As any IT administrator worth his salt knows, the challenge of application performance monitoring has increased exponentially. One of the biggest culprits is the inability to monitor and correlate complex Java application data. Monitoring the behavior of Java applications is challenging at best. Correlating this data with other IT performance and business metric data is almost impossible because of the sheer amount of data and the existence of silos in the enterprise that inhibit data sharing ...

“Analytics”, according to IT vendors, can save time, money and improve decision-making. Unfortunately, “analytics” is being thrown around generically by vendors in many different contexts, representing a wide range of capabilities that are as different as polar bears and Amazonian ants. As a result, “analytics” has become ambiguous and poorly defined ...

Because of the size and complexity of some enterprise application deployments, it can take some time just to incorporate and deploy analytics for the IT infrastructure and CE metrics. Meanwhile the executive or line of business owner who is funding the project is saying “What's in this for me? Where's my view of the business?”

For all the benefits, virtualization often meant losing control and visibility of systems which ultimately hindered the ability to deliver business solutions. But much has changed with the advent of self-learning predictive analytics that can correlate IT and application data to deliver a coveted “composite view” of critical application “health” and performance ...