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.

How classes of things and properties in general should be represented in conceptual models is a fundamental issue. For example, proponents of object-role modelling argue that no distinction should be made between the two constructs, whereas proponents of entity-relationship modelling argue the distinction is important but provide ambiguous guidelines about how the distinction should be made. In this paper, the authors use ontological theory and cognition theory to provide guidelines about how classification should be represented in conceptual models. The authors experimented to test whether clearly distinguishing between classes of things and properties in general enabled users of conceptual models to better understand a domain. They describe a cognitive processing study that examined whether clearly distinguishing between classes of things and properties in general impacts the cognitive behaviours of the users. The results support the use of ontologically sound representations of classes of things and properties in conceptual modelling.