A polygon model for heterogeneous database systems: the source tagging perspective
Proceedings of the sixteenth international conference on Very large databases
Data Quality for the Information Age
Data Quality for the Information Age
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Proceedings of the 2nd international workshop on Information quality in information systems
Utility-driven assessment of data quality
ACM SIGMIS Database
An approach for incorporating quality-based cost---benefit analysis in data warehouse design
Information Systems Frontiers
Overview and Framework for Data and Information Quality Research
Journal of Data and Information Quality (JDIQ)
Methodologies for data quality assessment and improvement
ACM Computing Surveys (CSUR)
Data Quality Tags and Decision-making: Improving the Design and Validity of Experimental Studies
Proceedings of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges
Data quality assessment in context: A cognitive perspective
Decision Support Systems
Dual Assessment of Data Quality in Customer Databases
Journal of Data and Information Quality (JDIQ)
Integrating data quality data into decision-making process: an information visualization approach
Proceedings of the 2007 conference on Human interface: Part I
Evaluating a model for cost-effective data quality management in a real-world CRM setting
Decision Support Systems
A multidimensional analysis of data quality for credit risk management: New insights and challenges
Information and Management
A risk based model for quantifying the impact of information quality
Computers in Industry
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This paper describes an experiment that explores the consequences of providing information regarding the quality of data used in decision making. The subjects in the study were given three types of information about the data's quality: none, two-point ordinal, and interval scale. This information was made available to the subjects, along with the actual data. Two decision strategies were explored: conjunctive and weighted linear additive. Two decision environments were used: a simple environment and a relatively complex environment. Various combinations of these factors were employed to explore several issues. These include complacency, consensus, and consistency. The paper provides preliminary insights into which type of data-quality information is most effective and the circumstances in which data-quality information is most effective. Such knowledge would be of value to those responsible for designing databases that support decision-makers. Overall, we find that in a situation where subjects are confronted with clearly differentiated alternatives, the inclusion of data-quality information impacted the selection of the preferred alternative while maintaining group consensus.