information:filtering
Data-driven policy

Data-driven policy is a policy designed by a government based on existing data, evidence, rational analysis and use of information technology to crystallize problems and highlight effective solutions. Data-driven policy making aims to make use of data and collaborate with citizens to co-create policy. Policy makers can now make use of new data sources and technological developments like Artificial Intelligence to gain new insights and make policy decisions which contribute to societal development.

In the 2020s, policymakers will use data for policies and public service design, while responding to citizen engagement demands. The Anticipatory Governance model is particularly important when considering the sheer amount of data available. In terms of using new technology to collect, analyze, and disseminate data, governments are only just beginning to utilize data science for policy implementation. With new technologies implemented in government administration, a more complete visualization of current problems will emerge, allowing for more precision in targeted policy-making. Data science involves the transformation, analysis, visualization, and presentation of data, and potentially improve the quality of life and society by providing a more informational environment for public debate and political decision-making. Some examples of utilizing data science in public policy making are resource optimization, improving current public services, and fraud and error mitigation.

Data sets rarely merge between government agencies or within agencies or countries' governments. This is beginning to change with the COVID-19 pandemic spreading globally in early 2020. Forecasting and creating data models to prevent the propagation of the virus has become a vital approach for policy makers in governments around the world.