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.
| Original language | English |
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| Title of host publication | Proceedings of the 2016 IEEE International Conference on Big Data |
| Place of Publication | Washington |
| Publication status | Published - 2016 |
| Event | IEEE International Confernce on Big Data - Washington D.C., United States Duration: 5 Dec 2016 → 8 Dec 2016 |
Conference
| Conference | IEEE International Confernce on Big Data |
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| Abbreviated title | (IEEE Big Data 2016) |
| Country/Territory | United States |
| City | Washington D.C. |
| Period | 5/12/16 → 8/12/16 |