Value realisation of data analytics to improve business intelligence in the manufacturing sector in the big data era

Abstract

Currently, the fourth industrial revolution is driven by cyber-physical systems through interconnected emerging technologies that produce a lot of information, referred to as the big data environment. However, extracting value from the information presents valuable opportunities and insights to those who are able to analyse the data. The current environment requires managers to use technologies such as business intelligence systems (BI) to analyse the data. Technology has advanced; managers who do not embrace it to conduct business intelligence become spectators in the world of business where they should be dominating. The aim of this research study was to explore the value to be derived from data analytics application to improve business intelligence in the manufacturing sector. The main objective was to propose suitable general frameworks that can guide managers to analyse data in the big data environment using BI to create a competitive advantage. The specific objectives were to explore how external data are integrated with internal data and to examine what is limiting managers from using BI systems to analyse the resultant data to extract value during the decision-making process. The study design was an exploratory qualitative case study that involved Executives, Senior managers, and Consultants as implementers of BI. Data were collected through semi-structured interviews and analysed using Atlas_ti to generate codes and themes. A focus group validated the findings. The results revealed that BI systems are not plug-and-play; managers lack technical skills and time due to operational commitments. External consultants lack knowledge of the business process. Internal consultants lack facilitating conditions and training. Managers are not at liberty to share their data even though the data are already structured. They are afraid of violating ethics because there are no clear frameworks to integrate systems. Four frameworks emerged from this study (i) a framework to reach the pervasive level of business intelligence maturity level (ii) the Trust benefit framework for external data sharing (iii) the BI skills imparting framework and (iv) the Framework for adopting data analytics in an environment of other emerging technologies. The research study made an original contribution to the Unified Theory of Acceptance and Use of Technology theory. It suggests the incorporation of trust, risk, and ethical considerations as key moderators for sharing data and adopting data analytics.