A product perspective on total data quality management
Communications of the ACM
Estimating and improving the quality of information in a MIS
Communications of the ACM
Communications of the ACM - Supporting community and building social capital
Data Quality for the Information Age
Data Quality for the Information Age
Fundamentals of Data Warehouses
Fundamentals of Data Warehouses
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
The Impact of Data Quality Information on Decision Making: An Exploratory Analysis
IEEE Transactions on Knowledge and Data Engineering
Modeling Completeness versus Consistency Tradeoffs in Information Decision Contexts
IEEE Transactions on Knowledge and Data Engineering
The Impact of Experience and Time on the Use of Data Quality Information in Decision Making
Information Systems Research
Information and Management
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Supporting data quality management in decision-making
Decision Support Systems
Economics-Driven Data Management: An Application to the Design of Tabular Data Sets
IEEE Transactions on Knowledge and Data Engineering
Journal of Management Information Systems
Private Markets for Public Goods: Pricing Strategies of Online Database Vendors
Journal of Management Information Systems
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
The Effects and Interactions of Data Quality and Problem Complexity on Classification
Journal of Data and Information Quality (JDIQ)
A Mathematical Framework for Data Quality Management in Enterprise Systems
INFORMS Journal on Computing
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Quantitative assessment of data quality is critical for identifying the presence of data defects and the extent of the damage due to these defects. Quantitative assessment can help define realistic quality improvement targets, track progress, evaluate the impacts of different solutions, and prioritize improvement efforts accordingly. This study describes a methodology for quantitatively assessing both impartial and contextual data quality in large datasets. Impartial assessment measures the extent to which a dataset is defective, independent of the context in which that dataset is used. Contextual assessment, as defined in this study, measures the extent to which the presence of defects reduces a dataset’s utility, the benefits gained by using that dataset in a specific context. The dual assessment methodology is demonstrated in the context of Customer Relationship Management (CRM), using large data samples from real-world datasets. The results from comparing the two assessments offer important insights for directing quality maintenance efforts and prioritizing quality improvement solutions for this dataset. The study describes the steps and the computation involved in the dual-assessment methodology and discusses the implications for applying the methodology in other business contexts and data environments.