Requirements on and Antecedents of Big Data Quality: An Empirical Examination to Improve Big Data Quality in Financial Service Organizations

Big data has been widely known for its enormous potential in various industries, including finance. However, more than half of financial service organizations reported that big data has not delivered their expected value, yet. Among all, data quality is one of the issues behind this phenomenon, and thereby being the focus of this study. The objective of this research project is to develop key requirements for a reference architecture that improves big data quality in financial institutions. A joint approach between Requirement Engineering and Data Quality Management frameworks is performed to yield the desired requirements. Data collecting method through three case studies, seven content analysis, and literature review are performed to obtain the most comprehensive result. Overall findings indicate that antecedents of big data quality consist of data, technology, people, process and procedure, organization, and external aspects. They also encompass discovery of big data value, accessibility to data, and operationality of big data projects. Furthermore, there are six Information System requirements and two Human and Organizational requirements to improve big data quality.Technology, Policy and ManagementMulti-Actor System