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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

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...