GASTAT Unveils Road To Riyadh Program Ahead Of 2026 UN World Data Forum In Riyadh

The General Authority for Statistics (GASTAT) has initiated the "Road to Riyadh" program. This initiative is part of the preparations for hosting the sixth United Nations World Data Forum in Riyadh from November 9 to 12, 2026. The program's first activities began alongside the Second Statistical Forum for Universities and Statistical Associations in Jeddah, coinciding with World Statistics Day.

The "Road to Riyadh" program aims to boost global recognition of Saudi Arabia's leadership in data and statistics. It highlights the Kingdom's efforts in digital transformation and developing a robust statistical ecosystem. These initiatives support achieving the Sustainable Development Goals and align with Saudi Vision 2030, which seeks a knowledge-based economy driven by statistical data.

The program fosters collaboration with experts, institutions, and statistical agencies worldwide. This collaboration facilitates knowledge exchange and strengthens international partnerships in data and statistical indicators. The activities include both in-person and virtual sessions, seminars, and workshops across various cities in Saudi Arabia.

These events will continue throughout the year until the Second Saudi Forum for Statistics on November 8. The sixth UN World Data Forum will follow from November 9 to 12, 2026. GASTAT has made the full program accessible on its official website for those interested in participating or learning more about these initiatives.

The "Road to Riyadh" program is part of GASTAT's ongoing efforts to enhance Saudi Arabia's standing in the statistical field. It builds on achievements that have contributed to the Kingdom's progress in various international indicators. These efforts demonstrate Saudi Arabia's commitment to leading advancements in data and statistics globally.

By hosting such significant events, Saudi Arabia aims to showcase its role as a pioneer in data-driven development. The focus is on creating a sustainable future through informed decision-making supported by comprehensive statistical analysis.

With inputs from SPA