This article discusses the potential to transfer big data algorithms developed for ‘predictive policing’ to the field of corporate sustainability. To do so the paper starts with the thought experiment asking if major corporate scandals with disastrous environmental (BP Oil spill) or social (Rana Plaza building collapse) consequences, or global warming and floods could be prevented if big data driven predictive algorithms were in place. The article reviews first efforts to utilize big data for promoting sustainability and for reducing harm. By analogy the concept of ‘predictive policing’ is identified to be transferred to a concept called “Predictive Sustainability Control”. A systematic literature review on predictive policing is conducted. In a next step all parameters, characteristics, functional areas and processes, as well as legal and ethical issues of predictive policing were clustered and presented. Subsequently the concept is developed out of the clustered themes and criteria (Table 1) and a definition is provided: Predictive Sustainability Controlis the use of analytical techniques to identify subjects for mutual deliberation, supervision and intervention with the goal of preventing future harm related to environmental, social and governance issues, solving past scandals, and identifying potential actors/corporations of unsustainable activities and their stakeholders in the near future. Furthermore three functional areas (sustainability management, stakeholder partnership and regulatory integration) are defined and the concept is operationalized in a big data driven environment (Fig. 1). In the operationalization the concept of a digital ‘planetary nervous system’ is proposed and eXtensible Business Reporting Language data repositories used in corporate data management and Corporate Social Responsibility reporting were integrated to arrive at a data set, where predictive analytics could be applied to prevent future harm and reduce current unsustainability. In conclusion the question of governance, data protection and privacy is discussed critically and future research avenues for theory advancement and empirical testing are presented. In closing limitations of legality and functionality are discussed. The overall scientific value can be seen in the potential, which big data and algorithms have also for promoting and enforcing sustainable development based on rigorous data management leading to the predictive identification of likely unsustainable events.