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首页> 外文期刊>ACM journal of data and information quality >Validating Data Quality Actions in Scoring Processes
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Validating Data Quality Actions in Scoring Processes

机译：验证评分过程中的数据质量行动

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摘要

Data quality has gained momentum among organizations upon the realization that poor data quality might cause failures and/or inefficiencies, thus compromising business processes and application results. However, enterprises often adopt data quality assessment and improvement methods based on practical and empirical approaches without conducting a rigorous analysis of the data quality issues and outcome of the enacted data quality improvement practices. In particular, data quality management, especially the identification of the data quality dimensions to be monitored and improved, is performed by knowledge workers on the basis of their skills and experience. Control methods are therefore designed on the basis of expected and evident quality problems; thus, these methods may not be effective in dealing with unknown and/or unexpected problems. This article aims to provide a methodology, based on fault injection, for validating the data quality actions used by organizations. We show how it is possible to check whether the adopted techniques properly monitor the real issues that may damage business processes. At this stage, we focus on scoring processes, i.e., those in which the output represents the evaluation or ranking of a specific object. We show the effectiveness of our proposal by means of a case study in the financial risk management area.
机译：数据质量在实现方面取得了势头，以实现糟糕的数据质量可能导致失败和/或效率低下，从而损害业务流程和应用结果。但是，企业往往采用基于实际和经验方法的数据质量评估和改进方法，而不对颁布的数据质量改进实践的数据质量问题和结果进行严格分析。特别地，数据质量管理，特别是识别要监控和改进的数据质量维度，是通过知识工作者在技能和经验的基础上执行的。因此，控制方法是在预期和明显的质量问题的基础上设计的;因此，这些方法在处理未知和/或意外问题方面可能没有有效。本文旨在提供基于故障注入的方法，以验证组织使用的数据质量行动。我们展示了如何检查采用的技术是否正确监控可能损坏业务流程的实际问题。在此阶段，我们专注于评分过程，即输出代表特定对象的评估或排名的过程。我们通过在金融风险管理领域进行案例研究，展示了我们提案的有效性。

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