The Giant Leap for Smart Cities: Scaling Up Smart City Artificial Intelligence of Things (AIoT) Initiatives

Despite the promise of AI and IoT, the efforts of many organizations at scaling smart city initiatives fall short. Organizations often start by exploring the potential with a proof-of-concept and a pilot project, with the process later grinding to a halt for various reasons. Pilot purgatory, in which organizations invest in small-scale...

Trusted decision-making: Data governance for creating trust in data science decision outcomes

Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to...

Future government data strategies: Data-driven enterprise or data steward?: Exploring definitions and challenges for the government as data enterprise

Comparable to the concept of a data(-driven) enterprise, the concept of a ggovernment as data (-driven) enterprise' is gaining popularity as a data strategy. However, what it implies is unclear. The objective of this paper is to clarify the concept of the government as data (-driven) enterprise, and identify the challenges and drivers that...

Data governance: Organizing data for trustworthy Artificial Intelligence

The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their...

Data Governance as Success Factor for Data Science

More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures...