What is Network Observability and What Should You Look for?

Networks are the backbone of all modern enterprises, yet like Internet connectivity, they tend to be invisible to most of us until something goes wrong. As networks grow in complexity and scale, seeing what’s truly happening in the network and ensuring optimal performance and reliability has also become increasingly challenging. This is where network observability comes into play, providing critical insights into the health, performance, and security of network infrastructure.

Network observability is a comprehensive approach to monitoring and understanding the state and behavior of the network infrastructure in real-time. It provides deeper insights and remediation capabilities than traditional network monitoring, going beyond metrics like uptime, latency, and packet loss, into how data flows through the network, the interactions between different network components like switches, APs, firewalls, and applications, and the overall health of the network environment. Network observability involves the collection and analysis of extensive telemetry data, including logs, metrics, traces, and network topology information, bringing insights and visualizations of the entire network to identify anomalies and understand the root cause of issues more effectively, leading to faster and more accurate resolutions.

The key advantage of network observability is its ability to provide a holistic, dynamic, and contextual view of the network, allowing networking and IT teams to predict and prevent potential problems before they impact users. This is especially crucial in complex, distributed network environments where traditional monitoring tools might miss subtle issues or provide incomplete data.

Leveraging advanced analytics, machine learning, and automation to continuously assess network performance and security, network observability enables organizations to optimize their networks, ensure compliance, and maintain high levels of service reliability and performance.

Comprehensive Telemetry Data – Telemetry data is collected, analyzed, and correlated from connected network equipment, sensor, application, and service sources to help network engineers understand the internal state of the network. Telemetry data sources include logs, network traffic patterns, packet flow traces, and routing tables, as well as performance metrics (i.e., bandwidth usage, latency, error rates), application latency, and other relevant data sources.

Real-time, End-to-End Visibility – Real-time visibility across the entire network, from core network infrastructure all the way to edge devices and cloud services is crucial for ensuring uninterrupted network performance and health. Providing live monitoring capabilities helps network engineers see how different components and services interact, proactively discovering performance issues, patterns or anomalies, and security threats.

Contextual Awareness and Correlation – Beyond simple data collection, understanding the context in which network events occur, such as the relationships between different devices and applications, offers a more holistic view of the network, making it easier to assess the impact of issues and prioritize responses. With advanced analytics and AI to analyze large amounts of telemetry data, identify patterns, and correlate events across different layers of the network, network administrators can rapidly uncover and resolve the root causes of issues.

Proactive Incident Identification and Response – Unlike more reactive methods of traditional network monitoring, network observability helps to automatically detect deviations from normal network behavior, such as unexpected traffic spikes or increases in latency, which could indicate potential problems or security threats. This approach helps network administrators resolve issues with minimal or no disruption at all.

NetOps Scalability – When scaling large, distributed, and complex network environments, network observability enables and unifies data collection, aggregation, and analysis across all network segments. This ensures consistent and responsive monitoring, detection, and remediation of issues in near real-time without affecting other network and IT resources.

AI and Automation – Network observability, like many other cloud services, utilize AI and automation to monitor, process, and transform data. From automating anomaly detection to pattern recognition to initiating corrective actions, such as rerouting traffic or adjusting configurations, AI-ready data models and machine learning algorithms can continuously observe and optimize the network based on predefined rules or AI-driven recommendations.

Network observability and network monitoring are interrelated, playing complementary roles in network lifecycle management. While they both address verifying and correcting network stability and performance, they diverge in how they aggregate and correlate data, and handle issues that arise within the network.

The purpose of network monitoring is to track specific metrics and events, such as uptime, latency, bandwidth usage, packet loss, jitter, throughput, and network device and service availability, to ensure the network is operating as expected. It involves setting thresholds for various key metrics and detecting any network patterns that deviate from normal network behavior and allowable thresholds, alerting network administrators of issues that need attention. However, traditional network monitoring often provides a snapshot-in-time state of the network and may not always reveal the underlying causes of network problems or how different issues are interconnected.

Network observability takes a more comprehensive and dynamic view that includes a deeper understanding of the state and behavior of the entire network, and everything connected to it. It goes beyond just simple data collection by collecting, analyzing, and correlating data from multiple sources, including logs, traces, topology, application layers, network devices, servers, and cloud applications, to understand the full context of network behavior, tracing network events across different components throughout the network environment. This creates a more complete and holistic view of the entire network, providing network administrators with real-time actionable insights into performance optimization, root cause analysis, and incident response.

