Organisational learning and prerequisites of data-driven risk-based regulation

Organisational learning and prerequisites of data-driven risk-based regulation: A DDRBR framework

in 't Veld, Mark (TU Delft Technology, Policy and Management)

van der Voort, H.G. (mentor)
Cunningham, S. (graduation committee)
Farahmand, Majid (mentor)

Delft University of Technology

Engineering and Policy Analysis

2019-07-15

Over the course of the last decade the role of data in society, businesses and governments has changed tremendously. New technologies continue to influence the way data is collected, managed and used in organisations public and private alike. For public organisations and governments, the promise of data lies in data-enhanced decision-making. Data-enhanced decision-making gives governments and governmental agencies the possibility to improve by increasing their efficiency and effectiveness. The Dutch Authority for Nuclear Safety and Radiation Protection (ANVS: Autoriteit Nucleaire Veiligheid en Stralingsbescherming) is the regulatory agency responsible for the regulation of nuclear safety and security, and the regulation of all uses of ionising radiation in the Netherlands. In order to perform their primary task, regulation, the ANVS executes the proactive inspection process by utilising risk-based regulation. The ANVS is a future oriented organisation and subsequently employs a strategy of continuous improvement. The ANVS have noticed the increasing use of data science in the public domain, and subsequently want to improve their regulatory framework via data-driven tools. These data-driven tools could supplement the existing regulatory framework by creating a data-driven risk-based regulation (DDRBR) framework.

data-driven risk-based regulation
data-driven inspections
prerequisites
Design Science Research

http://resolver.tudelft.nl/uuid:211a077b-f288-4e68-b07e-5b0c7c2f3cb6

2019-08-01

Student theses

master thesis

© 2019 Mark in 't Veld