The increase in the amount of data generated in different forms and across different domains has presented new opportunities to make inferences on social trends for policy decision making. We propose a framework for governmental, data-driven applications design to guide the problem of using big data within policymaking. A cross-disciplinary literature review was performed to analyse the full extent of this problem. Our literature review is based on a smart model to identify the prevalent themes in the literature, and thereby contribute to a more comprehensive framework.
The seminar will present the motivation of this work, discuss the potential of big data in policymaking, and the context in which this work was performed. A small live demo of the learning model used for the literature review will be presented, along with the tools and methodology. Next, the framework will be presented as well as our findings from an analytical validation using e-Oman’s data hub as a case.
Ali Al-Lawati is currently a Government Fellow at the United Nations University Operating Unit on Policy-Driven Electronic Governance (UNU-EGOV). He is also a technology professional with private and government experience. He has a broad experience in helping organisations work better through the use of technology to automate work. Ali spent several years consulting with international financial entities in the United States to automate workflows and implement document and business management systems.
In 2011, Ali was recruited by the Information Technology Authority of Oman. Ali participated and led many successful projects, including key government projects such as the G-Cloud and the Integration platform which helped establish a framework for the exchange of millions of data units across government entities.
Ali has past research experience in the usage and linking of data while maintaining their privacy. He is currently working as a Government Fellow with the Smart City Unit on issues related to using government data to help government officials make more informed decisions. His research interests include machine learning and privacy-aware data analysis.
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