A Semiotic Information Quality Framework: Applications

Recent theoretical developments in information quality research have focused on defining, understanding and assessing data quality. The frameworks are intended to further theoretical knowledge as well as inform practice. Prior experimental work on the impact of data quality upon management decision-making is not based explicitly on theoretically-grounded data quality frameworks. As a result, research on data quality tagging – providing information to individual decision-makers about the quality of data elements – has not addressed the form of the tags themselves, their rationale, semantics, nor how such tags are to be derived. Additionally, there is a paucity of experimental work based on such frameworks that examines how data quality impacts upon value-creation within organisations. To address these concerns, this paper presents one such framework (the Semiotic Information Quality Framework) and describes two distinct proposed empirical studies addressing the impact on decisions. The first examines how the form of data quality tags effects individual decision-makers, while the second explores the value of improving data quality in large-scale automated decision processes.