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Chapter 7 164 Such social network analyses are not only interesting for adjustment, retention, and performance outcomes, but also for knowledge transfer. Social network analysis can demonstrate how information is disseminated between home and host countries or between parent and subsidiary companies and whether intracompany communication channels improve because of expatriation. Second, collaborating with large multinationals may solve sample size issues. With their large expatriate populations scholars could test, for instance, whether assignments characteristics (e.g., home-host country combinations, duration, benefits) influence work, life, and family outcomes, keeping other personal and organizational context characteristics stable. Moreover, these larger companies are the perfect context to explore and experiment in what ways the expatriate experience abroad may be improved, for instance, by assigning some expatriates mentors or buddies, or by initiating communities. Preferably, researchers could look for natural experiments, where the HRM or mobility policies have changed over time, or have changed for specific group of assignees but not the other. Finally, multinationals usually have large, longitudinal HRIS and survey databases that are relatively rich in information compared to the small, convenience samples common in scientific expatriate research. Here, scholars could trade-off some of their theoretically founded measures in return for larger data volume. 7.6 Conclusion The current speed of technological development is rapidly changing the way in which we organize and manage employees. Fortunately, these developments come with improved capabilities to collect and analyze data about the perceptions and behaviors of employees and how these are influenced by HRM policies and practices. While it seems that both HRM research and practice have been slow to adopt analytics, the right skill- and mindset are developing, if not with some assistance from other domains. This dissertation demonstrated how people analytics can help HRM to become more evidence- based, illustrated among others in application to expatriate management. I propose that people analytics and the adoption of machine learning principles will bring more automated and personalized HRM with continuous improvement through exploration and experimentation. At the very least, the HRM function should retain an advisory seat at the analytics table in order to champion employee interests and advocate a balanced approach to machine learning initiatives. In the best case scenario, HRM scholars and HRM professionals will explore and lead this rise of people analytics.