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The Necessary Shift from Monitoring to True Network Clarity: How Does Observability Help?

Sandhya Saravanan
ManageEngine

Ever struggled to pinpoint the root cause of a network slowdown? Frustrated by alerts that only notify you when something is broken, without explaining why? You're not alone. Traditional network monitoring, while valuable, often falls short in providing the context needed to truly understand network behavior. This is where observability shines. In this blog, we'll compare and contrast traditional network monitoring and observability — highlighting the benefits of this evolving approach.

Traditional monitoring

Imagine your network is a busy highway: For monitoring, it has a few security check points and traffic cameras. This arrangement will tell you how many vehicles are on the road, and if there's any pileup on the highway or if any major network traffic congestion is noticed, an alarm goes off highlighting the issue. From this alarm, you know that something's wrong on the highway, but you have no way of knowing what it is. It could be anything from a simple traffic jam to a major accident. Figuring out the exact cause of the alarm and the resulting traffic slowdown with the help of cameras can be time-consuming.

Traditional network monitoring will tell you what's happening and nothing more.

Observability

Now, imagine instead of just a few traffic cameras, you have a comprehensive system monitoring the entire city. This system doesn't just count cars at intersections; it tracks individual vehicles, their routes, and even the weather conditions. This system is also aware of the road closures, construction, and events happening throughout the city. This is observability. It utilizes data to comprehend the reasons behind traffic patterns. In the event of congestion, the system can swiftly identify the root cause, such as a stalled vehicle on the side of a road before the congestion escalates. Observability can anticipate potential issues by detecting unusual trends, like a sudden surge in traffic in a specific location. Therefore, instead of simply reacting to a traffic jam, you can proactively tackle the underlying issues and ensure a smooth traffic flow.

Observability gives you the full picture and helps you understand why things are happening, not just what is happening.

Similarity between monitoring and observability

The only similarity between traditional monitoring and observability remains a shared goal: to gain insights into the health, performance, and behavior of a system. Both approaches are fundamentally data-driven, relying on the collection and analysis of stats like metrics, logs, and traces to understand what's happening within the system. Ultimately, the insights gained from both methods help with proactive issue detection, troubleshooting, resource allocation decisions, and more, even though the methods of investigation differ.

The bottom line is traditional monitoring focuses only on specific areas while observability involves a more comprehensive approach.

This table can best explain the difference between traditional monitoring and observability and where the former falls short:

Image
ManageEngine

 

The crucial role of observability in IT infrastructure

The evolution in IT management and observability is a response to the increasing complexity of modern networks, where traditional tools often don't have what it takes. Embracing observability gives IT admins a more comprehensive understanding of their network, leading to improved performance, faster troubleshooting, and a better user experience.

Here are some of the perks that comes with incorporating observability to improve business operations:

  • Proactive issue detection and improved user experience
  • Dynamic adaptation in cloud-native environments
  • Adopting modern application architectures
  • Log-based threat detection

Proactive issue detection and improved user experience: 

  • With observability, you can identify issues in real-time and quicken issue remediation.
  • A fully observable network is crucial for ensuring services operate as expected and maintaining critical SLAs.
  • Develop and implement comprehensive observability strategies for highly resilient applications, incorporating end-user application performance monitoring with the appropriate tools to guarantee customer satisfaction.

Dynamic adaptation in cloud-native environments: 

  • Due to the dynamic and distributed nature of cloud-native microservice environments, observability is the only way to achieve comprehensive visibility, enabling analysis of how, when, and where problems occur.
  • Observability facilitates resource mapping within IT architectures, enabling interconnected functionality and streamlining automated application deployments.
  • Use root cause analysis to pinpoint the location and cause of failures in a distributed application and implement the necessary fixes.

Adopting modern application architectures: 

  • Observability helps simplify application quality control during modernization and legacy transformation.
  • Benchmark performance, analyze behavior, and manage application-level configurations.
  • Achieve comprehensive visibility into application performance and availability, enabling efficient detection, troubleshooting, and root cause analysis of application issues.

Log-based threat detection:

  • Utilize threat detection techniques to anticipate and address potential application performance interruptions, including pinpointing the root cause of errors.
  • Use observability for continuous feedback via logs and reports, and leverage ML to predict potential issues.

Observability's true power lies in its ability to predict issues, understand the impact of changes, and provide solutions.

ManageEngine OpManager Plus comes with a pragmatic approach to observability, utilizing the power of AI and ML. By utilizing the data acquired from different network management tools, OpManager Plus offers a comprehensive observability solution. OpManager Plus delivers a comprehensive platform for system and application observability with built-in capabilities for network monitoring, bandwidth and configuration management, firewall analysis, and application performance tracking. Do you want to know how well this observability solution can work for you? Try OpManager Plus today! Leverage our 30-day free trial or book a personalized demo with our product experts to experience the power of OpManager Plus firsthand.

