Closing Ceremony of the “Data Cleansing of the National Data Warehouse Datasets Using AI” Project

The Minister of Digital Economy and Entrepreneurship, Engineer Sami Smeirat, sponsored the closing ceremony of the " Data Cleansing of the National Data Warehouse Datasets Using AI – For Some Use Cases" project. The ceremony was attended by representatives from several ministries and government institutions, as well as project partners from the Japan International Cooperation Agency (JICA) and the implementing company, Eqra Tech.

In his speech at the ceremony, Minister Smeirat emphasized that the project represents the state's vision of adopting advanced technology as part of the digital transformation approach. He noted that cleaning government data using artificial intelligence technologies is a pivotal step toward building more efficient institutions and more accurate and transparent government services.

Smeirat explained that this project aligns with the directives of the National Council for Future Technology, chaired by the Prime Minister and overseen by His Royal Highness Crown Prince Al Hussein bin Abdullah II, and is part of the implementation of the National Artificial Intelligence Strategy and the Economic Modernization Vision. He pointed out that the project is not only a successful technical experiment, but also a strategic step in supporting data-driven decision-making and enhancing integration between government agencies, thus achieving added value for citizens. The project included two major cases of AI use in government data cleansing. The first involved processing data from over two million Tawjihi student records between 1985 and 2004. AI technologies were used to complete the linkage with national figures, after traditional methods had reached 92%.

The second case involved harmonizing data from over 2.5 million land records between the Ministry of Local Administration and the Department of Lands and Survey, achieving a 94.6% compatibility rate.

The project's organizers emphasized that these results will contribute to improving the quality of government data, reducing errors and duplication, and enabling institutions to provide services based on accurate analysis and up-to-date information.