The impact of poor data quality on the typical enterprise
Communications of the ACM
Improving data warehouse and business information quality: methods for reducing costs and increasing profits
Enterprise knowledge management: the data quality approach
Enterprise knowledge management: the data quality approach
Criticality of data quality as exemplified in two disasters
Information and Management
The Impact of Data Quality Information on Decision Making: An Exploratory Analysis
IEEE Transactions on Knowledge and Data Engineering
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Supporting data quality management in decision-making
Decision Support Systems
Utility-driven assessment of data quality
ACM SIGMIS Database
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update
Journal of Management Information Systems
Design and natural science research on information technology
Decision Support Systems
GIGO or not GIGO: The Accuracy of Multi-Criteria Satisficing Decisions
Journal of Data and Information Quality (JDIQ)
The Practitioner's Guide to Data Quality Improvement
The Practitioner's Guide to Data Quality Improvement
Design science in information systems research
MIS Quarterly
A new method to simulate the triangular distribution
Mathematical and Computer Modelling: An International Journal
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Information quality is one of the key determinants of information system success. When information quality is poor, it can cause a variety of risks in an organization. To manage resources for information quality improvement effectively, it is necessary to understand where, how, and how much information quality impacts an organization's ability to successfully deliver its objectives. So far, existing approaches have mostly focused on the measurement of information quality but not adequately on the impact that information quality causes. This paper presents a model to quantify the business impact that arises through poor information quality in an organization by using a risk based approach. It hence addresses the inherent uncertainty in the relationship between information quality and organizational impact. The model can help information managers to obtain quantitative figures which can be used to build reliable and convincing business cases for information quality improvement.