Personality affects various social behaviors of an individual, such as collaboration, group dynamics, and social relationships within the workplace. However, existing methods for assessing personality have shortcomings: self-assessed methods are cumbersome due to repeated assessment and erroneous due to a self-report bias. On the other hand, automatic, data-driven personality detection raises privacy concerns due to a need for excessive personal data. We present an unobtrusive method for detecting personality within the workplace that combines a user’s online and offline behaviors. We report insights from analyzing data collected from four different workplaces with 37 participants, which shows that complementing online and offline data allows a more complete reflection of an individual’s personality. We also present possible applications of unobtrusive personality detection in the workplace.