Skip to main content

10 Application Monitoring Tips

Jay Labadini

Your applications should ensure end-user satisfaction and boost productivity for employees and partners. Therefore, IT pros implementing or monitoring applications should take the time to understand how end-users interact with their application, share the proper amount of information with the right stakeholders, implement the right workflows and ensure they are performing top-notch.

Here are 10 quick tips to help you get started.

Tip 1: Prioritize which applications should be monitored first

With a growing number of employees bypassing IT and going rogue to the cloud, it's anarchy out there. Plus counting legacy applications, Citrix and Terminal server hosted apps, CRM, EHR, custom-built applications, accounting, invoicing, HR, email and collaboration tools, the list of applications your employees, partners or customers rely on (and you support) is long.

Your applications fuel your business, so they must consistently perform well, and ultra-fast. Since you have to start somewhere, identify those critical applications that must perform well in order to run your business (e.g. applications migrated to the cloud, CRM, ERP, EHR systems), and monitor them first. You know better than anybody else what is critical to your business and users.

Tip 2: Identify critical transactions to monitor

Put on your "think from an end-user perspective hat" and map out common functions used by your power users (e.g. those using your applications the most, those driving the most revenue, upper management, etc.). Or better yet, schedule a meeting with your business counterparts, management and stakeholders to identify critical functionality from their perspective.

If you recently went through the process of implementing a new application, you should have your workflows already mapped, right? As you document critical transaction paths or workflows for your application users, this is a great time to fine tune your processes and minimize the number of steps needed for common functions.

Tip 3: Proactively monitor your applications from an end-user perspective

End-users are more impatient than ever before. Therefore, you should continuously monitor each one of these critical transactions (or workflows) from a user perspective, taking response time measurements for each step to ensure user SLAs are met.

It is unacceptable that in 35% of cases IT learns that there is an issue when a user opens a helpdesk ticket or calls to complain (Source: Forrester Research). Change the game and get ahead; find and resolve bottlenecks, errors and constraints, problems before your users are impacted.

Tip 4: Decide polling frequencies and alerting policies

A good rule of thumb is to monitor key transactions more frequently (e.g. being able to send a sales proposal is more critical than reporting on sales pipeline, or being able to sell online is more important that reading a product review) to identify performance degradation signs earlier.

Take the time to define who should be alerted in the event of specific threshold violations, and configure the number of response time violations that will trigger an alert to eliminate false positives and alert storms.

Don't forget to look for key monitoring functionality like scheduling monitoring tests or disable alerting on scheduled maintenance periods or when you are on vacation. You should be in control of your monitoring.

Tip 5: Identify geographical response time discrepancies early on

Employees at remote offices could experience slower response times than those accessing your applications from headquarters; legacy applications could underperform for some offices or branches. Get ahead of user complaints. The faster way to find and resolve problems like this is to monitor and compare availability and response time of your applications across multiple monitoring locations (Headquarters, Boston, NYC, remote office locations, etc.).

Tip 6: Define your custom reports

Since different metrics are important for different stakeholders, take the time to map out role-based reports with custom information for each team (per application, per transaction, per functionality, etc.), and automatically distribute reports on an on-going basis (daily, weekly or monthly basis) to keep everybody informed and aligned.

Tip 7: Centralize IT response procedures and workflow

From legacy applications, to client server applications, to web applications, to home-grown custom applications, cloud-based or green screen apps, most large enterprises have a complex portfolio with 250-500 applications to support. The cost of purchasing, configuring and maintaining several monitoring products to support individual applications is too high.

Plus lack of integration across monitoring consoles results in islands of uncorrelated information which leads to wrong conclusions, hinders troubleshooting and increases Mean-Time-To-Resolution (MTTR).

Instead, look for one solution that lets you test and monitor all applications, so you can quickly identify problem root cause.

Tip 8: Keep everybody in the loop

In a new era where end-user satisfaction rules, you need to continuously validate and demonstrate your SLAs, so go ahead and periodically share your SLA reports with your users and stakeholders. Provide a quick summary dashboard with a drill-in so that they don't have to peruse voluminous reports.

Plus since user satisfaction is the ultimate measurement of IT success (your success), this is the best metric to promote the value that IT provides to your organization.

Tip 9: Review results on an on-going basis

Do you need to fine-tune? Do you need to optimize application performance? With a metric-driven strategy in place you can keep all stakeholders in the know, and take informed business decisions that directly impact your bottom line (e.g. quickly ascertain if you need to focus on performance optimization or not, change cloud providers, etc.).

Tip 10: Ensure quality

Build a culture where application quality is not an afterthought. You should include testing (functional testing, regression testing, performance testing, load testing) in all application development/application implementation cycles right from the beginning to ensure quality. Being able to reuse your test scripts for production monitoring will also help streamline your processes.

In summary, your end-users have the last word on whether they are satisfied with the speed, availability and performance of your applications, so implement, test and monitor your applications from your end-users' perspective.

And don't forget your mobile users. Smart devices are not only competing for PCs' place in your users' lives, or in the enterprise – they are replacing the experience. In fact, the amount of time users spend browsing the Web on their mobile devices is trouncing desktops (Source: The Wall Street Journal). And mobile user expectations are on par with, if not higher than, their desktop counterparts. Therefore, look for SLA application monitoring for both mobile and desktop users. Good luck!

Jay Labadini is a VP and Co-Founder of Tevron.