Sandhya Saravanan is a Product Marketer at ManageEngine

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

The Necessary Shift from Monitoring to True Network Clarity: How Does Observability Help?

Sandhya Saravanan
ManageEngine

Ever struggled to pinpoint the root cause of a network slowdown? Frustrated by alerts that only notify you when something is broken, without explaining why? You're not alone. Traditional network monitoring, while valuable, often falls short in providing the context needed to truly understand network behavior. This is where observability shines. In this blog, we'll compare and contrast traditional network monitoring and observability — highlighting the benefits of this evolving approach.

Traditional monitoring

Imagine your network is a busy highway: For monitoring, it has a few security check points and traffic cameras. This arrangement will tell you how many vehicles are on the road, and if there's any pileup on the highway or if any major network traffic congestion is noticed, an alarm goes off highlighting the issue. From this alarm, you know that something's wrong on the highway, but you have no way of knowing what it is. It could be anything from a simple traffic jam to a major accident. Figuring out the exact cause of the alarm and the resulting traffic slowdown with the help of cameras can be time-consuming.

Traditional network monitoring will tell you what's happening and nothing more.

Observability

Now, imagine instead of just a few traffic cameras, you have a comprehensive system monitoring the entire city. This system doesn't just count cars at intersections; it tracks individual vehicles, their routes, and even the weather conditions. This system is also aware of the road closures, construction, and events happening throughout the city. This is observability. It utilizes data to comprehend the reasons behind traffic patterns. In the event of congestion, the system can swiftly identify the root cause, such as a stalled vehicle on the side of a road before the congestion escalates. Observability can anticipate potential issues by detecting unusual trends, like a sudden surge in traffic in a specific location. Therefore, instead of simply reacting to a traffic jam, you can proactively tackle the underlying issues and ensure a smooth traffic flow.

Observability gives you the full picture and helps you understand why things are happening, not just what is happening.

Similarity between monitoring and observability

The only similarity between traditional monitoring and observability remains a shared goal: to gain insights into the health, performance, and behavior of a system. Both approaches are fundamentally data-driven, relying on the collection and analysis of stats like metrics, logs, and traces to understand what's happening within the system. Ultimately, the insights gained from both methods help with proactive issue detection, troubleshooting, resource allocation decisions, and more, even though the methods of investigation differ.

The bottom line is traditional monitoring focuses only on specific areas while observability involves a more comprehensive approach.

This table can best explain the difference between traditional monitoring and observability and where the former falls short:

Image
ManageEngine

 

The crucial role of observability in IT infrastructure

The evolution in IT management and observability is a response to the increasing complexity of modern networks, where traditional tools often don't have what it takes. Embracing observability gives IT admins a more comprehensive understanding of their network, leading to improved performance, faster troubleshooting, and a better user experience.

Here are some of the perks that comes with incorporating observability to improve business operations:

  • Proactive issue detection and improved user experience
  • Dynamic adaptation in cloud-native environments
  • Adopting modern application architectures
  • Log-based threat detection

Proactive issue detection and improved user experience: 

  • With observability, you can identify issues in real-time and quicken issue remediation.
  • A fully observable network is crucial for ensuring services operate as expected and maintaining critical SLAs.
  • Develop and implement comprehensive observability strategies for highly resilient applications, incorporating end-user application performance monitoring with the appropriate tools to guarantee customer satisfaction.

Dynamic adaptation in cloud-native environments: 

  • Due to the dynamic and distributed nature of cloud-native microservice environments, observability is the only way to achieve comprehensive visibility, enabling analysis of how, when, and where problems occur.
  • Observability facilitates resource mapping within IT architectures, enabling interconnected functionality and streamlining automated application deployments.
  • Use root cause analysis to pinpoint the location and cause of failures in a distributed application and implement the necessary fixes.

Adopting modern application architectures: 

  • Observability helps simplify application quality control during modernization and legacy transformation.
  • Benchmark performance, analyze behavior, and manage application-level configurations.
  • Achieve comprehensive visibility into application performance and availability, enabling efficient detection, troubleshooting, and root cause analysis of application issues.

Log-based threat detection:

  • Utilize threat detection techniques to anticipate and address potential application performance interruptions, including pinpointing the root cause of errors.
  • Use observability for continuous feedback via logs and reports, and leverage ML to predict potential issues.

Observability's true power lies in its ability to predict issues, understand the impact of changes, and provide solutions.

ManageEngine OpManager Plus comes with a pragmatic approach to observability, utilizing the power of AI and ML. By utilizing the data acquired from different network management tools, OpManager Plus offers a comprehensive observability solution. OpManager Plus delivers a comprehensive platform for system and application observability with built-in capabilities for network monitoring, bandwidth and configuration management, firewall analysis, and application performance tracking. Do you want to know how well this observability solution can work for you? Try OpManager Plus today! Leverage our 30-day free trial or book a personalized demo with our product experts to experience the power of OpManager Plus firsthand.

Sandhya Saravanan is a Product Marketer at ManageEngine

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...