Essentially, while network monitoring is about tracking and responding to defined metrics and alerts, network observability evolves this approach, providing a comprehensive understanding of the network's internal state and behavior.

Network observability extends the monitoring capabilities combining more data sources and AI-driven automations. This deeper understanding of the network infrastructure helps network administrators not just make better decisions driven by data, but ensures much of network performance, security, and reliability optimizations can be automated, and only escalated when manual human intervention is needed.

Latency – This refers to the time it takes for data to travel from one point to another. High latency can mean performance or routing issues, or other network congestion, which can lead to slow applications and a poor user experience.

Packet Loss – This is counted as a percentage of packets lost during transmission. Packet loss can mean failed transactions or incomplete data. Monitoring packet loss helps to identify network issues like failing network equipment or misconfiguration issues early and implement solutions before they affect the entire network.

Bandwidth Utilization – This measures how much available network bandwidth is being used. Hitting bandwidth limits can cause critical slowdown on your network which can affect server and application performance. Monitoring this can adjust bandwidth needs by periods of high demand and alleviate network congestion during peak times.

Jitter – This metric measures the variability in packet arrival times. Monitoring jitter helps ensure real-time communications, such as VoIP and video conferencing, aren’t impacted by poor call quality, network lag, and other interruptions.

Throughput – Measures the amount of data successfully delivered over the network within a given time frame. Low throughput can signal issues with actual performance of the network and network devices, causing delays in resources being transferred, which can drag down productivity.

Error Rates – This includes a variety of errors that occur in a given time. Errors, for example input errors and CRC errors, can occur for various reasons, including electrical interference, failing hardware, faulty cable lines, or other interference. Monitoring error rates helps identify network instability, power issues, faulty hardware, or poor cable connections, leading to data corruption or retransmissions.

Organizations who incorporate network observability typically experience greater operational efficiency, increased network resilience, and more consistent and predictable network performance. Some of the key business benefits for network observability include:

Enhanced Operational Efficiency – Network observability provides more detailed real-time insights into network performance, making it easier for IT teams to quickly identify and resolve issues before they impact users on the network. Not only does this reduce downtime and minimize disruptions, but it also allows for more efficient network management by automating many of the network optimizations, corrective actions, and patches and updates typically done manually, freeing up resources to focus on other strategic initiatives.

Scalability and Expansion Plans – By providing real-time insights into network performance and utilization, businesses can efficiently scale their network infrastructure with changes in demand for new technologies and services, growing workloads, dynamic usage patterns, or expanding operations to new branch or campus locations. Additionally, a detailed and historical contextual data of traffic and usage patterns network wide, along with potential risks and issues, makes it easier to forecast network demands and plan future capacity needs to expand and bring additional services online.

Continuously Optimized Network Performance – Because network observability delivers a more holistic view of the entire network, businesses gain a deeper understanding of how data flows and applications interact throughout the network. continuous monitoring and optimization of network performance. By monitoring error rates, traffic patterns, and latency metrics, network administrators can identify issues, patterns, and trends and make more informed decisions, driven by data, to continuously optimize network performance. Continuously optimizing the network not only ensures predictable high performance, but also improves reliability, saves costs over time and preventing more costly fixes, and has better business outcomes like increased user experience and employee productivity.

Proactive and Automated Issue Resolution – Network observability enhances issue detection and resolution by providing real-time visibility into network behavior and comprehensive contextual data across different network devices, applications, and services. With advanced analytics and AI, network observability allows businesses to detect and address potential issues, such as bottlenecks, anomalies, and security threats, before they escalate into major problems. With improved troubleshooting and root cause analysis, correlating data across various network sources, like logs, traces, and metrics, enables faster and more accurate issue detection. Taking a proactive stance prevents costly outages and reduces the manual time spent troubleshooting, leading to more consistent and predictable network operations.

Modern networks have increased not only in size and scope but also complexity. The integration of Artificial Intelligence and automation into these networks represents a significant leap forward, transforming how IT teams manage and maintain networks.

Taking into consideration that a core objective of network observability is to provide deep insights into the short- and long-term state of a network, AI gives it a critical boost by enhancing the ability to process and analyze vast amounts of data in real time. AI and machine learning algorithms can sift through the vast array of metrics, logs, and traces generated by modern networks, identifying patterns and anomalies that might otherwise go unnoticed. This capability is crucial in today's environments, where the sheer volume and complexity of data can overwhelm human operators.

