Chapter 7 148 already have the necessary context and data – or a convenient sample at least – whereas the scholars often have the resources and capabilities to conduct a rigorous analysis. Chapter 6 illustrates these dual benefits of people analytics. In Chapter 6, I demonstrate one way in which organizations can leverage the value of the longitudinal information they already store in their HR information system (HRIS). As a scholar, I had the necessary means (e.g., time, objectivity, access to literature) to come up with theory- based hypotheses, to identify and collect the relevant data, to transform them into the needed format, and to conduct sophisticated statistical analyses. However, I could not have performed my research without the longitudinal information that the two cooperating multinationals provided on an enormous sample of graduate trainees, or without the knowledge that their HRM professionals provided in identifying relevant data, interpreting data patterns, and considering their implications. In terms of practical value, the survival analysis of Chapter 6 quantifies to what extent the organizations were capable of retaining their graduate trainees and how several HRM practices affected this process. The analysis provided relevant insights into complex longitudinal processes and facilitated evidence for informed decision-making regarding several HRM policies and practices. In terms of the scientific value, in particular, the longitudinal, context- dependent effects of short-term assignment were an important finding. A conventional HRM research setup would not as easily have generated such dual benefits – illustrated by the lack of scientific knowledge on these novel types of assignments. Bridges between Disciplines Additionally, people analytics has the potential to form bridges between functional disciplines in both scientific and practical settings. On the one hand, people analytics teams work on a wide variety of issues within the HRM domain. Projects may explore recruitment and selection, training and development, career and succession planning, performance management and rewards, absenteeism, effective leadership, diversity and inclusion, effective teamwork, employee health and safety, culture change, communication networks, strategic workforce planning, and many more. This diversity of projects requires a diversity of knowledge, mostly related to HRM, organizational behavior, psychology, and other social sciences, but also some knowledge of labor legislation. Professionals with these kind of functional backgrounds have historically been present in the HRM domain – both in practice and in science. On the other hand, people analytics projects call for more unusual expertise fields. Every project can require specific data sources, research designs, statistical analyses, legal considerations, and business implications. Take, for instance, the implementation of a decision-support system (i.e., predictive model) for the selection of successful job candidates. Its development and implementation will require knowledge of: how candidate success should be measured; what predictors could be important; how predictors should be measured; what and where data could be available; how data should be interpreted; which methods could be applied; how models and results should be interpreted; what possible HRM actions could be taken; what legal barriers and implications could be; what IT infrastructures could be used; how results and implications should be communicated; et cetera. People analytics teams therefore commonly include