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6 Reasons Every NetOps Team Should Use a Packet-Based Analytics Solution - Part 1

Jay Botelho

Today's networks are driving more digital change than ever before, which is putting increased pressure on those responsible for deploying, monitoring and maintaining them. As a result, there's a premium on actionable real-time visibility into networks and application performance issues.

NetOps teams are now expected to proactively identify problems before they impact the organization, and if problems do arise, to solve them in real time to minimize the effects on the end user. The goal is to provide actionable network transaction-based monitoring, rapid root-cause analysis and integrated packet-level forensics in a single solution, so teams can quickly identify latency, communication and capacity issues.

However, extracting insight from the flood of information traveling through today's high-speed networks is a constant challenge faced by those responsible for network performance and reliability. Statistical summaries and aggregated data are traditionally just a starting point for further investigation into problems. And historically, packet data has not scaled for high-speed real-time monitoring. As a result, a new breed of solution has been born that simultaneously provides the precision of packet-based analytics with the speed of flow-based monitoring (at a reasonable cost).

The end result is actionable visibility, which helps teams focus on the biggest problem areas, which essentially requires four elements.

First, the data must be acquired from wire data, a datacenter, the cloud or the edge.

Next, the network and data must be monitored for end-user experience in true real time.

Third, the team must be ready to investigate a problem or issue, from traffic to trace files.

And finally, a certain level of the packet data must be retained so teams can troubleshoot. How can this level of network visibility be put into action?

Here are 6 reasons to use these new NPM/APM analytics solutions:

1. Find out quickly if it's the application or the network

When problems emerge, you need to solve them fast. Understanding if this is an application or a network issue – and having the packet data to back up that claim – is critical to eliminating debates and war room discussions.

For example, see at a glance which transactions on the networks are experiencing the worst network and the worst application latency, from network-wide down to an individual server. When you see application latency that is outside of the norm, a single click can provide the actual packet data comprising the network transaction. This is the best data possible for determining the root cause of the problem. Often times application errors can quickly be identified in the packet payload data.

2. Gain visibility into the line of business

For many companies, the business is the application(s) they run. For example, an online retailer is defined by the performance of the web servers and associated web applications driving the storefront. Visibility into key performance indicators for these specific applications, including network and application latency and transaction quality for each and every transaction, drives real-time response that keeps the storefront running at its maximum potential.

3. Speed to resolution

Every second counts when the network or an application has a problem. Having the ability to navigate fluidly in real-time at up to 35 Gbps of network traffic, and then immediately click through to specific packet data dramatically speeds resolution time. Plus, the less time the network team or IT spends troubleshooting, the more time they can spend on projects to improve the network, like cloud migrations or building data warehouses.

Read 6 Reasons Every NetOps Team Should Use a Packet-Based Analytics Solution - Part 2 for 3 more reasons to use the new NPM/APM analytics solutions.

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6 Reasons Every NetOps Team Should Use a Packet-Based Analytics Solution - Part 1

Jay Botelho

Today's networks are driving more digital change than ever before, which is putting increased pressure on those responsible for deploying, monitoring and maintaining them. As a result, there's a premium on actionable real-time visibility into networks and application performance issues.

NetOps teams are now expected to proactively identify problems before they impact the organization, and if problems do arise, to solve them in real time to minimize the effects on the end user. The goal is to provide actionable network transaction-based monitoring, rapid root-cause analysis and integrated packet-level forensics in a single solution, so teams can quickly identify latency, communication and capacity issues.

However, extracting insight from the flood of information traveling through today's high-speed networks is a constant challenge faced by those responsible for network performance and reliability. Statistical summaries and aggregated data are traditionally just a starting point for further investigation into problems. And historically, packet data has not scaled for high-speed real-time monitoring. As a result, a new breed of solution has been born that simultaneously provides the precision of packet-based analytics with the speed of flow-based monitoring (at a reasonable cost).

The end result is actionable visibility, which helps teams focus on the biggest problem areas, which essentially requires four elements.

First, the data must be acquired from wire data, a datacenter, the cloud or the edge.

Next, the network and data must be monitored for end-user experience in true real time.

Third, the team must be ready to investigate a problem or issue, from traffic to trace files.

And finally, a certain level of the packet data must be retained so teams can troubleshoot. How can this level of network visibility be put into action?

Here are 6 reasons to use these new NPM/APM analytics solutions:

1. Find out quickly if it's the application or the network

When problems emerge, you need to solve them fast. Understanding if this is an application or a network issue – and having the packet data to back up that claim – is critical to eliminating debates and war room discussions.

For example, see at a glance which transactions on the networks are experiencing the worst network and the worst application latency, from network-wide down to an individual server. When you see application latency that is outside of the norm, a single click can provide the actual packet data comprising the network transaction. This is the best data possible for determining the root cause of the problem. Often times application errors can quickly be identified in the packet payload data.

2. Gain visibility into the line of business

For many companies, the business is the application(s) they run. For example, an online retailer is defined by the performance of the web servers and associated web applications driving the storefront. Visibility into key performance indicators for these specific applications, including network and application latency and transaction quality for each and every transaction, drives real-time response that keeps the storefront running at its maximum potential.

3. Speed to resolution

Every second counts when the network or an application has a problem. Having the ability to navigate fluidly in real-time at up to 35 Gbps of network traffic, and then immediately click through to specific packet data dramatically speeds resolution time. Plus, the less time the network team or IT spends troubleshooting, the more time they can spend on projects to improve the network, like cloud migrations or building data warehouses.

Read 6 Reasons Every NetOps Team Should Use a Packet-Based Analytics Solution - Part 2 for 3 more reasons to use the new NPM/APM analytics solutions.

Hot Topics

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...