AI-driven anomaly detection can pinpoint irregularities in network traffic that might indicate a potential security threat or performance degradation. Because it is continuously learning from historical data, AI models can differentiate between normal fluctuations and genuine issues, reducing the number of false positives and allowing IT teams to focus on real problems. AI can also assist with predictive maintenance by forecasting potential failures or performance bottlenecks based on historical trends, enabling proactive measures to be taken.

While AI enhances the analytical capabilities of network observability, automation plays a crucial role in streamlining responses and interventions. Automation in network monitoring involves the use of predefined rules and workflows to perform routine tasks, such as configuring devices, updating software, or implementing security patches, without human intervention. This not only reduces the workload on IT teams but also minimizes the risk of human error.

When combined with AI, automation becomes even more powerful. For example, when AI detects a potential issue, such as an unusual spike in network latency, automated systems can immediately trigger corrective actions, such as rerouting traffic or adjusting resource allocation. This reduces the time it takes to resolve issues, minimizing downtime and ensuring a more resilient network.

The integration of AI and automation with network observability creates a self-optimizing loop where networks can monitor, analyze, and improve themselves with minimal human intervention. AI provides the intelligence needed to make sense of complex data, while automation ensures that necessary actions are handled swiftly and accurately. This synergy leads to more reliable, efficient, and secure networks that can adapt to changing conditions in real time.

Although the types of networking observability and monitoring tools needed differ from organization to organization, there are still key features to consider when deciding on tools to use:

Comprehensive Data Collection – Network observability tools should gather data from diverse sources, including logs, metrics, traces, events, and network devices in order to provide a more holistic view and deeper understanding of network performance and health. Depending on your network needs, they should also support a wide range of protocols (i.e., NetFlow, sFlow, SNMP, etc.) to ensure compatibility with different network devices and services.

Real-time Monitoring and Alerts – The tools should provide real-time monitoring capabilities to quickly and proactively identify and respond to issues as they arise. Network administrators should be able to track network performance metrics such as bandwidth usage, latency, packet loss, and device health in real-time and receive customizable notifications based on set thresholds around those metrics.

Advanced, Actionable Analytics – The tools should leverage machine learning algorithms and AI to detect anomalies, predict potential issues, and provide actionable insights. They should be capable of dynamically generating performance baselines based on historical data and current day-to-day operations.

Anomaly Detection and Root Cause Analysis – Look for tools that can automatically trigger predefined actions (such as rerouting traffic, restarting services, or deploying patches) in response to detected issues. The tools should use advanced anomaly detection techniques, such as pattern recognition and statistical analysis, to detect deviations from normal patterns and quickly pinpoint the root cause of an issue.

Scalability and Flexibility – The tools should offer flexibility in deployment, whether on-site, in the cloud, or in hybrid environments. They should be able to scale as the network grows, allowing for increasing data volumes and complex infrastructure without sacrificing performance.

Security and Compliance Monitoring – The tools should facilitate compliance with industry standards by providing audit logs, access controls, and detailed reporting capabilities. They should also include features to monitor security threats, such as intrusion detection and vulnerability assessments.

Deciding whether to deploy a single network observability and monitoring tool versus adopting a Network as a Service (NaaS) solution depends on various factors, including your organization’s specific needs, infrastructure complexity, budget, and long-term goals. Here are some factors to consider to help you make an informed decision:

Assess Your Network's Complexity and Size

• Single Tool: If your network is relatively small, straightforward, and primarily on-site, a single, comprehensive tool may be sufficient. Such tools can provide deep visibility and control, allowing you to manage your network effectively without the overhead of a NaaS solution.
• NaaS Solution: For more distributed or larger complex networks, such as those with direct cloud connects or IoT sensor-rich environments, NaaS would be more appropriate as they offer scalability and flexibility in managing diverse environments and reducing the complexity of overseeing large-scale operations.

Evaluate Your Internal Expertise and Resources

• Single Tool: If your IT team has strong expertise in network management and you have the resources to handle deployment, maintenance, and troubleshooting, a single tool may be more cost-effective. You retain full control and customization capabilities, but managing upgrades, integrations, and scaling will be a more involved process.
• NaaS Solution: If your organization lacks the network management capabilities or wants to minimize the internal burden of network operations, a NaaS solution is the best direction. NaaS providers typically handle the complexities of deployment, scaling, maintenance, and updates, allowing your team to focus on strategic initiatives rather than day-to-day network management.

