Decision-making in large corporations - role of big data analytics & data mining

Abstract

Function - The goal of this particular paper is presenting a novel framework for strategic decision making utilizing Big Data Analytics methodology. Design/methodology/approach - In this particular research, 2 distinct machine learning algorithms, Random Forest as well as Artificial Neural Networks are used to forecast export volumes working with a considerable level of open industry information. The forecasted values are in the Boston Consulting Group Matrix to conduct strategic industry analysis. Results - The proposed technique is validated utilizing a hypothetical case study of a Chinese business exporting freezers and refrigerators. The results indicate the proposed methodology makes exact trade forecasts and helps to conduct strategic industry evaluation properly. Furthermore, the RF performs much better compared to the ANN in terminology of forecast accuracy. Investigate limitations/implications - This analysis provides just one case study to evaluate the proposed methodology. In future scientific studies, the validity of the suggested technique is further generalized in various item groups and nations. Functional implications - In present day extremely competitive business environment, a good strategic industry evaluation involves exporters or importers making much better predictions along with strategic choices. To us the proposed BDA based strategy, businesses may efficiently determine business opportunities and alter their strategic choices appropriately. Originality/value - This's the very first study to provide a holistic methodology for strategic industry evaluation using BDA. The proposed methodology effectively forecasts global trade volumes and helps with the strategic decision making practice through succeeding insights into worldwide marketplaces.