Editing Public policy (section)

=== 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.<ref>{{Cite journal|url=https://issues.org/esty-2/|title=The Promise of Data-Driven Policymaking|journal=Issues in Science and Technology|last1=Esty|first1=Daniel|last2=Rushing|first2=Reece|volume=23|number=4|date=Summer 2007|language=en-US|access-date=2020-01-08|archive-date=2022-01-21|archive-url=https://web.archive.org/web/20220121083716/https://issues.org/esty-2/|url-status=live}}</ref> Data-driven policy making aims to make use of data and collaborate with citizens to co-create policy.<ref>{{Cite book|last1=van Veenstra|first1=Anne Fleur|date=2017|pages=100–111|publisher=Springer International Publishing|isbn=978-3-319-64321-2|last2=Kotterink|first2=Bas|title=Electronic Participation |chapter=Data-Driven Policy Making: The Policy Lab Approach |series=Lecture Notes in Computer Science|volume=10429|doi=10.1007/978-3-319-64322-9_9|chapter-url=https://hal.inria.fr/hal-01703333/file/453549_1_En_9_Chapter.pdf|access-date=2020-01-12|archive-date=2022-01-18|archive-url=https://web.archive.org/web/20220118182809/https://hal.inria.fr/hal-01703333/file/453549_1_En_9_Chapter.pdf|url-status=live}}</ref> Policy makers can now make use of new data sources and technological developments like [[Artificial intelligence in healthcare|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|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.<ref>{{Cite journal |last1=Maffei |first1=Stefano |last2=Leoni |first2=Francesco |last3=Villari |first3=Beatrice |date=2020-04-02 |title=Data-driven anticipatory governance. Emerging scenarios in data for policy practices |journal=Policy Design and Practice |volume=3 |issue=2 |pages=123–134 |doi=10.1080/25741292.2020.1763896|s2cid=219423835 |doi-access=free |hdl=11311/1140914 |hdl-access=free }}</ref> With new technologies implemented in government administration, a more complete visualization of current problems will emerge, allowing for more precision in targeted policy-making.<ref>{{Cite web |last1=Esty |first1=Daniel |last2=Rushing |first2=Reece |date=1970-01-01 |title=The Promise of Data-Driven Policymaking |url=https://issues.org/esty-2/ |access-date=2022-03-29 |website=Issues in Science and Technology |language=en-US |archive-date=2022-01-21 |archive-url=https://web.archive.org/web/20220121083716/https://issues.org/esty-2/ |url-status=live }}</ref> 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.<ref>{{Cite journal |date=2020 |title=Research in Data Science and Applied to Public Administration |url=https://www.fct.pt/media/docs/Brochura_ResearchinDataScienceandAIappliedtoPA.pdf |journal= |publisher=Fundação para a Ciência e a Tecnologi |access-date=2022-05-09 |archive-date=2022-03-19 |archive-url=https://web.archive.org/web/20220319223356/https://fct.pt/media/docs/Brochura_ResearchinDataScienceandAIappliedtoPA.pdf |url-status=live }}</ref>

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.<ref>{{Cite web |title=Covid-19: How unprecedented data sharing has led to faster-than-ever outbreak research {{!}} Research and Innovation |url=https://ec.europa.eu/research-and-innovation/en/horizon-magazine/covid-19-how-unprecedented-data-sharing-has-led-faster-ever-outbreak-research |access-date=2022-05-06 |website=ec.europa.eu |date=23 March 2020 |language=en |archive-date=2022-05-09 |archive-url=https://web.archive.org/web/20220509025339/https://ec.europa.eu/research-and-innovation/en/horizon-magazine/covid-19-how-unprecedented-data-sharing-has-led-faster-ever-outbreak-research |url-status=live }}</ref> 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.<ref>{{Cite journal |last1=Hasan |first1=A. |last2=Putri |first2=E. R. M. |last3=Susanto |first3=H. |last4=Nuraini |first4=N. |date=2021-01-20 |title=Data-driven modeling and forecasting of COVID-19 outbreak for public policy making |journal=ISA Transactions |volume=124 |pages=135–143 |language=en |doi=10.1016/j.isatra.2021.01.028 |issn=0019-0578 |pmc=7816594 |pmid=33487397}}</ref>