Recent studies have indicated that companies are increasingly experiencing Data Quality related problems as more and more complex data are being collected. In order to address such problems, literature suggests the implementation of a Total Data Quality Management Program that should consist of the following phases: data quality deﬁnition, measurement, analysis and improvement. Data Quality is often deﬁned as “ﬁtness for use”. Although “ﬁtness for use” captures the essence of quality, it is diﬃcult to measure Data Quality using this broad deﬁnition. Thus, it has long been acknowledged that the quality of data is best described or analyzed via multiple attributes or dimensions.
Despite broad discussion in the Data Quality literature, there is no one deﬁnite set and exact deﬁnition of Data Quality dimensions because Data Quality is context dependent. Therefore, Data Quality dimensions should be identiﬁed and deﬁned in relation to tasks to achieve a suitable level of Data Quality.
Our research identiﬁes important Data Quality dimensions for evaluating the quality of the data for credit risk assessment. We also explore the key Data Quality challenges and causes of Data Quality problems in ﬁnancial institutions, based on statistical analysis.
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