GUIDELINES FOR BIG DATA MANAGEMENT: ACHIEVING THE COMPETITIVE ADVANTAGE OF THE INDUSTRIAL BUSINESS SECTOR

Abstract

The management of big data is crucial for gaining a competitive edge in the digital era. Currently, there is a dearth of effective and efficient guidelines for managing Big Data. This study aims to investigate the guidelines for managing Big Data in order to gain a competitive advantage in the industrial business sector. It will develop a structural equation model through the use of mixed-methodology research. The qualitative research included conducting in-depth interviews with nine experts and organising focus group discussions with 11 qualified experts. A survey was conducted among 500 executives responsible for information management in the industrial business sector, using questionnaires as part of the quantitative research. Descriptive Statistics, Inferential Statistics, and Multivariate Statistics were employed. The research findings highlighted the significance of prioritising four key components in order to attain a competitive advantage in the industrial business sector through effective Big data management: 1) Information Management (x̅ = 4.16) was being the regular evaluation of compliance with the organization's data governance policy, 2) Human Resource and Organization Development (x̅ = 4.09) was being the design of work systems that encourage employees considering information for decision-making; 3) Innovation and Technology Management (x̅ = 4.08) was being the creation of Big data analytics models for market research and product development and 4) Alliance Centric (x̅ = 4.02), was the analysis of market and environment to adjust collaboration plans with business alliances. The results of hypothesis testing indicated that there was no statistically significant difference (p > 0.05) in the emphasis on Big data management as a factor contributing to competitive advantage between two groups of industrial businesses, classified by the number of years of registered experience. The analysis of the developed structural equation model (SEM) confirmed its suitability and consistency with the empirical data. The model exhibited satisfactory fit indices, including a chi-square probability level of 0.075, a relative chi-square of 1.163, a goodness of fit index of 0.962, and a root mean square error of approximation of 0.018. The results indicate that the model accurately represents the relationships among the variables being studied.