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10 Questions to Ask When Evaluating Network Performance Management Solutions - Part 2

Jay Botelho

Successful insight into the performance of a company's networks starts with effective network performance management (NPM) tools. However, with the plethora of options it can be overwhelming for IT teams to choose the right one. This blog continues the 10 essential questions to ask before selecting an NPM tool.

Start with: 10 Questions to Ask When Evaluating Network Performance Management Solutions - Part 1

Question #6: Does the NPM solution support machine-learning, advanced anomaly detection and correlation?

Most solutions make broad claims in these areas, without much to show for it. Networks, and the demands on those networks, are highly unique between companies, so it's extremely difficult even with today's computing technologies to apply generalizations across network performance monitoring. But what is becoming more practical is the ability of NPM solutions to learn and apply knowledge based on machine learning of data trends over time, to create baselines and identify anomalous behavior without having to pre-configure limits or behavior characteristics.

Legacy systems require a great deal of a prior knowledge, and then significant configuration, for anomaly detection to work effectively. ML and AI are beginning to change that, but it's important to really validate the claims of any NPM solution.

Question #7: Is the solution utilizing advanced analytics and reporting?

To derive meaningful insights into complex issues, analytics platforms must provide reports and analyses on most, if not all, of a network's performance. This includes offering custom reporting for baselining and trend analysis and the ability to easily pivot reports to focus on key network performance intelligence.

Additionally, a modern NPM solution should correlate data across multiple network domains offering a cohesive, big-picture view of performance metrics and providing intelligent alerting, giving back valuable time to strapped IT teams.

Question #8: Does the solution assist with capacity planning?

Under-provisioning network resources can lead to congestion, bad user experience, and loss of productivity — overall, a negative business impact. Over-provisioning can lead to excess spending and a hit to the bottom line. Therefore, capacity planning is critical in helping to avoid performance problems and negative impacts.

When looking at an NPM solution, it is critical that it supports capacity planning through these features:

■ Service Level Agreement (SLA) management

■ Network and application analysis

■ Baselining and trending

■ Exception management

■ QoS management

Question #9: Does the solution facilitate root-cause analysis?

Most NPM solutions focus on visualization and reporting based on flow data (NetFlow, sFlow, IPFIX, etc.). These solutions, and the flow data that feed them, provide enough detail to troubleshoot many network and application issues. But at times flow data are simply not enough to get to the root cause of a problem. When more detailed data are needed, a recording of the network traffic itself, at the packet level, provides the detailed data needed for root-cause analysis. And when this packet data is analyzed with appropriate software, the software itself can identify many of these detailed network and application issues.

An NPM solution that can quickly pivot from flow data for visualization and reporting to packet data for analysis provides the most comprehensive solution and will significantly reduce the mean time to repair (MTTR).

Question #10: Can the solution provide scalable, enterprise support?

As the number of devices in many organizations continues to grow, it's important to implement tools that support this growth, particularly for large-scale organizations. A modern NPM platform must be able to analyze devices and environments at scale without latency and extend into additional environments such as multi-vendor WAN, public and private clouds and more. It also must support capacity planning and predict if a network can support an increase in business-critical traffic.

As organizations continue to grow and disperse, it is more evident than ever that ensuring optimal network performance is critical to business efficiency. When choosing a network performance monitoring solution, considering the questions above and implementing a unified platform will help organizations eliminate the cost and complexity of point solutions, reduce downtime, and successfully address the challenges of a modern network system.

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Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

10 Questions to Ask When Evaluating Network Performance Management Solutions - Part 2

Jay Botelho

Successful insight into the performance of a company's networks starts with effective network performance management (NPM) tools. However, with the plethora of options it can be overwhelming for IT teams to choose the right one. This blog continues the 10 essential questions to ask before selecting an NPM tool.

Start with: 10 Questions to Ask When Evaluating Network Performance Management Solutions - Part 1

Question #6: Does the NPM solution support machine-learning, advanced anomaly detection and correlation?

Most solutions make broad claims in these areas, without much to show for it. Networks, and the demands on those networks, are highly unique between companies, so it's extremely difficult even with today's computing technologies to apply generalizations across network performance monitoring. But what is becoming more practical is the ability of NPM solutions to learn and apply knowledge based on machine learning of data trends over time, to create baselines and identify anomalous behavior without having to pre-configure limits or behavior characteristics.

Legacy systems require a great deal of a prior knowledge, and then significant configuration, for anomaly detection to work effectively. ML and AI are beginning to change that, but it's important to really validate the claims of any NPM solution.

Question #7: Is the solution utilizing advanced analytics and reporting?

To derive meaningful insights into complex issues, analytics platforms must provide reports and analyses on most, if not all, of a network's performance. This includes offering custom reporting for baselining and trend analysis and the ability to easily pivot reports to focus on key network performance intelligence.

Additionally, a modern NPM solution should correlate data across multiple network domains offering a cohesive, big-picture view of performance metrics and providing intelligent alerting, giving back valuable time to strapped IT teams.

Question #8: Does the solution assist with capacity planning?

Under-provisioning network resources can lead to congestion, bad user experience, and loss of productivity — overall, a negative business impact. Over-provisioning can lead to excess spending and a hit to the bottom line. Therefore, capacity planning is critical in helping to avoid performance problems and negative impacts.

When looking at an NPM solution, it is critical that it supports capacity planning through these features:

■ Service Level Agreement (SLA) management

■ Network and application analysis

■ Baselining and trending

■ Exception management

■ QoS management

Question #9: Does the solution facilitate root-cause analysis?

Most NPM solutions focus on visualization and reporting based on flow data (NetFlow, sFlow, IPFIX, etc.). These solutions, and the flow data that feed them, provide enough detail to troubleshoot many network and application issues. But at times flow data are simply not enough to get to the root cause of a problem. When more detailed data are needed, a recording of the network traffic itself, at the packet level, provides the detailed data needed for root-cause analysis. And when this packet data is analyzed with appropriate software, the software itself can identify many of these detailed network and application issues.

An NPM solution that can quickly pivot from flow data for visualization and reporting to packet data for analysis provides the most comprehensive solution and will significantly reduce the mean time to repair (MTTR).

Question #10: Can the solution provide scalable, enterprise support?

As the number of devices in many organizations continues to grow, it's important to implement tools that support this growth, particularly for large-scale organizations. A modern NPM platform must be able to analyze devices and environments at scale without latency and extend into additional environments such as multi-vendor WAN, public and private clouds and more. It also must support capacity planning and predict if a network can support an increase in business-critical traffic.

As organizations continue to grow and disperse, it is more evident than ever that ensuring optimal network performance is critical to business efficiency. When choosing a network performance monitoring solution, considering the questions above and implementing a unified platform will help organizations eliminate the cost and complexity of point solutions, reduce downtime, and successfully address the challenges of a modern network system.

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...