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Optimizing ERP Application Performance in an Increasingly Complex Delivery Environment

Kieran Taylor

According to industry statistics, the average costs of downtime for a leading ERP system can range between $535,780 and $838,100 per hour. Put another way, almost $15,000 is lost every minute an ERP application is down. And that’s just the tip of the iceberg, because poor application performance also exposes businesses to a wide range of risks including lost competitive edge, wasted resources, tarnished brand image, reduced customer satisfaction, increased financial and legal scrutiny and non-compliance.

In essence, the health of ERP application performance is a proxy for business health, and fast, reliable applications have never been more important. However, the increased complexity within modern application delivery environments makes it very difficult to ensure strong performance. As a result, many applications supporting businesses today are running at less than optimal levels, putting expensive and highly-visible ERP investments on the line.

Business-critical ERP applications depend on a wide range of data center components working together, including databases, operating systems, servers, networks, storage, management tools and back-up software. Within this complex environment there are many potential points of failure and performance degradation. More traditional approaches to managing application performance often measure components like database efficiency, and other likely problem spots like the network. But what they don’t demonstrate is the end-to-end performance of business transactions.

So how can enterprises ensure high-performing ERP applications today? First, businesses must flip the problem diagnosis paradigm. It’s no longer sufficient to look just for opportunities to optimize different components without an understanding of how these improvements directly translate to an improved end-user experience.

Instead, businesses must proactively gain an understanding of the end-user experience; then, they can trace back to all the different elements to identify where bottlenecks are and what should be changed in order to resolve them.

This approach helps businesses be proactive in preventing end-user complaints from arriving at the help desk, when it’s likely too late and the damage may already be done.

This approach also helps organizations to pinpoint the source of existing and potential performance problems quickly. To this end, businesses must also monitor all transactions all the time.

Sampling is not sufficient because there’s no guarantee that a performance problem will occur during a sampling interval, especially in this age of mobile devices when end users are accessing applications all the time.

Second, businesses must have a consolidated view of all the variables impacting ERP application performance, from the end users’s browser, across the network, through the data center and into the integrated subsystems. This is known as having a complete view across the ERP application delivery chain, and it’s the key to having more control over it. Once a business understands the end-user experience and the complete picture supporting it, they can then more effectively identify areas for acceleration that will result in faster transactions.

No doubt, today’s complex delivery environments make it more challenging than ever to ensure strong application performance. The good news is that new approaches to application performance management (APM), including focusing on end-user transaction performance, consolidating all application delivery chain variables in a “single pane of glass” approach and monitoring all applications 24x7, can make it easier to ensure high performance, quickly and cost-effectively.

Kieran Taylor is Sr Director, Product & Solutions Marketing, APM & DevOps, CA Technologies .

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Optimizing ERP Application Performance in an Increasingly Complex Delivery Environment

Kieran Taylor

According to industry statistics, the average costs of downtime for a leading ERP system can range between $535,780 and $838,100 per hour. Put another way, almost $15,000 is lost every minute an ERP application is down. And that’s just the tip of the iceberg, because poor application performance also exposes businesses to a wide range of risks including lost competitive edge, wasted resources, tarnished brand image, reduced customer satisfaction, increased financial and legal scrutiny and non-compliance.

In essence, the health of ERP application performance is a proxy for business health, and fast, reliable applications have never been more important. However, the increased complexity within modern application delivery environments makes it very difficult to ensure strong performance. As a result, many applications supporting businesses today are running at less than optimal levels, putting expensive and highly-visible ERP investments on the line.

Business-critical ERP applications depend on a wide range of data center components working together, including databases, operating systems, servers, networks, storage, management tools and back-up software. Within this complex environment there are many potential points of failure and performance degradation. More traditional approaches to managing application performance often measure components like database efficiency, and other likely problem spots like the network. But what they don’t demonstrate is the end-to-end performance of business transactions.

So how can enterprises ensure high-performing ERP applications today? First, businesses must flip the problem diagnosis paradigm. It’s no longer sufficient to look just for opportunities to optimize different components without an understanding of how these improvements directly translate to an improved end-user experience.

Instead, businesses must proactively gain an understanding of the end-user experience; then, they can trace back to all the different elements to identify where bottlenecks are and what should be changed in order to resolve them.

This approach helps businesses be proactive in preventing end-user complaints from arriving at the help desk, when it’s likely too late and the damage may already be done.

This approach also helps organizations to pinpoint the source of existing and potential performance problems quickly. To this end, businesses must also monitor all transactions all the time.

Sampling is not sufficient because there’s no guarantee that a performance problem will occur during a sampling interval, especially in this age of mobile devices when end users are accessing applications all the time.

Second, businesses must have a consolidated view of all the variables impacting ERP application performance, from the end users’s browser, across the network, through the data center and into the integrated subsystems. This is known as having a complete view across the ERP application delivery chain, and it’s the key to having more control over it. Once a business understands the end-user experience and the complete picture supporting it, they can then more effectively identify areas for acceleration that will result in faster transactions.

No doubt, today’s complex delivery environments make it more challenging than ever to ensure strong application performance. The good news is that new approaches to application performance management (APM), including focusing on end-user transaction performance, consolidating all application delivery chain variables in a “single pane of glass” approach and monitoring all applications 24x7, can make it easier to ensure high performance, quickly and cost-effectively.

Kieran Taylor is Sr Director, Product & Solutions Marketing, APM & DevOps, CA Technologies .

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...