Network observability tools explained
Network observability tools emerged as an evolution of performance management products that aggregate data such as queue statistics, error counters, and log metrics from myriad sources on-premises and in the cloud to convey in near real-time the health of services, applications, infrastructure, and other components in today’s hybrid environments. Network observability tools provide information on the health and behavior of applications, offer insights into end-user experience, and detect anomalies that are indicative of security incidents.
This evolution of observability technology follows increasing enterprise trends toward multi-cloud deployments and hybrid work, which fuel a growing need for IT teams to gain visibility across owned private and public networks, including service provider environments. With data collected from a wider ranges of sources, network observability tools promise to provide actionable guidance on how to resolve performance problems before they impact end users and customers. The data aggregated by observability tools can uncover security issues and drive collaboration between network and security teams to identify, troubleshoot, and prevent incidents from impacting the business.
Now in the era of artificial intelligence, network observability technologies in some instances also automatically escalate events and speed problem resolution. Using AI and intelligent automation, network observability tools can more quickly and accurately analyze data from varying sources to determine the root cause of an incident and mitigate the impact across an enterprise environment.
“IT professionals recognized network observability as something deeper than network monitoring, moving beyond the collection and presentation of data that most network monitoring tools excel at today. They hinted at a system that turns data into knowledge and actionable insights,” according to the report, Network Observability: Delivering Actionable Insights to Network Operations, by research firm Enterprise Management Associates (EMA).
Network observability tools promise to speed and simplify the daunting task of collecting, analyzing, and making sense of volumes of data across hybrid environments to empower IT teams to optimize performance, improve service levels, and reduce security risk.
In this buyer’s guide:
- Why enterprises need network observability tools
- What to look for in network observability tools
- Major trends in network observability tools
- Leading network observability vendors
- 10 key questions to ask yourself before buying network observability tools
- 10 key questions to ask vendors about network observability tools
- Essential reading
Why enterprises need network observability tools
Enterprise IT teams today must manage highly dynamic, diverse environments. Network observability tools collect data needed to gain a deep understanding of complex environments and lessen the negative impact of performance and security incidents.
In the past, standardized and static networks enabled IT managers to more easily monitor the health and performance of infrastructure, applications, and services. Now the path an application takes to complete a request for an end user traverses from back-end systems to the cloud to service provider networks and end-user devices – which include a range of laptops, tablets, and smartphones. Also adding to the complexity is the reality of hybrid work most businesses must now support, putting pressure on IT teams to deliver the same level of performance and security to in-office, remote, and mobile employees. Network observability tools collect, analyze, and visualize data from multiple components to help IT organizations maintain reliability, security, and efficiency across their enterprise environment that spans beyond the physical network perimeter.
There are several factors in today’s digital environments driving the need for network observability tools. To start, enterprise networks are extremely complex, powering sophisticated applications that are supported across distributed systems. IT teams must manage on-premises components as well as external cloud-based environments and service provider networks as part of their overall network. By monitoring traffic patterns, latency, packet loss, bandwidth, and other metrics across many devices, IT teams can get views into performance, identify indicators of potential security incidents, and spot deviations from expected performance to more quickly resolve problems before they impact end users and customers.
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Network World
Observability tools are also being called upon to help collaborative IT and security teams more quickly identify when a security attack is the cause of network events, for instance. By bringing data from multiple sources together for analysis, observability tools can help IT teams understand if network events belie a security threat. Most enterprises have a proliferation of monitoring tools, all generating alerts in their own manner, but observability tools promise to normalize the data and dissect what it means for IT managers.
“Part of the problem is that when data is scattered across multiple tools, the experience and context is lost. Network observability tools should stitch a lot together. Yes, you could see the same data cross five different tools, but how observable is it if John must mentally piece it together?” said Carlos Casanova, principal analyst at Forrester Research. “Sometimes network metrics will reveal a bad actor on the network that the security team would recognize but would not necessarily be picked up by network teams.”
In the same vein, network observability tools also promise to reduce the noise created from many systems throwing off alerts and events – bubbling up the information that is most important and relevant to network managers. Simply put, there are just too many systems and components generating data for human operators to gather and examine. Network observability tools reduce the work needed to collect and analyze volumes of data, and they also explain how the data will impact services when correlated with other events or incidents occurring across the network.
Network observability tools also thrive on data. The data collected makes the technology more intelligent about the environment it is monitoring and enables IT teams to make data-driven decisions on their networks. Not only can network observability tools drive better decisions for real-time monitoring, but they can also highlight areas for improvement to help network managers deliver higher-quality services and better performing applications.
