A Reference Model for Big Data Analytics

Abstract

The goal of big data analytics systems is to help business decisions supported by pieces of evidence from voluminous and diverse but uncertain data with high processing speed to create value. However, how the goal can be achieved is unclear and involves further exploration. In this paper, we propose a reference model for big data analytics which can help extract business questions and rationally evolve the questions into optimal analytics operationalizations explicitly considering underlying assumptions to achieve big data analytics goals aligned with business goals. It consists of 4 sub-models, Business Question Extraction Model, Big Data Analytics Evolution Model, Analytics Algorithm Reference Model, and Goal-Oriented Optimal Selection Model. We applied our reference model to a shipment decision in the retail business as an empirical study. We compared it with existing solutions in diverse aspects.

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