The eMaintenance solutions consolidate computing and information and communication technologies (ICT) with prognostics and health management (PHM) for maintenance decision-making. The development to eMaintenance is to move from reactive to predictive maintenance. The role of technology in maintenance management has developed through the years, first beginning with a manual system, followed by a computerized maintenance management system (CMMS), and the eMaintenance management system (EMMS). Since analytics is the process of generating knowledge based on understanding, maintenance analytics (MA) is considered as big data analytics for maintenance. Enhanced reliability and reduced costs by predictive maintenance can help to reduce the economic risks for industrial system providers. Establishment of knowledge discovery in databases (KDD) mechanisms for maintenance decision support can be facilitated through provision of a meta-level model through which a range of concepts, models, techniques, and methodologies can either be clarified and/or integrated.