Data quality assessment in context: A cognitive perspective

a r t i c l e i n f o In organizations today, the risk of poor information quality is becoming increasingly high as larger and more complex information resources are being collected and managed. To mitigate this risk, decision makers assess the quality of the information provided by their IS systems in order to make effective decisions based on it. To do so, they may rely on quality metadata: objective quality measurements tagged by data managers onto the information used by decision makers. Decision makers may also gauge information quality on their own, subjectively and contextually assessing the usefulness of the information for solving the specific task at hand. Although information quality has been defined as fitness for use, models of information quality assessment have thus far tended to ignore the impact of contextual quality on information use and decision outcomes. Contextual assessments can be as important as objective quality indicators because they can affect which information gets used for decision making tasks. This research offers a theoretical model for understanding users' contextual information quality assessment processes. The model is grounded in dual-process theories of human cognition, which enable simultaneous evaluation of both objective and contextual information quality attributes. Findings of an exploratory laboratory experiment suggest that the theoretical model provides an avenue for understanding contextual aspects of information quality assessment in concert with objective ones. The model offers guidance for the design of information environments that can improve performance by integrating both objective and subjective aspect of users' quality assessments. Organizational data is a critical resource that supports business processes and managerial decision making. Advances in information technology have enabled organizations to collect and store more data than ever before. This data is processed in a variety of different and complex ways to generate information that serves as input to organizational decision tasks. As data volumes increase, so does the complexity of managing it and the risks of poor data quality. Poor quality data can be detrimental to system usability and hinder operational performance, leading to flawed decisions [27]. It can also damage organizational reputation, heighten risk exposure, and cause significant capital losses [28]. While international figures are difficult to determine, data quality problems currently cost U.S. businesses over $600 billion annually [1]. Data quality is hence an important area of concern to both practitioners and researchers. Data quality researchers have used the terms " data quality …