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How to Ensure APM Success

Keith Bromley

A recent APMdigest blog by Jean Tunis, The Evolving Needs of Application Performance Monitoring - Part 2, provided an excellent background on Application Performance Monitoring (APM) and what it does. APM solution benefits are much more understood than in years past. An interesting data point from Gartner Inc. mentioned in the article confirms this, stating that IT departments are planning to increase the use of APM solutions to monitor their applications from 5% in 2018 to a projected 20% in 2021.

A further topic that I wanted to touch on though is the need for good quality data. If you are to get the most out of your APM solution possible, you will need to feed it with the best quality data. Irrelevant data, fragmented data, and corrupt data are all common culprits that either end up decreasing the speed to resolution, or prevent problem resolution altogether, by APM solutions.

There are two easy activities you can conduct to increase the quality of the input data to your APM tool. First, install taps to collect monitoring data. Taps can be installed anywhere across your network. This lets you collect ingress/egress traffic to your network, data to/from remote branch offices, and data from anywhere across the network that you think might be experiencing some sort of issue.

Taps deliver the ultimate experience in flexibility. In contrast, SPAN and mirroring ports off of your Layer 2 and 3 switches do not have that same flexibility. For instance, placing switches all over your network to capture data is unnecessary and expensive. In addition, mirroring ports can drop data, especially in CPU overload situations. When it comes to troubleshooting and performance monitoring, you need every piece of relevant data, not just portions of relevant data.

Secondly, you need to deploy a network packet broker (NPB) in your network. The function of the NPB is to aggregate monitoring data from across your network, filter that data based upon the criteria you are looking for, and remove unnecessary, duplicate copies of the data. Once this is accomplished, the NPB forwards the data onto your APM solution. The NPB may reduce the traffic sent to your APM solution by 50% or more; making your APM solution that much more effective and potentially reduce your future APM tool costs.

Something else to consider is that the tap and NPB concept can be used in cloud solutions as well. This means you can deploy the concept for both physical on-premises and virtual network. This is especially important for hybrid cloud (mixture of physical on-premises and public/private cloud) scenarios that are prevalent in today’s enterprise networks. This mixture of different network types can be a significant problem that is easily remedied with a tap, virtual tap, and NPB approach.

In the end, APM solutions are a critical component to troubleshooting and performance monitoring, but you need to make sure that the APM solution is getting the right data.

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

How to Ensure APM Success

Keith Bromley

A recent APMdigest blog by Jean Tunis, The Evolving Needs of Application Performance Monitoring - Part 2, provided an excellent background on Application Performance Monitoring (APM) and what it does. APM solution benefits are much more understood than in years past. An interesting data point from Gartner Inc. mentioned in the article confirms this, stating that IT departments are planning to increase the use of APM solutions to monitor their applications from 5% in 2018 to a projected 20% in 2021.

A further topic that I wanted to touch on though is the need for good quality data. If you are to get the most out of your APM solution possible, you will need to feed it with the best quality data. Irrelevant data, fragmented data, and corrupt data are all common culprits that either end up decreasing the speed to resolution, or prevent problem resolution altogether, by APM solutions.

There are two easy activities you can conduct to increase the quality of the input data to your APM tool. First, install taps to collect monitoring data. Taps can be installed anywhere across your network. This lets you collect ingress/egress traffic to your network, data to/from remote branch offices, and data from anywhere across the network that you think might be experiencing some sort of issue.

Taps deliver the ultimate experience in flexibility. In contrast, SPAN and mirroring ports off of your Layer 2 and 3 switches do not have that same flexibility. For instance, placing switches all over your network to capture data is unnecessary and expensive. In addition, mirroring ports can drop data, especially in CPU overload situations. When it comes to troubleshooting and performance monitoring, you need every piece of relevant data, not just portions of relevant data.

Secondly, you need to deploy a network packet broker (NPB) in your network. The function of the NPB is to aggregate monitoring data from across your network, filter that data based upon the criteria you are looking for, and remove unnecessary, duplicate copies of the data. Once this is accomplished, the NPB forwards the data onto your APM solution. The NPB may reduce the traffic sent to your APM solution by 50% or more; making your APM solution that much more effective and potentially reduce your future APM tool costs.

Something else to consider is that the tap and NPB concept can be used in cloud solutions as well. This means you can deploy the concept for both physical on-premises and virtual network. This is especially important for hybrid cloud (mixture of physical on-premises and public/private cloud) scenarios that are prevalent in today’s enterprise networks. This mixture of different network types can be a significant problem that is easily remedied with a tap, virtual tap, and NPB approach.

In the end, APM solutions are a critical component to troubleshooting and performance monitoring, but you need to make sure that the APM solution is getting the right data.

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...