Time to read: 5 minutes 10 seconds | Published: May 08, 2025
Network observability What is Network Observability?
Network observability is the process of seeing and understanding what is happening within a network. This is typically achieved by collecting, correlating, and analyzing data, including network metrics, device characteristics, traffic flows, and event logs to generate visualizations that represent the state of the network.
Network observability workflows are typically integrated as part of broader AIOps, security, or network management tools. They are used to improve visibility and capture telemetry across multiple layers of the Open Systems Interconnection (OSI) model to develop a rich and comprehensive data set. This empowers IT teams to proactively address or mitigate network issues related to performance, health, behavior, policies, and resources. While most network observability tools are vendor-specific, HPE Aruba Networking Central and HPE OpsRamp are designed to integrate data from a wide range of third-party IT infrastructure.
Defining network observability
Network observability involves gathering detailed telemetry and data from a wide variety of network devices such as network switches, access points, and gateways, as well as connected clients and IoT devices. Network observability tools continually collect data to offer a clear, real-time view of network health and performance.
Modern network observability tools further enhance the value of data collection by using artificial intelligence (AI) and machine learning (ML) to correlate and analyze data for insights, identifying trends, predicting issues, and automating testing and troubleshooting workflows to significantly reduce the workload on network operations teams.
Key components of network observability
1. Data collection
Observability starts with collecting data from various sources, including network devices (e.g., switches, access points, gateways), applications, servers, and end-user devices. Data types include:
- Metrics: Quantitative measurements like bandwidth usage, latency, packet loss, etc.
- Logs: Event data, error messages, and system activity.
- Traces: End-to-end transaction records for understanding how data flows through the network.
2. Correlation and context
Observability tools correlate data from multiple sources to provide context. For example, linking application performance issues to specific network events helps pinpoint root causes.
3. Visualization
Dashboards, charts, and topology maps provide visual representations of the network's health and performance, making it easier for teams to interpret data.
4. Analytics and insights
Observability platforms often include artificial intelligence (AI) and machine learning (ML) capabilities to analyze data against a reference data lake to identify patterns, anomalies, and proactively suggest changes to enhance network performance.
What are the benefits of network observability?
Modern network observability helps deliver comprehensive metrics, insights and analytics. Third-party observability and AI-powered automation are crucial for maintaining seamless reliability, security, and performance for today’s rapidly evolving digital landscape. Below are some key benefits that network observability has to offer:
- Broad visibility: Discover insights from network devices, as well as connectivity links, applications, and endpoints to enhance network performance and ensure seamless connectivity.
- Performance optimization: Find and address bottlenecks, traffic flows, and bandwidth usage to prioritize business critical applications.
- Proactive issue resolution: Monitor traffic, latency, and errors to detect and address problems before they impact users, devices, and business workloads on the network.
- Security hardening: Identify patterns that indicate unusual or malicious activity within the network to inform segmentation policies and detect breaches for risk mitigation and faster incident response.
- Compliance and reporting: Accurately collect data and generate reports for regulation and policy compliance audits and to demonstrate adherence to security and performance standards.
- Capacity planning: Analyze historical data to assess load and forecast traffic for informed decisions about infrastructure scaling to accommodate business growth.
- Operational cost reduction: Minimize risk of network downtime and impact on operational expenses through accelerated troubleshooting and efficient management of network infrastructure.
Network observability versus network monitoring
Traditional network monitoring focuses on tracking predefined metrics and sending alerts when thresholds are crossed. This limited approach to root cause analysis struggles to keep pace with the dynamic nature and complex scale of today’s modern enterprise networks, often distributed across on-premises, cloud, remote, and hybrid environments.
Modern network observability provides a deeper, more holistic view of the network by leveraging granular data and advanced analytics. It goes beyond traditional network monitoring to capture what happened, identify why it happened, and then takes action to help resolve issues using AI-powered tools and a suite of integrations.
This makes network observability particularly valuable in dynamic, modern IT environments where traditional network monitoring may fall short. It is a critical capability for managing today's increasingly complex networks and enabling organizations to maintain optimal performance, improve reliability, and ensure a seamless experience for end users.
What are the best practices for network observability?
To implement network observability effectively and successfully for an enterprise environment, consider the following best practices:
- Collect data broadly: Systemically gather data across network devices and layers. When evaluating infrastructure equipment like access points and network switches, consider the breadth of their ability to produce real-time telemetry, analytics, application visibility, and event logs.
- Consolidate and integrate tools: Deploy a centralized management platform that can unify security, infrastructure, and application performance monitoring. Consider whether vendor tools can integrate or provide third-party observability to help reduce blind spots, prevent data silos, and avoid fragmented views that inhibit visibility.
- Use AI for insights at scale: Leverage AI and ML to quickly identify patterns, predict problems, and help automate corrective action, reducing workload and resources needed to maintain real-time visibility across the network infrastructure.
- Prioritize user experiences: When ranking and addressing network issues identified through network observability practices, prioritize based on impact to user productivity and satisfaction. This will help prevent the accumulation of incident tickets that could hinder the team’s progress.
- Regularly evaluate and improve: Schedule routine reviews for network performance, solutions implemented, and observability practices to adapt your strategy as your network demands evolve. Network observability solutions with a deep data lake can even support peer-to-peer benchmarking and evaluation, helping the team rank their network against enterprises similar in scale and structure.
What are HPE network observability solutions?
HPE offers a range of solutions that work together to deliver intuitive network observability and broad telemetry collection. It includes third-party observability with AI-powered automation guided by HPE AI ethics and principles and the industry’s largest network telemetry data lake, providing a unified interface for HPE Aruba Networking and hybrid Enterprise network environments.
- HPE Aruba Networking Central
- HPE OpsRamp
- HPE Aruba Networking User Experience Insight (UXI)
- HPE GreenLake for Networking
Why do enterprises need network observability?
When compared to network monitoring, network observability better addresses enterprise needs for a holistic, proactive approach to building and maintaining a modern network that can scale and keep pace with today’s rapidly changing digital landscape.
Network observability | Network monitoring | |
|---|---|---|
| Coverage for problem detection | Broader range of telemetry data collection and analysis, including network devices, clients, and applications. | Collects specific metrics and logs from network devices. |
| Depth of insights and visibility | Deeper insights that correlate data from various sources, enabling faster RCA and proactive issue resolution. | Surface-level view of network performance that may require manual intervention to diagnose issues. |
| Proactive vs. reactive approach | Proactive, detecting anomalies and issues before they impact performance, often leveraging AI. | Typically reactive, alerting when predefined thresholds are breached. |
| User experience | Accelerated insights with faster and automated resolution, improved uptime and performance. | Slower issue resolution as operators face alert fatigue and manual troubleshooting. |
| Data collection | Diverse, unstructured data collection across packets, APIs, and telemetry. | Structured data gathered from SNMP and logs. |