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

10 Application Monitoring Tips

Jay Labadini

Your applications should ensure end-user satisfaction and boost productivity for employees and partners. Therefore, IT pros implementing or monitoring applications should take the time to understand how end-users interact with their application, share the proper amount of information with the right stakeholders, implement the right workflows and ensure they are performing top-notch.

Here are 10 quick tips to help you get started.

Tip 1: Prioritize which applications should be monitored first

With a growing number of employees bypassing IT and going rogue to the cloud, it's anarchy out there. Plus counting legacy applications, Citrix and Terminal server hosted apps, CRM, EHR, custom-built applications, accounting, invoicing, HR, email and collaboration tools, the list of applications your employees, partners or customers rely on (and you support) is long.

Your applications fuel your business, so they must consistently perform well, and ultra-fast. Since you have to start somewhere, identify those critical applications that must perform well in order to run your business (e.g. applications migrated to the cloud, CRM, ERP, EHR systems), and monitor them first. You know better than anybody else what is critical to your business and users.

Tip 2: Identify critical transactions to monitor

Put on your "think from an end-user perspective hat" and map out common functions used by your power users (e.g. those using your applications the most, those driving the most revenue, upper management, etc.). Or better yet, schedule a meeting with your business counterparts, management and stakeholders to identify critical functionality from their perspective.

If you recently went through the process of implementing a new application, you should have your workflows already mapped, right? As you document critical transaction paths or workflows for your application users, this is a great time to fine tune your processes and minimize the number of steps needed for common functions.

Tip 3: Proactively monitor your applications from an end-user perspective

End-users are more impatient than ever before. Therefore, you should continuously monitor each one of these critical transactions (or workflows) from a user perspective, taking response time measurements for each step to ensure user SLAs are met.

It is unacceptable that in 35% of cases IT learns that there is an issue when a user opens a helpdesk ticket or calls to complain (Source: Forrester Research). Change the game and get ahead; find and resolve bottlenecks, errors and constraints, problems before your users are impacted.

Tip 4: Decide polling frequencies and alerting policies

A good rule of thumb is to monitor key transactions more frequently (e.g. being able to send a sales proposal is more critical than reporting on sales pipeline, or being able to sell online is more important that reading a product review) to identify performance degradation signs earlier.

Take the time to define who should be alerted in the event of specific threshold violations, and configure the number of response time violations that will trigger an alert to eliminate false positives and alert storms.

Don't forget to look for key monitoring functionality like scheduling monitoring tests or disable alerting on scheduled maintenance periods or when you are on vacation. You should be in control of your monitoring.

Tip 5: Identify geographical response time discrepancies early on

Employees at remote offices could experience slower response times than those accessing your applications from headquarters; legacy applications could underperform for some offices or branches. Get ahead of user complaints. The faster way to find and resolve problems like this is to monitor and compare availability and response time of your applications across multiple monitoring locations (Headquarters, Boston, NYC, remote office locations, etc.).

Tip 6: Define your custom reports

Since different metrics are important for different stakeholders, take the time to map out role-based reports with custom information for each team (per application, per transaction, per functionality, etc.), and automatically distribute reports on an on-going basis (daily, weekly or monthly basis) to keep everybody informed and aligned.

Tip 7: Centralize IT response procedures and workflow

From legacy applications, to client server applications, to web applications, to home-grown custom applications, cloud-based or green screen apps, most large enterprises have a complex portfolio with 250-500 applications to support. The cost of purchasing, configuring and maintaining several monitoring products to support individual applications is too high.

Plus lack of integration across monitoring consoles results in islands of uncorrelated information which leads to wrong conclusions, hinders troubleshooting and increases Mean-Time-To-Resolution (MTTR).

Instead, look for one solution that lets you test and monitor all applications, so you can quickly identify problem root cause.

Tip 8: Keep everybody in the loop

In a new era where end-user satisfaction rules, you need to continuously validate and demonstrate your SLAs, so go ahead and periodically share your SLA reports with your users and stakeholders. Provide a quick summary dashboard with a drill-in so that they don't have to peruse voluminous reports.

Plus since user satisfaction is the ultimate measurement of IT success (your success), this is the best metric to promote the value that IT provides to your organization.

Tip 9: Review results on an on-going basis

Do you need to fine-tune? Do you need to optimize application performance? With a metric-driven strategy in place you can keep all stakeholders in the know, and take informed business decisions that directly impact your bottom line (e.g. quickly ascertain if you need to focus on performance optimization or not, change cloud providers, etc.).

Tip 10: Ensure quality

Build a culture where application quality is not an afterthought. You should include testing (functional testing, regression testing, performance testing, load testing) in all application development/application implementation cycles right from the beginning to ensure quality. Being able to reuse your test scripts for production monitoring will also help streamline your processes.

In summary, your end-users have the last word on whether they are satisfied with the speed, availability and performance of your applications, so implement, test and monitor your applications from your end-users' perspective.

And don't forget your mobile users. Smart devices are not only competing for PCs' place in your users' lives, or in the enterprise – they are replacing the experience. In fact, the amount of time users spend browsing the Web on their mobile devices is trouncing desktops (Source: The Wall Street Journal). And mobile user expectations are on par with, if not higher than, their desktop counterparts. Therefore, look for SLA application monitoring for both mobile and desktop users. Good luck!

Jay Labadini is a VP and Co-Founder of Tevron.

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...