaction between datasets, making metadata a cornerstone for the development of smart, self-describing data systems. This effort will ensure that metadata is not just an adjunct to data but a dynamic layer that actively enhances the value, utility, and interoperability of statistical information systems. Embracing a metadata-driven approach will facilitate more intelligent data processing pipelines, improved data quality, and a richer, more user-friendly data consumption experience across the board.
2
ConceptMaking our data AI ready and enabling the data mesh
Anticipating the future, the SIS-CC aims to make its data solutions AI-ready, laying the groundwork for a robust data mesh architecture. This involves preparing data in ways that are consumable not just by traditional data processing applications but also by AI and machine learning models, ensuring that data is accessible, interoperable, and meaningful. By strategically structuring data, and by fostering a culture where data is seen as a product, supporting a more efficient and well-defined, decentralised approach to data management. This data mesh framework will empower various domains within the statistical organisations to own and share their data products, effectively democratising data and making it easier for AI technologies to leverage vast amounts of data for insights, thus driving innovation and value creation across the statistical information systems.
3
ConceptEstablish a systematic User Research
Understanding and meeting the evolving needs of users will be paramount in the next phase of SIS-CC’s strategy. Building on the creation of the User Research Task Force (UTF) to establish a systematic user research framework to tap directly into the feedback and insights from its global community. This effort will involve developing a structured, continuous process for gathering, analysing, and acting on user feedback to refine and enhance the suite of tools offered by SIS-CC. The focus will be on enhancing user experience, ensuring the relevancy and usability of statistical data products, and fostering an environment of co-innovation where users actively contribute to the development roadmap. This systematic feedback loop will ensure that SIS-CC remains agile and responsive to the needs of its diverse user base, ultimately driving wider adoption and deeper engagement with its platforms.
4
ConceptBlazing fast (High-performing SDMX web services)
In an era where data volumes continue to explode and the demand for real-time insights grows, SIS-CC commits to delivering blazing fast, high-performing SDMX web services. The goal is to significantly reduce data retrieval times, enhance the responsiveness of data queries, and ensure that users can access and interact with data with minimal latency. Leveraging the latest technologies, optimised algorithms, and scalable infrastructure, SIS-CC aims to set new standards for performance in the official statistics community. This commitment to high performance is not just about speed but also about ensuring reliability and availability of data services at scale, providing a superior experience for users and supporting more agile, data-driven decision-making processes among its global community.
