Understanding big data analytics and the Interpretive approach for analysis purposes

Abstract

Big data is a complex sets of data based on its unprecedented, structured, and unstructured nature of its volumes, varieties and velocities. This makes it difficult for many organisations to achieve their objectives from a mono-approach in the use of analytics tools such as descriptive, predictive and prescriptive for big data analysis. The focus of this research was to explore and examine the possibility of combining the interpretive approach with the analytics tools for big data analysis purposes in an organisation, towards enhancing business objectives. Thus, the interpretive approach, from the perspective of subjectivism is required to enact and bridge that gap created from the mono-approach in using the analytics tools. The objectives of this research emanates from this point, and therefore were to: (1) identify some of the gaps in the application of big data analytics; and (2) propose methods through which the interpretive approach can be combined with the analytics tools, for big data analysis, to leverage business objectives. Existing related materials were gathered to achieve the objective from the perspective of qualitative methods. The analysis of the materials followed the interpretive approach from an epistemological standpoint in order to gain a better understanding of the knowledge that is obtainable about combining analytics tools with the interpretive approach for the analysis of big data. Based on the findings from the analysis a complementarity data analytics and interpretive approach model was developed and proposed, for organisational purposes.