People analytics (PA) has experienced significant growth in recent years due to the increasing availability of employee data and the impact of digitalization on organizations. This data-driven approach utilizes inductive methods to predict various outcomes in the field of human resources. Nevertheless, concerns have emerged regarding the availability and reliability of the data used in PA. Surprisingly, the quality standards of these data-driven methods have not been evaluated in the PA literature, despite their widespread adoption. To address these gaps, nine research questions covering expertise areas, psychological constructs, patterns/trends, study types, data sources, reliability reporting, data-driven frameworks, prediction accuracy, and open science practices in PA were reviewed. A scoping review was conducted to extract relevant information from each piece of literature, while bibliometric analysis provides a structured analysis of trends, themes, and key contributors. A total of 3,103 records were identified from the Scopus (n = 449) and APA PsycINFO (n = 2,700) databases, with nine studies included in the review. Findings indicated a lack of consideration given to quality, reliability aspects, and open science practices within PA literature. The predominant emphasis of the research was on the evaluation of variables, particularly turnover intention. This study contributes to advancing the understanding of PA by emphasizing the importance of incorporating quality standards and open science practices to enhance the reliability and credibility of research findings. The classification of the PA literature and recommendations for future research directions are provided, highlighting the need for a hierarchy of knowledge in the field. / Scoping Review of People Analytics