Decision-making in maintenance has to be augmented to instantly understand and efficiently act, i.e. the new know. The new know in maintenance needs to focus on two aspects of knowing: 1) what can be known and 2) what must be known, in order to enable the maintenance decision-makers to take appropriate actions.Hence, the purpose of this paper is to propose a concept for knowledge discovery in maintenance with focus on Big Data and analytics. The concept is called Maintenance Analytics (MA). MA focuses in the new knowledge discovery in maintenance. MA addresses the process of discovery, understanding, and communication of maintenance data from four time-related perspectives, i.e. 1) "Maintenance Descriptive Analytics (monitoring)"; 2) "Maintenance Diagnostic Analytics"; 3) "Maintenance Predictive Analytics"; and 4) "Maintenance Prescriptive analytics".Keywords: big data, maintenance analytics, eMaintenance, Knowledge discovery, maintenance decision support. INTRODUCTIONThe dynamic global and local business scenarios put new demands on the decision-making processes in an organisation.The new decision-making processes need to provide enhanced capability for knowledge discovery online and in real-time. To increase the overall business efficiency, organisations need to implement a knowledge discovery platform in their core processes such as business, operation, and maintenance. Knowledge discovery is depended on availability if accurate and consistence data and information.Today, enterprises are overwhelmed by managing data and its logistics. It can be testified that there is a growing gap between data generation and data understanding (Witten et. al, 2011). It can be considered that decisions are also becoming more complex with greater uncertainty, increasing time pressure, more rapidly changing conditions, and higher stakes (Busemeyer & Pleskac, 2009). The increased information needs and the development of Information and Communication Technology (ICT) have added velocity to everything that is done within an organization through transforming the business process into eBusiness (Lee, 2003). The knowledge discovery, which is an essentially a major aspect for maintenance decision support; is usually done by discovering special pattern of data, i.e. by clustering together data that share certain common properties (Wang, 1997).Extensive application of ICT and other emerging technologies facilitate easy and effective collection of data and information (Parida, 2006;Candell et al., 2009). In maintenance, enhanced use of ICT facilitates the development of artefacts (e.g. frameworks, tools, methodologies and technologies); which aim to support maintenance decision-making. These artefacts also enable improvement of different maintenance approaches, such as; preventive maintenance and corrective maintenance. Furthermore, ICT provide additional capabilities, which can be used within diagnostic and prognostic processes. The prognostic and diagnostic processes in an enterprise can be facilitated through provision of pr...