Featured White Paper: HP introduces HP Service Health Analyzer, and explains why predictive analytics is a key to HP's BSM solution.
Making sure you have complete visibility into the health of your business service, that you can adapt, and even survive, in today’s cloud and virtualized IT environment isn’t just a “nice-to-have.” It is mandatory.
Managing a dynamic infrastructure and applications will take more than just reacting to business service problems when they occur, or manually updating static thresholds that are difficult to set accurately and problematic to maintain.
In today’s world, you need advanced notification of problems so you can solve those issues before the business is impacted.
You need better visibility into how your applications and business services are correlated with your dynamic infrastructure, so you can track anomalies across the complete IT stack, including the network, servers, middleware, applications, and business processes.
You need an easier way of determining acceptable thresholds as a basis for identifying events that might impact the business.
You need automation to leverage the knowledge from past events that can be applied to address new events more efficiently and can also be used to suppress extraneous events allowing IT to focus on just the business-impacting events.
While IT organizations have the methods to collect massive amount of data, what has been lacking is the analytic tool set and automated intelligence to correlate these disparate metrics from both an application and a topology perspective to help these organizations anticipate or forecast potential problems on the horizon. IT managers are looking into the world of predictive analytics, one of the notable business intelligence trends of 2011, to help them improve service uptime and performance, thereby increasing business-generated revenue and decreasing maintenance and support cost.
This white paper features HP Service Health Analyzer (SHA), a predictive analytics tool built on top of a real-time, dynamic service model so you can understand the relationship of metric abnormalities with the application and its underlying infrastructure.