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Advances in information technology have fueled an exponential increase in the volume and diversity of data generated by organizations and individuals. In this era, Data Science has emerged as a crucial discipline for uncovering hidden patterns within data, thereby facilitating smarter decision-making processes. This paper presents a comprehensive and up-to-date overview of the challenges and opportunities in the application of Data Science, with a particular focus on the PLS (Partial Least Squares) analysis method. The PLS method, implemented through the SmartPLS application, synergizes partial path analysis with partial least squares techniques and has gained prominence as a preferred method for analyzing complex structural models within the field of Data Science. This study delves into the practical applications and benefits of PLS in handling diverse and intricate datasets, and also elucidates the potential obstacles encountered during its implementation. By examining the methodological strengths and addressing the challenges associated with PLS, this paper aims to provide valuable insights for researchers and practitioners seeking to leverage this method and the SmartPLS application for enhanced data analysis and informed decision-making.
Karo et al. (Tue,) studied this question.
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