Application Performance Management

Optimizing Performance of Critical Applications in Financial Services

Big Banks Tapping Predictive IT Analytics to Reduce Operational Risk and Assure Service Performance

By Daniel Heimlich

High profile IT outages are again in the news and have become recognized as the defining example of IT operational risk.  According to one survey, computer outages cost American businesses at least $4 billion last year and as business reliance on information technology and cloud computing grows, the cost of computer downtime will mushroom exponentially.  

Not surprisingly, news is made when these outages hit banks whose online banking services are tied directly to their end users’ bottom line. There are also critical outages in institutional trading platforms that don’t make the news, but are even more costly to capital markets’ enterprises.

Concern over operational risk in today’s global banking and securities trading environments has led to even greater concern about process latencies and performance problems in online banking, trading, and payment systems.  Service outages or delays in transaction processing can result in penalties, compliance issues and ultimately in customer and revenue loss. 

And despite significant investments in monitoring tools for applications and infrastructure performance, 60% of IT operations’ executives acknowledge that they can’t identify and resolve performance problems before end-users or the business is impacted.  That’s because there are innumerable monitoring tools being used in IT Operations and Application Support groups, with no cross-silo visibility or correlation.  These monitoring tools also generate more performance data than IT support knows what to do with. The net result: no “big picture” and no warning about problems until it’s too late.

However, there are some notable exceptions to this scenario.  Some of the world’s largest banks, who were also early adopters of large-scale virtualized infrastructure, were the first to deal with the complexity of virtualization management and the deluge of IT and application data that came with it.  They discovered self-learning, predictive IT analytics as central to performance management solutions that could analyze and correlate data and metrics across different silos such as servers, network and storage.  This cross-silo analysis capability was extended to take into account application infrastructure, customer experience, and business activity data.  The resulting sophisticated correlation of the interdependencies between applications and infrastructure enabled them to detect IT performance and application anomalies before they cascaded into larger failures and outages.

It also resulted in the advent of unified performance dashboards that deliver a composite view of the “health” of key applications.  This new ability to have broad spectrum visibility removed barriers that had previously prevented them from achieving their business processing goals.  It is a service-focused approach that provides a means of measuring the performance of business processes across the monitoring spectrum beginning at the hardware level, up through the application performance and resource utilization, into business items and finally the customer experience.  And unlike rules-based management solutions that rely on human guesswork, these advanced, self-learning analytic technologies continuously adapt to changing business conditions to provide accurate indicators of impending performance issues.

Ultimately, predictive IT analytics lets Service Delivery Managers get ahead of crisis management, giving them time to proactively manage application health and reduce operational risk.

Eight of the world’s 10 largest banks are now running performance solutions featuring these predictive IT analytic layers that sit on top of their traditional application and infrastructure monitoring tools, providing a holistic view of performance across business, application, and infrastructure silos.  They analyze and correlate all the data, turning it into actionable intelligence to increase IT visibility, minimize their operational risk, reduce failed customer interactions, and protect their revenue streams.  

To learn more, check out this white paper on how predictive IT analytics is used to assure service performance and availability of global payments systems.

02 November 2011

Posted by: Netuitive