“Network observability is driven by a host of industry leading technologies and practices. Cloud services, machine learning, artificial intelligence, network virtualization, automated operations, real-time telemetry, programmatic interfaces, open source, … all and more are combining to serve the complex needs of network and IT management in this digital era. Blending all the previously mentioned technologies and practices and more into a coherent and comprehensive network and larger IT observability effort is a challenge for enterprises, service providers, and solution suppliers,” said Mark Leary, research director for network analytics and automation at IDC, in a statement.
What to look for in network observability tools
Network observability tools vary across vendors, but there are common capabilities that are essential to a successful implementation. There is no one-size-fits-all network observability tool for every environment. Understanding the capabilities available from providers will help IT buyers determine which tool might be the best fit for their specific environment. Ultimately, IT leaders need to be confident that they know how their applications and services are performing before a problem occurs.
Data aggregation for multiple systems and roles
Network observability tools must collect data from many sources and make sense of the data for stakeholders across multiple teams. Network observability tools not only need to present the data collected from sources, but they must also explain how the data will impact services when correlated with other events or incidents occurring across the network.
Data visualization that puts metrics in context
Network observability vendors must enable their tools to provide data visualization capabilities for multiple stakeholders. Data from various sources might not mean much when seen isolated, but when correlated across components it could tell a different story.
Dashboards specific to IT stakeholders
Providers should offer dashboards that provide meaningful information gleaned from the data to network, security, DevOps, site reliability, and other teams will be much more valuable than a tool that performs just raw data collection.
Noise management to prioritize events
Network observability tools must not only collect and visualize volumes of data, but they must also manage the noise for IT operators—shining a spotlight on the alerts that matter. Traditionally, network operators would be expected to troubleshoot and triage the various data points collected across tools to understand what it might mean for overall performance. Now network observability vendors promise to bubble up the potential performance impact when events from multiple sources happen simultaneously.
Traffic analysis that includes provider networks
Network observability tools should see the full path traffic takes, including multiple internet and cloud service provider environments, to identify locations where performance degrades. For example, leading tools can analyze how traffic is being routed across the internet and myriad service providers to not only detect performance issues but also suggest areas for optimization. These tools should also be able to show enterprises where their traffic is going and analyze the data “in-flight” and not only at rest. For instance, BGP routing issues could stem from service provider configuration issues, and network observability tools will empower enterprises with the data to attribute the performance issue to their provider.
AI, automated actions, and actionable insights
Network observability tools should be able to automatically escalate the events that will most impact performance. AI and machine learning (ML) will be critical in the level of automation enterprise IT leaders will be able to apply with network observability tools. AI-driven capabilities are still emerging, but IT buyers should make sure to understand how AI and ML will use pattern matching of data to improve root-cause analysis and understand predictive situations.
Integration among existing tools
Network observability tools should work with other network, systems, and performance monitoring tools that enterprise IT leaders have already invested in for their environments. When evaluating tools, inquire about partners, technology support, and APIs for this type of tool in part due to the data that must be collected and correlated across systems.
Multi-cloud support and contributions from cloud providers
To get a full picture, network observability tools must also be able to see into cloud networks. Fortunately, cloud vendors are providing more insights into their environments to help companies understand how they could be impacting performance.
Standard support for telemetry data
Part of the integration requirements for network observability can be addressed with a standard known as OpenTelemetry. OpenTelemetry is a framework that enables tools to capture telemetry data from cloud-native software, and it looks to collect observability data from traces, metrics and logs. This type of standard support in network observability tools is becoming a must-have feature because of how important it is for tools to share data so IT can get the complete picture and identify anomalies that might impact performance.
Major trends in network observability tools
A few key trends are shaping the network observability market for IT buyers, and industry watchers expect providers to compete for customer dollars by accelerating their use of AI and automation, offering cloud-based services, and enhancing data insights with predictive capabilities.
“The outlook remains bright for enterprise network observability spending owing to the crucial role that this management technology plays in delivering intelligence, insights, and automation to the network (and networking staff),” IDC stated in a recent report.
Increased collaboration among IT teams
Observability is a key area in which network, security, and other IT teams are collaborating more. For enterprise organizations, breaking down silos between teams can lead to improved collaboration as well as speed problem resolution and improve application and service performance. Using shared data enables teams to act with the context of their environment while also understanding the impact to other areas of the environment. According to IDC, “Doing so should improve collaboration since it reduces or eliminates conflicting conclusions surfaced by tools that analyze different and incomplete data sets.”
Root-cause analysis and predictive capabilities
Observability tools should speed problem resolution by correlating data from multiple sources and pointing network operators to the likely cause. Looking ahead, network observability tools should help enterprise IT organizations avoid problems, accelerate digital rollouts, and accurately time network upgrades based on intelligence gathered with the data. Network observability is moving toward a more proactive and predictive network and IT management model, according to IDC, because there is “demand for solutions that ease operation and promote proactive optimization and ready innovation.”
