Interdisciplinarity in data science over big data: Findings for mining industry

Abstract

Data Science and Big Data are leveraged by businesses in many ways to improve operational and strategic capabilities, and ultimately, to positively impact corporate financial performance. However, there are challenges related to Big Data, such as modelling, new paradigms and novel architectures that require original approaches to address data complexities. In the specific case of iron ore mining industry, there is a considerable pressure at present to reduce costs due to the recent major fall in iron ore prices. This study discusses if an interdisciplinary approach could help mining industries to extract the most of data science initiatives over big data. In this study we applied a narrative literature review method to briefly present a chronological review of disciplines and interdisciplinarity as well as the evolution of data science over big data. Then we discussed: 1) the importance of involving people from different profiles; 2) the relevance of technology transfer inside computing research field; 3) the requirements for integrating so many different people and technologies in such initiative. We concluded that achieving results with Data Science initiative over big data is not related to a single knowledge area, especially in mining industries.