Management information systems: conceptual foundations, structure, and development (2nd ed.)
Management information systems: conceptual foundations, structure, and development (2nd ed.)
Data quality and due process in large interorganizational record systems
Communications of the ACM
On thin ice:micros and data integrity
Datamation
Implications of data quality for spreadsheet analysis
ACM SIGMIS Database
Assessing data reliability in an information system
Journal of Management Information Systems - Special Issue: Database Management
Optimal imputation of erroneous data: Categorical data, general edits
Operations Research
Integrity analysis: methods for automating data quality assurance
Information and Software Technology
Methodology for allocating resources for data quality enhancement
Communications of the ACM
Powers-of-ten information biases
MIS Quarterly
Information and Software Technology
Data quality: management and technology
Data quality: management and technology
Managing data quality in accounting information systems: a stochastic clearing system approach
Managing data quality in accounting information systems: a stochastic clearing system approach
Implications of errors in survey data: a Bayesian model
Management Science
The notion of data and its quality dimensions
Information Processing and Management: an International Journal
The impact of data accuracy on system learning
Journal of Management Information Systems - Special section: Research in integrating learning capabilities into information systems
Journal of Management Information Systems - Special section: Realizing value from information technology investment
Information Resources Management Journal
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Information systems provide data for business processes and decision making. There is strong evidence that data items stored in organizational databases have a significant rate of errors. If undetected in use, errors in data may significantly affect business outcomes. The question examined in this paper is the extent to which business professionals are able to evaluate the quality of data in the information systems they use and the impact of their evaluations on decision-making behavior. Models of error detection and error correction processes are developed. The validity of the models is then examined through an analysis of interviews with ten actuaries. The findings show that actuaries detect errors in data using three general methods and that actuaries consider feasibility and costs when deciding whether to correct data errors.