monitoring the applications is also set to become a complex task due to dynamic provisioning and deprovisioning of IT resources to meet
applications’ demands.
For example, an enterprise of 5,000 servers and 125 business applications typically generates 1.3 terabytes of IT operational data every day—via collection
of various metrics, such as events, server monitoring logs, application monitoring logs, endpoint
managers, network monitoring and storage monitoring. Enterprises need to adopt
analytical solutions that can analyze the terabytes of big data from IT operations and provide relevant
insights that can be acted upon immediately. Some of the situations where predictive maintenance may
apply are depicted in figure 2.
monitoring the applications is also set to become a complex task due to dynamic provisioning and deprovisioning of IT resources to meetapplications’ demands. For example, an enterprise of 5,000 servers and 125 business applications typically generates 1.3 terabytes of IT operational data every day—via collectionof various metrics, such as events, server monitoring logs, application monitoring logs, endpoint managers, network monitoring and storage monitoring. Enterprises need to adoptanalytical solutions that can analyze the terabytes of big data from IT operations and provide relevant insights that can be acted upon immediately. Some of the situations where predictive maintenance may apply are depicted in figure 2.
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