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Data Quality Assessment: A Case Study of PT JAS Using TDQM Framework

机译:数据质量评估:使用TDQM框架的PT JAS案例研究

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The success of a company in increasing profits and managing loss risk is largely determined by data. Good quality data can improve the quality of decision making at the top management level. PT JAS is a financial company that manages risk. Of course to support good risk management needs to be supported by good quality of data sources. The aim of this research is to identify data dimensions, analyze and measure the quality of data. So that, it can be used as a support for the company's strategy in managing risk and understanding the current conditions of data quality. In measuring this data quality, the writer uses a total data quality management (TDQM) framework. TDQM provides a common framework for facilitating understanding in data improv approach through data quality management. The steps taken in measuring data quality are identification of data to be measured, determining the dimensions of data quality used, measurement of data quality, and then analyze the measurement results. From the measurement results, obtained factors that cause data quality problems, for example the pattern of imports of debtor data in large numbers. In addition, there are no policies related to data quality management and from some validation function errors in the system. Therefore, by measuring the quality of data and analyzing the causes, companies can find out in advance the condition of the quality of company data and immediately formulate strategies and steps in order to improve and develop the quality of the data they have, so that data can be a useful and valuable asset.
机译:公司在增加利润和管理损失风险方面的成功在很大程度上取决于数据。高质量的数据可以提高最高管理层的决策质量。 PT JAS是一家管理风险的金融公司。当然,要支持良好的风险管理,就需要高质量的数据源来支持。这项研究的目的是确定数据维度,分析和衡量数据质量。因此,它可以用作公司管理风险和了解数据质量当前状况的策略的支持。在测量此数据质量时,编写者使用了总体数据质量管理(TDQM)框架。 TDQM提供了一个通用框架,以通过数据质量管理促进对数据即兴方法的理解。测量数据质量所采取的步骤是识别要测量的数据,确定使用的数据质量的维度,测量数据质量,然后分析测量结果。从测量结果中,得出导致数据质量问题的因素,例如大量债务人数据的导入模式。此外,系统中没有与数据质量管理和某些验证功能错误相关的策略。因此,通过测量数据质量并分析原因,公司可以提前发现公司数据质量的状况,并立即制定策略和步骤,以改善和发展他们拥有的数据质量,从而使数据可以是有用和有价值的资产。

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