The previous definition raises awareness on some constraints that should be considered. Any analytical process is only as good as the quality of data available for analysis. As such, it is important to ensure that datasets used for analysis are cleaned and parsed to present a concise, valid, and clear picture of the datasets being used. Moreover, trusted and reliable analytics and artificial intelligence — which are key ingredients for transparent, evidence-based policymaking— require findable, accessible, interoperable, secure and high-quality data. In fact, regarding collection of large amount of data and the application of such systems, some challenging issues should be considered. (Maissenhaelter, Woolmore, & Schlag, 2018) Therefore, latter in July 2020, parallel to the following necessity raised by the COVID-19 pandemic, it was released a strategy for data governance and data policies at the European Commission (Comission, 2020), focusing on processes thar endure 
(1) Data Governance and management; 
(2) Protection and information security; (3) Data Quality; (4) Interoperability and standards.

Zuletzt geändert: Freitag, 30. September 2022, 09:20