IBM’s Watson analytics and other IBM analytics tools enable advanced analytics to help users in three ways: It boosts operational performance and reduces downtime by bringing intelligence to production equipment. Using data from connected machines helps to predict anomalies and failure patterns and to initiate corrective measures. It helps to improve product quality and yield using quality analytics that helps to detect the conditions that contribute to process failure and quality issues, including image and audio analytics. To deliver these outcomes, IBM considers making a range of analytics technologies available to MindSphere, from dashboards through predictive and prescriptive analytics, to the power of cognitive analytics. Cognitive analytics has a key role to help customers improve operational performance. User can access and analyze all types of data from sensors. They also can identify hidden connections in huge amounts of production data to help diagnose issues and draw on the resulting knowledge to continuously improve across manufacturing processes. MindSphere can be used by industrial enterprises as the basis for their own digital services, in fields such as predictive maintenance, energy data management or resource optimization. Machine and plant engineering companies in particular can use the platform to monitor machine fleets distributed around the world for servicing purposes, to reduce standstill periods and so offer new business models. MindSphere also provides the foundation for data-based services from Siemens such as predictive maintenance of machine tools (Machine Tool Analytics) or integrated drive systems (Drive Train Analytics).