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Using Analytics to Detect Application Performance Anomalies

IT organizations are under more pressure to deliver exceptional business performance than ever. Further complicating the challenge is the evolving nature of Information Technology (IT). The rise of Big Data, mobile, cloud, and BYOD have added complexity, making it ever more challenging for IT to acquire the visibility they need to detect anomalies.

Today, an organization’s application infrastructure typically includes Web components, messaging middleware and mainframes. Application performance is impacted by many factors coming from multiple sources—application servers, messaging protocols, virtualized systems, capacity issues and many more. Inevitably, failures in one or more of these systems occur — and IT is left to deal with the result.

Such situations are why Application Performance Management (APM) solutions exist. To be effective, APM must deliver three major benefits:

- Gain enough visibility to see an entire system

- Track activities through the infrastructure chain as they occur

- Correlate events—many of which might seem unrelated—in order to spot developing trends before users are impacted.

Surprisingly, a number of APM platforms miss on one or more of these key functions.

Monitoring is Not Enough

To be sure, most APM solutions do a good job of monitoring individual applications. But, monitoring is not enough. When problems arise, especially in today's complex topologies, the failure of a single application is rarely the culprit. Performance threats usually are the result of multiple issues — and many of these, if caught early in the process using real-time analytics, could prevent much larger failures from occurring. Evading cascading failures is essential. Ideally, IT Specialists should avoid being in the position of putting out fires — they should be able to make sure the fire never starts. But, without the necessary visibility, this is no simple task.

To properly manage today's application environment, organizations must be able to analyze the entire application chain from end to end, understanding the dependencies between the links in the chain. It must also be able to focus on early detection of abnormalities, differentiating symptom from cause rather than simply reacting to an outage. The combination of these two factors provides the level of assurance IT needs in its key mission: to reduce the frequency and duration of outages.

End-to-end performance monitoring and analysis must embrace the entire IT environment, from .NET to mainframes. It must cover a wide range of components from J2EE application servers, Web Services to middleware messaging, brokers and even legacy applications. It must also be elastic, having the ability to transparently scale to meet unexpected surges in demand.

Analyzing Situations with Complex Event Processing

Accomplishing the second requirement — proactive analytics, rather than reactive response — requires a sophisticated technology, one example being Complex Event Processing (CEP). CEP engines, along with business policies, analyze situations or "business views" comprised of multiple events and key performance indicators.

Instead of alerts based on individual events passing a threshold, the analytical approach is analyzing situations. It compares application behavior against your norms, looking for anomalies that indicate potential problems. Norms are established dynamically using statistical functions such as Bollinger bands, momentum oscillators, standard deviation, velocity, fluctuation and rates of change.

This approach ensures that real problems — not just transient variations, a.k.a. "false alarms" — are identified and ensures true readings of real-time performance.

With CEP-based analytics, IT Specialists are assisted in quickly identifying root causes, instead of merely chasing symptoms. By dynamically analyzing event streams, the CEP approach can differentiate symptoms from cause — even inferring an explanation where there is signal loss.

APM solutions using real-time anomaly detection have the ability to maintain SLAs in the most high-demand deployments including payments, EFT, trading, settlement, compliance patient data, claims processing and retail order management. They not only bring developing situations to the attention of IT staff before users are aware, but also assist in diagnosing and correcting the underlying causes quickly and efficiently.

In an era when business functions are more sophisticated, diverse, integrated and immediate than ever, analytical Application Performance Management plays an essential role for IT professionals and their customers.

Charley Rich is VP Product Management and Marketing at Nastel Technologies.

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Using Analytics to Detect Application Performance Anomalies

IT organizations are under more pressure to deliver exceptional business performance than ever. Further complicating the challenge is the evolving nature of Information Technology (IT). The rise of Big Data, mobile, cloud, and BYOD have added complexity, making it ever more challenging for IT to acquire the visibility they need to detect anomalies.

Today, an organization’s application infrastructure typically includes Web components, messaging middleware and mainframes. Application performance is impacted by many factors coming from multiple sources—application servers, messaging protocols, virtualized systems, capacity issues and many more. Inevitably, failures in one or more of these systems occur — and IT is left to deal with the result.

Such situations are why Application Performance Management (APM) solutions exist. To be effective, APM must deliver three major benefits:

- Gain enough visibility to see an entire system

- Track activities through the infrastructure chain as they occur

- Correlate events—many of which might seem unrelated—in order to spot developing trends before users are impacted.

Surprisingly, a number of APM platforms miss on one or more of these key functions.

Monitoring is Not Enough

To be sure, most APM solutions do a good job of monitoring individual applications. But, monitoring is not enough. When problems arise, especially in today's complex topologies, the failure of a single application is rarely the culprit. Performance threats usually are the result of multiple issues — and many of these, if caught early in the process using real-time analytics, could prevent much larger failures from occurring. Evading cascading failures is essential. Ideally, IT Specialists should avoid being in the position of putting out fires — they should be able to make sure the fire never starts. But, without the necessary visibility, this is no simple task.

To properly manage today's application environment, organizations must be able to analyze the entire application chain from end to end, understanding the dependencies between the links in the chain. It must also be able to focus on early detection of abnormalities, differentiating symptom from cause rather than simply reacting to an outage. The combination of these two factors provides the level of assurance IT needs in its key mission: to reduce the frequency and duration of outages.

End-to-end performance monitoring and analysis must embrace the entire IT environment, from .NET to mainframes. It must cover a wide range of components from J2EE application servers, Web Services to middleware messaging, brokers and even legacy applications. It must also be elastic, having the ability to transparently scale to meet unexpected surges in demand.

Analyzing Situations with Complex Event Processing

Accomplishing the second requirement — proactive analytics, rather than reactive response — requires a sophisticated technology, one example being Complex Event Processing (CEP). CEP engines, along with business policies, analyze situations or "business views" comprised of multiple events and key performance indicators.

Instead of alerts based on individual events passing a threshold, the analytical approach is analyzing situations. It compares application behavior against your norms, looking for anomalies that indicate potential problems. Norms are established dynamically using statistical functions such as Bollinger bands, momentum oscillators, standard deviation, velocity, fluctuation and rates of change.

This approach ensures that real problems — not just transient variations, a.k.a. "false alarms" — are identified and ensures true readings of real-time performance.

With CEP-based analytics, IT Specialists are assisted in quickly identifying root causes, instead of merely chasing symptoms. By dynamically analyzing event streams, the CEP approach can differentiate symptoms from cause — even inferring an explanation where there is signal loss.

APM solutions using real-time anomaly detection have the ability to maintain SLAs in the most high-demand deployments including payments, EFT, trading, settlement, compliance patient data, claims processing and retail order management. They not only bring developing situations to the attention of IT staff before users are aware, but also assist in diagnosing and correcting the underlying causes quickly and efficiently.

In an era when business functions are more sophisticated, diverse, integrated and immediate than ever, analytical Application Performance Management plays an essential role for IT professionals and their customers.

Charley Rich is VP Product Management and Marketing at Nastel Technologies.

Hot Topics

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...