The Effect of Data Security Procedures and Big Data Analytics on Engineering Performance: A Case Study of Lagos (Iganmu) Industrial Layout

Abstract

The purpose of this research is to comprehend how big data analytics affect engineering performance. The industrial part especially the engineering practice is among the most significant and delicate in the world. Gathering and manufacturing have a huge social impact on the economies of the nations and, consequently, on the lives of individuals all over the world. The potential for big data to completely alter engineering practice and enhance ongoing engineering projects. Many organizations appear to be aware of the advantages big data can bring to their performance in engineering practice, particularly its significant possible worth, but they encounter a number of challenges when implementing it, primarily because they are having trouble figuring out how to use the derived insights for their development. The development of new strategies and services is a crucial engineering activity, and it has been demonstrated to significantly affect an organization’s viability. If these insights are monetized, Organizations aiming for an improved engineering practice can build brand-new, customer-centered, and data-driven projects or both goods and services, providing a long-lasting competitive advantage and new revenue streams. According to empirical research, companies that have engineering practice incorporated with a data-driven approach that can show how big data contributes to improved performance, while those that have not yet instilled the entire organization struggle with an absence of comprehension on how to use big data technology to create potential value and accomplish their organizational goals. Due to the enormous strategic potential of big data, this article tries to conceptualize and investigate its effects on corporate performance. It also explores the impacts of big data on engineering performance because of its high strategic potential. Finally, it explores whether and how the creation of new engineering services and projects makes use of big data and related technologies. An in-depth SWOT, binary Logistic Regression analysis, and the use of grounded theory combine previous big data studies with several enterprises in Lagos, Nigeria’s Iganmu industrial layout area. The caliber of data gathered, data availability, legal considerations of data confidentiality and safekeeping, and highly qualified individuals working with big data are additional critical factors that influence the use of a data-driven approach. Therefore, in order for companies to achieve effectiveness and efficiency, they need to reflect on and make strategic decisions utilizing a comprehensive perspective on big data.