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Measurement of Export Data Quality Using Task-Based Data Quality (TBDQ): Case Study of the Directorate General of Customs and Excise

机译:使用基于任务的数据质量(TBDQ)的导出数据质量的测量:习俗和消费委员会的案例研究

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The Indonesian Directorate General of Customs and Excise (DGCE), as the regulator of export policies in Indonesia, is required to have good quality export data. However, in its management, export data were reported to have persistent problems based on reports of export evaluations and the results of data cleansing with relevant stakeholders. These problems should be addressed immediately because export data is vital for Indonesia, therefore it is necessary to measure the quality of export data at the Indonesian DGCE. One of the benefits of measuring export data is to investigate existing data problems, hence it is easier to set a number of improvements. This study uses the Task-Based Data Quality (TBDQ) framework because it matches the characteristics of the export information system. The dimensions used in this study are Completeness, Accuracy, Timeliness, and Consistency determined from interviews with experts. The results showed that the quality of export data is not yet optimal with the highest anomaly percentage of 1.4494%. Improvements of the anomalous data are made based on the Improving Task by the weighting calculation, that has been done using the Analytic Hierarchy Process (AHP) so that the problems of data accuracy and differences in the results of data cleansing can be eliminated.
机译:印度尼西亚海关总局(DGCE)作为印度尼西亚出口政策监管机构,需要具有良好的质量出口数据。但是,在其管理中,据报道,出口数据基于出口评估的报告以及与相关利益攸关方进行的数据清理结果进行持续存在问题。应立即解决这些问题,因为导出数据对于印度尼西亚至关重要,因此必须测量印度尼西亚DGCE的出口数据的质量。测量出口数据的一个好处是调查现有数据问题,因此可以更容易设置多种改进。本研究使用基于任务的数据质量(TBDQ)框架,因为它与导出信息系统的特性匹配。本研究中使用的尺寸是与专家采访中确定的完整性,准确性,及时性和一致性。结果表明,出口数据质量尚未最佳,最高异常百分比为1.4494%。通过对加权计算的改进来改进异常数据,这已经使用分析层次处理(AHP)来完成,使得可以消除数据清洁结果的数据准确性和差异的问题。

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