Abstract
The users, sensors, and networks of the Internet of Things (IoT) have been generating huge amounts of data for the past decade. Given the increasingly sophisticated models, computing power, and open software available to analyse this data, we would expect governments to have developed many useful applications to improve public service.
However, what we see in practice is that, despite heavy investments in these technologies, governments fail to meet these expectations, only making modest use of the possibilities data brings.
This paper provides a holistic framework of techno-rational and socio-political drivers at the macro-, meso-, and micro-level that explain success and failure of big data and AI in government.
The paper presents original conclusions on why data science in the public sector fails and provides practical recommendations for practitioners to consider in decision-making processes.
Overall, the paper suggests a modest approach to data science in the public sector that emphasizes adding value, with a focus on investing in basic data infrastructure and developing a shared organisational vision.
Speaker
Language
- English