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It's Not Downtime We Should be Worried About - It's Uptime

Ivar Sagemo

Here's an eight-letter word, twice as bad as any four-letter word, that no business leader wants to hear: downtime. Today's businesses are far more dependent on IT services than ever, and that's true whether you're talking about internal IT services (like ERP) used to drive strategic operations, or external IT services used to satisfy client and customer demand.

Among the more daunting long-term potential consequences of downtime to the organization are these:

• Lost revenues because business couldn't be transacted. A recent Ponemon study tells us the average cost of downtime for US-based organizations is a stunning $5600/minute.

• Diminished brand strength, because the company is seen as unreliable. The same study suggests the average length of downtime was 90 minutes, leading to roughly $500K of costs per incident.

• Evaporating market share, because unhappy customers go to competitors.

Quite a mess in short. And it's a mess that's rapidly getting bigger. Aberdeen Group found that between 2010 and 2012, the cost per hour of downtime climbed an average of 65%!

Now, all of this is obvious in my own area of application performance monitoring (APM), and while downtime is a problem, there is a bigger issue, more subtle lurking just beneath the surface. When we believe everything is running smoothly but don't know something is wrong, that’s when the most damage happens.

For instance, suppose a BizTalk-based service is up and running in a holistic sense, but operating in a subtly inconsistent manner — difficult to detect — that leads to lost transactions from time to time. By this I mean occasionally lost e-mails, lost database entries, lost purchase orders, etc. Time spent by customers resending email and clients waiting or employees spending time looking for an invoice that has not come through – all of these cause more loss in productivity and reputation over a longer period of time.

According to Pricewaterhouse Coopers, the average organization, spends $120 searching for a lost document and wastes 25 hours recreating each lost document.* But what we don't know is how much productivity is lost through not knowing when a problem exists, searching for a file that is not lost at all. Waiting on document resends, searching for "missing" invoices or customer relationships that need to be repaired due to an apparent miscommunication because information is not flowing smoothly in a system all impact company efficiency and costs.

Application performance, and service uptime, can be affected by myriad factors — some as subtle as a gradual shortage of key computational resources. That's why it's important to find a way to granularly monitor your system. To have control and visibility over the problems that are happening so you can decide which ones to tackle is key.

Over time, I think we're going to see that kind of granular insight play a larger and larger role in APM as a field. But in the meantime, it's important to have a clear view of the information that flows throughout your organization so you can see any problem lurking out of sight.

Ivar Sagemo is CEO of AIMS Innovation.

Related Links:

www.aimsinnovation.com

* DocuSense Blog: How Much are Lost Documents Costing You?

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It's Not Downtime We Should be Worried About - It's Uptime

Ivar Sagemo

Here's an eight-letter word, twice as bad as any four-letter word, that no business leader wants to hear: downtime. Today's businesses are far more dependent on IT services than ever, and that's true whether you're talking about internal IT services (like ERP) used to drive strategic operations, or external IT services used to satisfy client and customer demand.

Among the more daunting long-term potential consequences of downtime to the organization are these:

• Lost revenues because business couldn't be transacted. A recent Ponemon study tells us the average cost of downtime for US-based organizations is a stunning $5600/minute.

• Diminished brand strength, because the company is seen as unreliable. The same study suggests the average length of downtime was 90 minutes, leading to roughly $500K of costs per incident.

• Evaporating market share, because unhappy customers go to competitors.

Quite a mess in short. And it's a mess that's rapidly getting bigger. Aberdeen Group found that between 2010 and 2012, the cost per hour of downtime climbed an average of 65%!

Now, all of this is obvious in my own area of application performance monitoring (APM), and while downtime is a problem, there is a bigger issue, more subtle lurking just beneath the surface. When we believe everything is running smoothly but don't know something is wrong, that’s when the most damage happens.

For instance, suppose a BizTalk-based service is up and running in a holistic sense, but operating in a subtly inconsistent manner — difficult to detect — that leads to lost transactions from time to time. By this I mean occasionally lost e-mails, lost database entries, lost purchase orders, etc. Time spent by customers resending email and clients waiting or employees spending time looking for an invoice that has not come through – all of these cause more loss in productivity and reputation over a longer period of time.

According to Pricewaterhouse Coopers, the average organization, spends $120 searching for a lost document and wastes 25 hours recreating each lost document.* But what we don't know is how much productivity is lost through not knowing when a problem exists, searching for a file that is not lost at all. Waiting on document resends, searching for "missing" invoices or customer relationships that need to be repaired due to an apparent miscommunication because information is not flowing smoothly in a system all impact company efficiency and costs.

Application performance, and service uptime, can be affected by myriad factors — some as subtle as a gradual shortage of key computational resources. That's why it's important to find a way to granularly monitor your system. To have control and visibility over the problems that are happening so you can decide which ones to tackle is key.

Over time, I think we're going to see that kind of granular insight play a larger and larger role in APM as a field. But in the meantime, it's important to have a clear view of the information that flows throughout your organization so you can see any problem lurking out of sight.

Ivar Sagemo is CEO of AIMS Innovation.

Related Links:

www.aimsinnovation.com

* DocuSense Blog: How Much are Lost Documents Costing You?

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

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 ...