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Chapter 2 42 practitioners should jump on the bandwagon and seek cross-functional collaborations, where domain experts within managerial functions team up with experts in statistics and machine learning in order to test their academic theories and deploy relevant business solutions. Preliminary empirical evidence from fields such as operation management and IT shows that collaborations between functional management domains and statistical researchers can add great value to organizational performance (cf. Wamba et al., 2017). One such direction would be to apply advanced statistical methods to leverage value from big data in business functions that are yet underexposed. For instance, in HR, BDA could be used to predict the hiring success of applications, the effectiveness of training courses, or the number of workplaces needed. Another interesting knowledge-sharing opportunity lies in peripheral clusters, such as Sports Analytics where novel measurement methods (e.g., wearables, sensors) are already being used to optimize a variety of processes. Dissemination of such knowledge to more mainstream clusters of management research could be benecial for future operations in organizations. For example, wearables can be used to explore the communication patterns in organizations with the aim of improving knowledge sharing, or to monitor employees’ health in order to improve their well-being (e.g., Wenzel & Van Quaquebeke, 2017). Second, ethical considerations are essential in BDA research (Boyd & Crawford, 2012; Herschel & Miori, 2017). It goes without saying that all researchers should make sure that the privacy and the interests of their study subjects are protected, but ethicality is even more important when dealing with “big” data types such as continuous audiovisual, biometric, behavioral, or geolocation monitoring. Particularly when it comes to predictive analytics, scholars and practitioners should take additional care in preventing the creation of self-fulfilling prophecies or the incorporation of human bias into decision-making algorithms (Herschel & Miori, 2017). Additionally, big data and analytics are often seen as objective and accurate (Boyd & Crawford, 2012) whereas this is not necessarily the case. Nevertheless, complex and inaccurate data or predictions can create a false sense of authority that, as a result, becomes undisputable in organizations. In light of these precautionary notes, we were surprised that our results did not include clusters or core papers specifically exploring the ethical perspectives related to BDA or the ethical issues related to predictive analytics. We call for future research examining to what extent the above issues occur in organizations, how they are currently handled, and what best practices can be implemented from a management perspective to prevent them. In practice, continuously exploring and testing both the financial and ethical implications of their analytical initiatives would allow organizations to establish their long-term survival firmly.