Big Data Analytics: Service and Manufacturing Industries Perspectives

Big Data Analytics: Service and Manufacturing Industries Perspectives

Nachiappan Subramanian (University of Sussex, UK), Muhammad D. Abdulrahman (The University of Nottingham – Ningbo, China), Hing Kai Chan (The University of Nottingham – Ningbo, China) and Kun Ning (The University of Nottingham – Ningbo, China)
Copyright: © 2017 |Pages: 11
DOI: 10.4018/978-1-5225-0956-1.ch002

Abstract

In this chapter, we will introduce practical issues and implementation challenges from the industry perspective. In particular, we explain three aspects based on the panel discussions from the set of representatives participated in a big data conference from three dominant industries such as e-commerce, health care and computer hardware, which are sought of big data for their growth and development. We introduce overall challenges and explain typical industry based practical issues, how they visualize the big picture for their strategic development and how industries are gearing towards converting the challenges to big opportunities through the partnership of universities. Finally, based on the content analysis we offer potential trends and future research directions.
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Healthcare Perspective

During the proceedings of the conference, Company A suggested that the most disruptive technology in supply chain is big data analytics, based on their survey. This is closely followed by digital supply chain. A recent PWC report noted that there are different interpretations for industry 4.0 in Germany, US and China. The report noted however, that despite the differences, “Data application and integration will be the key ability of the industrial age of the Internet” as very important. Additionally, industry 4.0 will bring opportunities in five different aspects that include interconnection, integration, data, innovation, and transition. Two of these aspects, interconnection thinking and data, will be leading survival modesfo r companies in the future. These firms envisage big challenges in data operation in the age of Internet of Technologies (IoT) and called for increased discussions on challenges of using big data. Moreover recently some companies started to distribute their data in a way that paves way to visualize the data to recognize the importance and benefits. It is obvious that firms have huge data but not all are able to make use of such data by way of accurate and independent analysis. This has necessitated the need for collaboration among industries and higher institutions to effectively utilize the available data. The collaboration between universities and the industry is increasingly perceived as a motive to enhance innovation through knowledge exchange. University interaction aims mainly to encourage knowledge and technology exchange (Bekkers & Bodas Freitas, 2008; Siegel, Waldman, & Link, 2003). In the conference, the discussions were focused on how to help both university and companies to be benefited by the collaboration.

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