Despite the substantial body of evidence detailing the multifaceted use of data within organizations, the conceptualizations of data and their value propositions remain disjointed and require updating. Information Systems scholars contend that the traditional ways of conceiving data now appear inadequate in framing this ever-evolving data-driven phenomenon. In this context, we argue for a reassessment of the fundamental assumptions about data in the field. This paper offers a comprehensive literature review, through which we conceptualize the role of data into four distinguishable types: data as a tool, as a commodity, as a practice, and as algorithmic intelligence. Each type possesses a set of identifiable characteristics, usage, and unique pathways of value creation. Together these elements form a typology, which provides an explanation for the intricate and complex nature of data use in organizations and the diverse sources of their value.

Governments are increasingly relying on algorithmic decision-making (ADM) to deliver public services. Recent information systems literature has raised concerns regarding ADM’s negative unintended consequences, such as widespread discrimination, which in extreme cases can be destructive to society. The extant empirical literature, however, has not sufficiently examined the destructive effects of governmental ADM. In this paper, we report on a case study of the Australian government’s “Robodebt” programme that was designed to automatically calculate and collect welfare overpayment debts from citizens but ended up causing severe distress to citizens and welfare agency staff. Employing perspectives from systems thinking and organisational limits, we develop a research model that explains how a socially destructive government ADM programme was initiated, sustained, and delegitimized. The model offers a set of generalisable mechanisms that can benefit investigations of ADM’s consequences. Our findings contribute to the literature of unintended consequences of ADM and demonstrate to practitioners the importance of setting up robust governance infrastructures for ADM programmes.

Abstract The appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.