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GASTAT announces the winning teams in the Data Innovation Hackathon

15-04-2026

The General Authority for Statistics (GASTAT) announced the winning teams of the Data Innovation Hackathon, organized from February 15 to April 15, 2026. The hackathon is the first of its kind in leveraging innovation and technology to advance statistical work, as part of “Road to Riyadh” series of events in preparation for Saudi Arabia’s hosting of the 6th UNWDF in November 2026.
In addition, GASTAT revealed that the hackathon comprised two main tracks, in which 132 teams competed. A total of 16 teams qualified for the final stage, with eight teams in the “Innovative Data Collection” track focusing on the use of non-traditional sources and eight teams in the “Intelligent Processing and Classification” track, leveraging artificial intelligence technologies. The final judging phase resulted in the selection of six winning teams that presented innovative solutions aimed at enhancing the quality of official statistics and enhancing their reliability.
In the same context, GASTAT will hold the closing ceremony on Monday, April 20, at its headquarters, where the detailed results of the hackathon will be announced. The event will showcase the winning projects and highlight the most prominent innovative solutions presented, in addition to honoring the winning teams in recognition of their efforts and contributions to advancing statistical work and enhancing data quality.
It is noteworthy that the Data Innovation Hackathon reflects GASTAT’s commitment to expanding the use of modern technologies and enhancing the role of artificial intelligence in developing statistical processes, in line with the objectives of Saudi Vision 2030, which aims to build a knowledge-based economy driven by data. It also highlights the progress of the statistical sector in Saudi Arabia and strengthens its position as a leading regional hub in data and innovation while enabling national and international talent, including students, academics, and professionals to participate in developing practical solutions to statistical challenges.
 

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