Barriers to the Adoption of Big Data Analytics in Saudi Arabia’s Manufacturing Sector: An Interpretive Structural Modeling Approach

Big data analytics has the potential to greatly improve the operations of manufacturing industries, aid in decision making, and foster innovation. However, there exist several barriers that undermine the successful adoption of big data analytics in these industries. This paper presents a structural analysis of the barrier to big data analytics adoption in manufacturing industries. Through an extensive literature review and expert analysis, a compilation of the various barriers was made. The interpretive structure modeling (ISM) technique was then used to analyze the interplay between the barriers: this technique was used to build a hierarchy whose respective objective functions indicated how each barrier influenced the other. These findings help in the understanding of the hierarchical relationships between the various barriers and can thus help organizations in prioritizing strategies to mitigate these barriers. The results depict some barriers which do have a high-power influence over others and, as such, depict critical points that manufacturing industries need to address when adopting big data analytics. This paper also elaborates the relationships between the barriers, which will help the decision makers create strategies to mitigate them effectively. This study’s findings contribute to the existing body of knowledge on barriers to adopting big data analytics in manufacturing industries and provides an efficient approach for organizations to systematically address barriers.