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How Do You Quantify the ROI of Network Monitoring?

Dirk Paessler

Return on Investment is a tricky term. It is quite simple to take the total cost of software and amortize it over a period of time. But in the case of network monitoring, that analysis ignores what the software actually does. Put simply, network monitoring gives IT visibility and insight into their infrastructure, helping spot problems before they start, and ensuring uptime and availability. Calculating ROI for such software without acknowledging its impact would be akin to amortizing the cost of a sales enablement tool without considering if it increases sales. A more forward-looking approach that accounts for the software’s impact is necessary, but the analysis is not without its issues.

When used correctly, network monitoring software can prevent a number of problems – mail server crashes, website failures, and network downtime, among others. The benefit to users and IT is obvious, but the effect on the bottom line is more difficult to quantify. Losing email for a day affects productivity, but losing email at 9 a.m. on a Monday is different than 4 p.m. on a Friday. Similarly, a website crash is a disaster if it happens for a retailer on Cyber Monday, but is less of a problem for most other businesses.

There have been studies aimed at quantifying the costs of IT failures. In 2012, industry analyst Michael Krigsman published an article that put the total cost of IT failures on the world economy at $3 trillion per year. A Gartner study from 2014 put a finer point on the issue, stating that the average cost of network downtime is $5,600 per minute, or $300,000 an hour. While the effects of downtime and outages will be felt differently by individual businesses, these studies highlight both the need for network monitoring, and illustrate the financial case that can be made for it.

IT managers looking to make the case for network monitoring in their budgets do not need to use analyst figures or estimates. Instead, they can look at a number of local factors – including the costs of IT staffing, the average time it takes to restore failures, number of network failures in the previous year, and SLAs with various service providers. By arming themselves with data, IT leaders will have an easier time explaining to the business side about the need for network monitoring.

The budgeting process for IT grows more difficult every year. Nearly every part of the business now spends money on technology, and in some cases a great deal of the budget is shifted towards marketing and sales enablement. As IT managers are constantly asked to do more with less, they need monitoring more than ever – it keeps an eye on infrastructure when they can’t. It is imperative that IT departments do not lose out on a critical tool simply because it does not have the eye-catching appeal of the "Next Big Thing". But with hard numbers and a little common-sense thinking, IT can make the case for network monitoring successfully.

Dirk Paessler is CEO and Founder of Paessler AG.

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How Do You Quantify the ROI of Network Monitoring?

Dirk Paessler

Return on Investment is a tricky term. It is quite simple to take the total cost of software and amortize it over a period of time. But in the case of network monitoring, that analysis ignores what the software actually does. Put simply, network monitoring gives IT visibility and insight into their infrastructure, helping spot problems before they start, and ensuring uptime and availability. Calculating ROI for such software without acknowledging its impact would be akin to amortizing the cost of a sales enablement tool without considering if it increases sales. A more forward-looking approach that accounts for the software’s impact is necessary, but the analysis is not without its issues.

When used correctly, network monitoring software can prevent a number of problems – mail server crashes, website failures, and network downtime, among others. The benefit to users and IT is obvious, but the effect on the bottom line is more difficult to quantify. Losing email for a day affects productivity, but losing email at 9 a.m. on a Monday is different than 4 p.m. on a Friday. Similarly, a website crash is a disaster if it happens for a retailer on Cyber Monday, but is less of a problem for most other businesses.

There have been studies aimed at quantifying the costs of IT failures. In 2012, industry analyst Michael Krigsman published an article that put the total cost of IT failures on the world economy at $3 trillion per year. A Gartner study from 2014 put a finer point on the issue, stating that the average cost of network downtime is $5,600 per minute, or $300,000 an hour. While the effects of downtime and outages will be felt differently by individual businesses, these studies highlight both the need for network monitoring, and illustrate the financial case that can be made for it.

IT managers looking to make the case for network monitoring in their budgets do not need to use analyst figures or estimates. Instead, they can look at a number of local factors – including the costs of IT staffing, the average time it takes to restore failures, number of network failures in the previous year, and SLAs with various service providers. By arming themselves with data, IT leaders will have an easier time explaining to the business side about the need for network monitoring.

The budgeting process for IT grows more difficult every year. Nearly every part of the business now spends money on technology, and in some cases a great deal of the budget is shifted towards marketing and sales enablement. As IT managers are constantly asked to do more with less, they need monitoring more than ever – it keeps an eye on infrastructure when they can’t. It is imperative that IT departments do not lose out on a critical tool simply because it does not have the eye-catching appeal of the "Next Big Thing". But with hard numbers and a little common-sense thinking, IT can make the case for network monitoring successfully.

Dirk Paessler is CEO and Founder of Paessler AG.

Hot Topics

The Latest

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...