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3 Keys to Preventing Poor Application Performance from Damaging Your Business

Jay Botelho

In today's business landscape, digital transformation is imperative to success. According to Gartner, 87% of senior business leaders report it as a top priority. Even in the midst of a worldwide health crisis, as 52% of companies report planning to cancel or defer investments, just 9% are planning to make those cuts to digital transformation initiatives. This level of commitment makes sense, since digital-first companies are 64% more likely than their peers to exceed their business goals, according to a 2019 Adobe report. Applications are the fundamental drivers behind digital business models, supporting everything from core business processes and transactions, to service delivery and collaboration.

When application performance declines, your business operations slow, revenue generating transactions fail, users discard and circumvent critical applications, employee productivity drops, and customer experiences and retention wane. For instance, when one live events company expanded its ticketing, concert promotion, and venue operation business across 37 countries to serve 530 million users, 26,000 annual events, and 75 festivals, the organization experienced performance problems when tickets for especially popular acts went on sale, creating serious customer satisfaction issues. And as the COVID-19 pandemic drove increasing demand for live concert streaming , video and audio quality and reliability issues surfaced that had to be addressed to maintain the brand.

Another example is healthcare workers who rely on wireless tags or badges to send emergency alerts. When performance issues turn 1-2 second response times into 3-4 minute waits, patient care and outcomes suffer. In short, failing to support and optimize application performance can stop your business in its tracks, or worse.

All application traffic travels across the network. While application performance management tools can offer insight into how critical applications are functioning, they do not provide visibility into the broader network environment. Without this piece of the puzzle, application operation teams can't tell if poor performance is due to inefficient network traffic patterns that cause latency, or bottlenecks, packet drops and jitter on the network, etc. For digital enterprises today, this type of blind spot is simply unacceptable.

Fortunately, some network performance management and diagnostics (NPMD) solutions today provide application intelligence that allows network operations (NetOps) teams to understand the correlation between network performance and application performance. This can help break down siloes between network managers and application teams and ensure critical applications can reliably support business operations.

In order to optimize application performance, you need a few key capabilities. Let's explore three steps that can help NetOps teams better support the critical applications upon which your business depends:

1. Establishing Effective Application Visibility

To gain a full picture of application performance, especially when performance is degraded, you need actual network traffic data, not simulated data. You must be able to access and review data from network flow record protocols (such as IPFIX and NetFlow v9), which support flow record extensions that provide key metadata such as NBAR and AVC. Most importantly, you need a platform that can collect this data across every domain across your entire network. This will provide the end-to-end visibility you need to plot out global traffic flows with application context.

2. Evaluating Application Performance

You need deep insights into several types of network data in order to successfully assess and understand application performance. Network flows with IPFIX or NetFlow extensions are helpful because they can provide application performance-specific reporting. IPSLA and agent-based synthetic monitoring solutions can test the health and performance of application traffic paths. Deep packet inspection (DPI) can give you in-depth insight into application traffic, providing the ultimate truth about what's happening on the network and how critical applications are performing. Some infrastructure vendors even embed DPI metadata in extensible flow records. The key to assessing application performance lies in your ability to collect, correlate and analyze all these disparate data types.

3. Properly Optimizing the Network

After establishing the necessary visibility into application performance and equipping your team to effectively analyze it, the next step is to push changes that optimize your network to support optimal application performance. Some AIOps-driven NPMD solutions can intelligently recommend the appropriate actions. Machine learning, big data, and predictive analytics technology can reveal how the network is impacting application performance and how changes can resolve potential problems.

For instance, automated capacity management capabilities can highlight potential capacity issues that will impact application performance (such as in the example of the live events company above) and suggest changes you can make to the network to address them (such as prioritizing business-critical applications over recreational applications to ensure the most important traffic is delivered with the best quality). These tools should have the ability to reconfigure the network, leveraging SNMP or integrations with network element management systems to adjust quality of service (QoS) settings. They can also integrate with an SD-WAN platform to adjust policies and QoS settings.

Today's digital businesses must ensure that users have an expected level of performance when working with various applications. Poor application performance can negatively impact employee productivity, product and service functionality, customer satisfaction, and inevitably, the bottom line. Your network administrators and application teams need simplified, comprehensive visibility across your entire network infrastructure, as well as the business critical applications that rely on it.

Leverage the above three best practices to ensure you have the insights needed to identify and resolve potential issues proactively, reduce management costs, and verify that your network and applications are always able to meet business objectives.

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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 ...

