Abstract
As big data becomes an important part of business analytics for gaining insights about business practices, the quality of big data is an essential factor impacting the outcomes of business analytics. Although this is quite challenging, conceptual modeling has much potential to solve it since the good quality of data comes from good quality of models. However, existing data models at a conceptual level have limitations to incorporate quality aspects into big data models. In this paper, we focus on the challenges cause by Variety of big data propose IRIS, a conceptual modeling framework for big data models which enables us to define three modeling quality notions – relevance, comprehensiveness, and relative priorities and incorporate such qualities into a big data model in a goal-oriented approach. Explored big data models based on the qualities are integrated with existing data grounded on three conventional organizational dimensions creating a virtual big data model. An empirical study has been conducted using the shipping decision process of a worldwide retail chain, to gain an initial understanding of the applicability of this approach.
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Park, G. et al. (2020). A Big Data Conceptual Model to Improve Quality of Business Analytics. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_2
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DOI: https://doi.org/10.1007/978-3-030-50316-1_2
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