Dual assessment of data quality in customer databases

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 datasets 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. © 2009 ACM.