Neurocritical care relies on the continuous, real-time measurement of numerous physiologic parameters. While our capability to obtain such measurements from patients has grown markedly with multimodal monitoring in many neurologic or neurosurgical intensive care units (ICUs), our ability to transform the raw data into actionable information is limited. One reason is that the proprietary nature of medical devices and software often prevents neuro-ICUs from capturing and centrally storing high-density data. Also, ICU alarm systems are often unreliable because the data that are captured are riddled with artifacts. Informatics is the process of acquiring, processing, and interpreting these complex arrays of data. The development of next-generation informatics tools allows for detection of complex physiologic events and brings about the possibility of decision support tools to improve neurocritical care. Although many different approaches to informatics are discussed and considered, here we focus on the Bayesian probabilistic paradigm. It quantifies the uncertainty inherent in neurocritical care instead of ignoring it, and formalizes the natural clinical thought process of updating prior beliefs using incoming patient data. We review this and other opportunities, as well as challenges, for the development and refinement of informatics tools in neurocritical care.