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Data-driven governance: application assumptions to the State and public services of Big Data practices used by the electoral sector.

Verbiest, Alexis
(2020)

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VERBIEST_79941700_2020.pdf
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Details

Supervisors
Kolp, Manuel
Faculty
Louvain School of Management
Degree label
Master [120] en ingénieur de gestion
Abstract
As part of the Big Data revolution, this work consider the possibility of a Data-driven governance in Belgium, similarly to what is done in the electoral Big Data sector. The question that raised during this work is the following: is it possible to conceive a Belgian State that uses Big Data to improve its governance, as political parties and electoral market players do in order to acquire votes and generate profit? The presentation consisted of three parts. In the first part, we presented the context of the Big Data revolution. In the second part, we studied the impact of the use of Big Data during the last French election, where four trends were identified: personalization, predictability, optimization and objectivation. In the third part, we transposed these trends to the field of public governance and explored how the State could, thanks to Big Data, create value, both for itself and for its citizens in different sectors. The sectors studied are the following: education, prevention against tax and social fraud, road traffic and finally connected objects "secured by design". We tried to asses each sector in order to see wether it was appropriate (or not) to opt for Data-driven governance.