People analytics depicts the algorithmization of human resources management characterized by the data-driven automation and support of people-related processes or tasks. On the one hand, people analytics promises productivity increases through optimizing workforce planning, hiring, or talent development. On the other hand, the extensive data collection and analysis of employees’ behaviors can be perceived as invasive, raising privacy concerns. This debate cannot only be explained by diverging norms and values, for example, practitioners realizing commercial opportunities while being criticized by academic commentaries. Instead, an alternative explanation suggests that the opposing views can be reconciled by diving into the conceptual differences regarding what analytical methods and data sources people analytics entails. Hence, this paper proposes the conceptions of operational and strategic people analytics based on a literature review of academics’ and practitioners’ literature. Four propositions about these conceptions’ privacy and performance implications are derived. Future research should empirically validate these propositions.