Consider Scalability and Future Growth

• Single Tool: A single tool can be sufficient if your network is stable and not expected to grow significantly in the near future. However, if your network does expand, you may face challenges in scaling the tool to meet the increasing demands, which might require additional investments in hardware, software, or licenses, which inherently bring other challenges to network design, architecture, provisioning, and deployments.
• NaaS Solution: NaaS solutions are inherently scalable and designed to grow with your network without the need for significant upfront investments. If your organization is expanding or you anticipate rapid growth, a NaaS solution offers the flexibility to scale seamlessly while mitigating for increased complexity and maintaining predictable pricing.

Budget and Cost Considerations

• Single Tool: Initial costs for a single tool may be lower, especially if you already have an existing network infrastructure. However, the total cost of ownership (TCO), which includes ongoing maintenance and upgrades, can add up over time, especially as your network expands.
• NaaS Solution: NaaS solutions often operate on a subscription-based model, spreading costs over time rather than requiring a large upfront investment. This can make budgeting more predictable and manageable. Additionally, NaaS providers often bundle services, reducing the need for separate investments in Internet, hardware, software, installation, maintenance, upgrades, and support.

Integration with Existing Infrastructure

• Single Tool: If you have a well-established network infrastructure with existing monitoring and management tools, then a single tool that integrates into your existing network infrastructure may be the best option.
• NaaS Solution: NaaS can offer more comprehensive integration with various hardware and software makers, cloud services, SD-WAN, SASE, and other emerging technologies. Because NaaS is brand and technology agnostic, businesses can avoid vendor lock-in and instead integrate the best technologies for the network experience you want. If your organization is moving towards a cloud-first or hybrid model, NaaS delivers better alignment with your strategic direction.

Security and Compliance Requirements

• Single Tool: If you need granular control over your network's security and compliance, and if these requirements are complex and specific to your own internal governance, a single tool may be preferable. It allows you to customize security policies and compliance measures precisely to your needs.
• NaaS Solution: NaaS providers typically offer robust security features and compliance certifications as part of their service. Some NaaS providers also offer custom control over security and compliance requirements giving you more finetuned control if need for industry-specific compliance If you’re looking for a turnkey solution that ensures industry-standard compliance without the need for extensive internal oversight, a NaaS solution may be more appealing.

Choosing between a single network observability tool and a NaaS solution boils down to your organization’s current state and future needs. A single tool can offer deep control and may be ideal for smaller, less complex environments with an experienced IT team. Alternatively, a NaaS solution provides scalability, ease of management, and access to the latest network technologies, making it suitable for growing, complex networks or organizations looking to minimize internal resource commitments and focus on long-term network agility that doesn’t lock capital and resources into multi-year deals. Consider your network’s complexity, your team’s expertise, budget constraints, and your strategic goals to determine the best option.

The future of network security lies in the continued integration of advanced technologies, such as artificial intelligence (AI) and machine learning (ML). These technologies enable proactive threat detection and response, ensuring that networks remain secure in an evolving threat landscape. As cyber threats become more sophisticated, leveraging advanced technologies becomes increasingly critical to stay ahead of cyber threats and build a more resilient security posture.

• AI and ML: These technologies enhance threat detection and response capabilities by analyzing vast amounts of data and identifying patterns indicative of malicious activity. Unlike traditional security measures that rely on predefined rules, AI and ML can adapt to new threats in real-time, providing a dynamic defense mechanism against cyberattacks.
• Automation: Automating routine security tasks, such as patch management and system monitoring, frees up internal IT resources to focus on more strategic initiatives. This not only increases operational efficiency but also reduces the likelihood of human error, which can be a significant vulnerability in any security framework.
• Quantum Security: With the potential to break traditional encryption methods, quantum computing represents a significant risk to current security infrastructures. Quantum security involves developing new encryption techniques that can withstand quantum attacks, ensuring the long-term integrity of sensitive data.

Where is Your Network Security Headed?

While there are many capabilities and approaches deploying and managing network security for NaaS, it is crucial for enterprises to have well-defined strategies and controls in place to safeguard their digital infrastructure. Implementing a combination of technical measures, such as encryption, multi-factor authentication, and regular security patches, alongside comprehensive policies and proactive monitoring, can create a solid defense against cyberattacks. By leveraging advanced technologies and best practices to ensure that every layer of the network is protected, organizations can mitigate the risk of breaches and maintain the integrity and confidentiality of their data.