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How to Detect (and Resolve) IT Ops/APM Issues Before Your Users Do

Kevin Conklin

Among the most embarrassing situations for application support teams is first hearing about a critical performance issue from their users. With technology getting increasingly complex and IT environments changing almost overnight, the reality is that even the most experienced support teams are bound to miss a major problem with a critical application or service. One of the contributing factors is their continued reliance on traditional monitoring approaches.

Traditional tools limit us to monitoring for a combination of key performance indicator thresholds and failure modes that have already been experienced. So when it comes to finding new problems, the best case is alerts that describe the symptom (slow response time, transaction fails, etc.). A very experienced IT professional will have seen many behaviors, and consequently can employ monitoring based on best practices and past experiences. But even the most experienced IT professional will have a hard time designing rules and thresholds that can monitor for new, unknown problems without generating a number of noisy false alerts. Anomaly detection goes beyond the limits of traditional approaches because it sees and learns everything in the data provided, whether it has happened before or not.

Anomaly detection works by identifying unusual behaviors in data generated by an application or service delivery environment. The technology uses machine learning predictive analytics to establish baselines in the data and automatically learn what normal behavior is. The technology then identifies deviations in behavior that are unusually severe or maybe causal to other anomalies – a clear indication that something is wrong. And the best part? This technology works in real-time as well as in troubleshooting mode, so it's proactively monitoring your IT environment. With this approach, real problems can be identified and acted upon faster than before.

More advanced anomaly detection technologies can run multiple analyses in parallel, and are capable of analyzing multiple data sources simultaneously, identifying related, anomalous relationships within the system. Thus, when a chain of events is causal to a performance issue, the alerts contain all the related anomalies. This helps support teams zero in on the cause of the problem immediately.

Traditional approaches are also known to generate huge volumes of false alerts. Anomaly detection, on the other hand, uses advanced statistical analyses to minimize false alerts. Those few alerts that are generated provide more data, which results in faster troubleshooting.

Anomaly detection looks for significant variations from the norm and ranks severity by probability. Machine learning technology helps the system learn the difference between commonly occurring errors as well as spikes and drops in metrics, and true anomalies that are more accurate indicators of a problem. This can mean the difference between tens of thousands of alerts each day, most of which are false, and a dozen or so a week that should be pursued.

Anomaly detection can identify the early signs of developing problems in massive volumes of data before they turn into real, big problems. Enabling IT teams to slash troubleshooting time and decrease the noise from false alarms empowers them to attack and resolve any issues before they reach critical proportions.

If users do become aware of a problem, the IT team can respond "we're on it" instead of saying "thanks for letting us know."

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How to Detect (and Resolve) IT Ops/APM Issues Before Your Users Do

Kevin Conklin

Among the most embarrassing situations for application support teams is first hearing about a critical performance issue from their users. With technology getting increasingly complex and IT environments changing almost overnight, the reality is that even the most experienced support teams are bound to miss a major problem with a critical application or service. One of the contributing factors is their continued reliance on traditional monitoring approaches.

Traditional tools limit us to monitoring for a combination of key performance indicator thresholds and failure modes that have already been experienced. So when it comes to finding new problems, the best case is alerts that describe the symptom (slow response time, transaction fails, etc.). A very experienced IT professional will have seen many behaviors, and consequently can employ monitoring based on best practices and past experiences. But even the most experienced IT professional will have a hard time designing rules and thresholds that can monitor for new, unknown problems without generating a number of noisy false alerts. Anomaly detection goes beyond the limits of traditional approaches because it sees and learns everything in the data provided, whether it has happened before or not.

Anomaly detection works by identifying unusual behaviors in data generated by an application or service delivery environment. The technology uses machine learning predictive analytics to establish baselines in the data and automatically learn what normal behavior is. The technology then identifies deviations in behavior that are unusually severe or maybe causal to other anomalies – a clear indication that something is wrong. And the best part? This technology works in real-time as well as in troubleshooting mode, so it's proactively monitoring your IT environment. With this approach, real problems can be identified and acted upon faster than before.

More advanced anomaly detection technologies can run multiple analyses in parallel, and are capable of analyzing multiple data sources simultaneously, identifying related, anomalous relationships within the system. Thus, when a chain of events is causal to a performance issue, the alerts contain all the related anomalies. This helps support teams zero in on the cause of the problem immediately.

Traditional approaches are also known to generate huge volumes of false alerts. Anomaly detection, on the other hand, uses advanced statistical analyses to minimize false alerts. Those few alerts that are generated provide more data, which results in faster troubleshooting.

Anomaly detection looks for significant variations from the norm and ranks severity by probability. Machine learning technology helps the system learn the difference between commonly occurring errors as well as spikes and drops in metrics, and true anomalies that are more accurate indicators of a problem. This can mean the difference between tens of thousands of alerts each day, most of which are false, and a dozen or so a week that should be pursued.

Anomaly detection can identify the early signs of developing problems in massive volumes of data before they turn into real, big problems. Enabling IT teams to slash troubleshooting time and decrease the noise from false alarms empowers them to attack and resolve any issues before they reach critical proportions.

If users do become aware of a problem, the IT team can respond "we're on it" instead of saying "thanks for letting us know."

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...