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Antecedents of big data quality: An empirical examination in financial service organizations

机译:大数据质量的先决条件:金融服务机构的经验检验

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Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of big data quality in financial institutions. This will help to understand how data quality from big data analysis can be improved. For this, a literature review was performed and data was collected using three case studies, followed by content analysis. The overall findings indicate that there are no fundamentally new data quality issues in big data projects. Nevertheless, the complexity of the issues is higher, which makes it harder to assess and attain data quality in big data projects compared to the traditional projects. Ten antecedents of big data quality were identified encompassing data, technology, people, process and procedure, organization, and external aspects.
机译:大数据以其巨大的潜力而闻名。与潜力相反,在最近的一项调查中,超过一半的金融服务组织报告说,大数据未能实现预期的价值。造成这种情况的主要原因之一与数据质量有关。这项研究的目的是确定金融机构中大数据质量的前提。这将有助于了解如何提高大数据分析的数据质量。为此,进行了文献综述,并使用三个案例研究收集了数据,然后进行了内容分析。总体发现表明,大数据项目中根本没有新的数据质量问题。但是,问题的复杂性更高,与传统项目相比,这使得在大数据项目中评估和获得数据质量更加困难。确定了十个大数据质量的前因,包括数据,技术,人员,过程和过程,组织以及外部方面。

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