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This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

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3 Keys to Preventing Poor Application Performance from Damaging Your Business

Jay Botelho

In today's business landscape, digital transformation is imperative to success. According to Gartner, 87% of senior business leaders report it as a top priority. Even in the midst of a worldwide health crisis, as 52% of companies report planning to cancel or defer investments, just 9% are planning to make those cuts to digital transformation initiatives. This level of commitment makes sense, since digital-first companies are 64% more likely than their peers to exceed their business goals, according to a 2019 Adobe report. Applications are the fundamental drivers behind digital business models, supporting everything from core business processes and transactions, to service delivery and collaboration.

When application performance declines, your business operations slow, revenue generating transactions fail, users discard and circumvent critical applications, employee productivity drops, and customer experiences and retention wane. For instance, when one live events company expanded its ticketing, concert promotion, and venue operation business across 37 countries to serve 530 million users, 26,000 annual events, and 75 festivals, the organization experienced performance problems when tickets for especially popular acts went on sale, creating serious customer satisfaction issues. And as the COVID-19 pandemic drove increasing demand for live concert streaming , video and audio quality and reliability issues surfaced that had to be addressed to maintain the brand.

Another example is healthcare workers who rely on wireless tags or badges to send emergency alerts. When performance issues turn 1-2 second response times into 3-4 minute waits, patient care and outcomes suffer. In short, failing to support and optimize application performance can stop your business in its tracks, or worse.

All application traffic travels across the network. While application performance management tools can offer insight into how critical applications are functioning, they do not provide visibility into the broader network environment. Without this piece of the puzzle, application operation teams can't tell if poor performance is due to inefficient network traffic patterns that cause latency, or bottlenecks, packet drops and jitter on the network, etc. For digital enterprises today, this type of blind spot is simply unacceptable.

Fortunately, some network performance management and diagnostics (NPMD) solutions today provide application intelligence that allows network operations (NetOps) teams to understand the correlation between network performance and application performance. This can help break down siloes between network managers and application teams and ensure critical applications can reliably support business operations.

In order to optimize application performance, you need a few key capabilities. Let's explore three steps that can help NetOps teams better support the critical applications upon which your business depends:

1. Establishing Effective Application Visibility

To gain a full picture of application performance, especially when performance is degraded, you need actual network traffic data, not simulated data. You must be able to access and review data from network flow record protocols (such as IPFIX and NetFlow v9), which support flow record extensions that provide key metadata such as NBAR and AVC. Most importantly, you need a platform that can collect this data across every domain across your entire network. This will provide the end-to-end visibility you need to plot out global traffic flows with application context.

2. Evaluating Application Performance

You need deep insights into several types of network data in order to successfully assess and understand application performance. Network flows with IPFIX or NetFlow extensions are helpful because they can provide application performance-specific reporting. IPSLA and agent-based synthetic monitoring solutions can test the health and performance of application traffic paths. Deep packet inspection (DPI) can give you in-depth insight into application traffic, providing the ultimate truth about what's happening on the network and how critical applications are performing. Some infrastructure vendors even embed DPI metadata in extensible flow records. The key to assessing application performance lies in your ability to collect, correlate and analyze all these disparate data types.

3. Properly Optimizing the Network

After establishing the necessary visibility into application performance and equipping your team to effectively analyze it, the next step is to push changes that optimize your network to support optimal application performance. Some AIOps-driven NPMD solutions can intelligently recommend the appropriate actions. Machine learning, big data, and predictive analytics technology can reveal how the network is impacting application performance and how changes can resolve potential problems.

For instance, automated capacity management capabilities can highlight potential capacity issues that will impact application performance (such as in the example of the live events company above) and suggest changes you can make to the network to address them (such as prioritizing business-critical applications over recreational applications to ensure the most important traffic is delivered with the best quality). These tools should have the ability to reconfigure the network, leveraging SNMP or integrations with network element management systems to adjust quality of service (QoS) settings. They can also integrate with an SD-WAN platform to adjust policies and QoS settings.

Today's digital businesses must ensure that users have an expected level of performance when working with various applications. Poor application performance can negatively impact employee productivity, product and service functionality, customer satisfaction, and inevitably, the bottom line. Your network administrators and application teams need simplified, comprehensive visibility across your entire network infrastructure, as well as the business critical applications that rely on it.

Leverage the above three best practices to ensure you have the insights needed to identify and resolve potential issues proactively, reduce management costs, and verify that your network and applications are always able to meet business objectives.

The Latest

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 ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...