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

Abstract

Abstract-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.

Machine Learning Methods for Systemic Risk Analysis in Financial Sectors

“…In addition, data quality also should be considered in big data analysis. Fardani Haryadi, Hulstijn, Wahyudi, van der Voort, and Janssen (2016) defined data quality of different banks and proposed detailed goals. Brammertz and Mendelowitz (2014) proposed an information extraction for financial regulation from financial big data which comes from the granularity information of financial contracts, transformed into a contingent cash flow and they also developed a mixed model for multi-level financial objectives such as institutions, systems and individuals.…”

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

“…Likewise, Lakshen et al [50] argued on the various technical challenges that must be addressed before the potential of BDT and DQD can be fully realised. Haryadi et al [14] confirmed that BDT and DQD are the central issues for implementing BDA.…”

“…Meanwhile, the concept of information quality is defined as how well the information supports the task [46]. Haryadi et al [14] asserted that data quality is focused on data that have not been analysed, while information quality is focused on the analysis that has been done on the data. This study, however, opines that data quality should focus on the wellness and appropriateness of data, which encompasses either before or after it has been analysed, in which it should meet the requirements of organisations [12].…”

“…Salih et al [12] and Wamba et al [13] have highlighted the lack of data quality mechanisms being applied in BDA prior to data usage. Several studies have considered the potential of data quality for BDA application [14][15][16][17][18], yet, specific questions about what drives the dimensions of data quality remain unanswered. Nevertheless, studies on data quality and BDA are still underway and have not reach a good level of maturity [7].…”

“…These attempts to assign the maximum number of characteristics to big data show the lack of uniform consensus regarding the core of big data characteristics [33]. Although big data characteristics and data quality are viewed as distinct domains, several studies have found that these two domains are interconnected and closely related [9,14,17]. A better understanding of the core characteristics of big data and the dimensions of data quality is needed.…”