Automated actions driven by data
Automating repetitive tasks across operations has long provided gains in staff productivity, but today’s environments require more advanced automation driven by intelligence gathered with context-rich data. Coupling data analytics with automation will delivers more precise and proactive management actions that could enable networks to achieve greater levels of intelligent automation. “Observability solutions that direct or even take actions with minimal or no operator involvement, move the network closer to the level of automation available within the computing infrastructure,” IDC states in a report.
Cloud-based offerings
Industry watchers expect to see investment in on-premises network observability tools fall off in favor of cloud-based offerings. “IDC forecasts very strong growth (a CAGR of 24.3% for 2022–2027) for cloud-based network observability solutions,” according to an IDC report. “These SaaS-based solutions account for all revenue growth in enterprise network observability over the forecast period.”
Leading network observability vendors
Network observability tools providers range from well-known names with a long history in network management and upstarts looking to make a name for themselves in observability.
BMC Software: BMC Helix for Observability and AIOps includes software that monitors environments to proactively identify issues before they impact the business and speed time to resolution.
Broadcom: AIOps and Observability products from Broadcom correlate data across users, applications, infrastructure, and network services, then apply machine learning, analytics, and automation to provide visibility across the environment and deliver actionable insights to IT operators.
Catchpoint: Catchpoints Internet Performance Monitoring (IPM) platform is designed to proactively catch issues across the internet stack and provide IT teams with a holistic view of customer and workforce digital experiences.
Cisco: Cisco Observability Platform brings data together from multiple domains to provide visibility into application performance, user experience, and security incidents, and then correlates the data with business outcomes to speed problem resolution and derive insights with business context. Cisco’s Observability Platform incorporates technology from both its AppDynamics and ThousandEyes acquisitions.
Datadog: Datadog Observability Platform provides visibility across systems, applications, and services in a single platform, while creating actional context to avoid downtime, reduce costs, and perform AI-powered anomaly detection.
Dynatrace: Dynatrace Observability platform captures and unifies the dependencies between all observability data to intelligently combine metrics, logs, traces, user experience, and security data.
Elastic: Elastic Observability automates anomaly detection and accelerates root-cause analysis by shifting from manual data collection to interactive exploration with generative AI.
Gigamon: Gigamon Deep Observability Pipeline provides real-time network intelligence derived from packets, flows, and application metadata to deliver defense-in-depth and complete performance management across hybrid and multi- cloud IT infrastructure.
Honeycomb: Honeycomb Observability Platform organizes telemetry data for accurate exploration from a common user interface regardless of data type, enabling IT operators to determine the cause of issues for a single user or identify complex patterns across multiple users and services.
Kentik: Kentik Network Observability Platform is delivered as a service and helps network managers monitor the digital experience across multiple platforms using AI and machine learning.
New Relic: New Relic Observability Platform is a cloud-based offering that monitors applications and services in real time to provide insights into software, hardware, application, and cloud performance.
Observe: The Observe Observability Cloud architecture uses a data lake to unify telemetry, a data graph to map and link relevant datasets, and data apps to get data into Observe and provide logical starting points for using that data, which contributes to faster troubleshooting.
Riverbed: Network Observability monitors, troubleshoots, and analyzes what is happening across hybrid network environments, providing end-to-end visibility and actionable insights that can help proactive resolve performance issues.
ServiceNow: ServiceNow Cloud Observability unifies critical telemetry data in a unified platform to resolve cloud-native service issues faster, enhance cross-team collaboration, and deliver better business outcomes.
SolarWinds: SolarWinds Observability delivers unified and comprehensive visibility for cloud-native, on-premises, and hybrid custom and commercial applications to help ensure optimal service levels and end user satisfaction with business services.
Splunk (Cisco): Splunk Observability Cloud provides unified visibility and real-time troubleshooting across any environment, helping IT operators to spot issues that impact business and accelerate mean time to repair by combining related data visually.
10 key questions to ask yourself before buying network observability tools
10 key questions to ask vendors about network observability tools
Essential reading
- Network observability tools promise benefits, but obstacles hinder results
- Survey: Observability tools can create more resilient, secure networks
- New Relic brings business context to its observability platform
- SolarWinds amps up observability software with AI
- Network observability: What it means to vendors and to you
- Arista goes big with network observability platform
- Cisco observability: What you need to know
- Cisco aims for full-stack observability with AppDynamics/ThousandEyes tie-in
- Kentik boosts observability platform with genAI
Source:: Network World