Practical Observability for the Modern Enterprise

In our increasingly connected digital world, IT environments must be reliable and high-performing to deliver exceptional customer experiences. Yet, achieving clear visibility into their health and performance remains a complex challenge. In this article, we discuss practical observability strategies for modern enterprises to proactively detect issues, streamline troubleshooting, and optimize system performance.

Visibility challenges in a modern enterprise

The modern enterprise has a complex IT landscape. It can have tens if not hundreds of microservices talking to each other. These microservices could be running anywhere – from local servers and cloud compute instances to smart devices in a remote factory. To complicate things further, the microservices could also be talking to other layers e.g. the database and APIs.

With so many variables, IT Leaders and practitioners grapple with the following questions:

• How do I get a pulse of what is actually happening within my environment at any given moment?
• How can I predict issues before they even happen?
• What do I do to quickly recover from an incident?
• How do I make sense of all the telemetry data I am capturing?
• How do I standardize the different tools that my organization uses?

Why observability is essential

During the early years of my career, I used application performance monitoring tools such as Wily Introscope to monitor and troubleshoot issues in my Java and .NET environments. This was sufficient back then when applications were monolithic, and running on a static infrastructure.

Things are very different now:

1. Distributed architectures: A single transaction may traverse multiple services, containers, and cloud environments, making it difficult to trace issues.
2. Ephemeral systems: Applications are dynamic and highly scalable with components frequently spinning up and down.
3. Data overload: An enormous amount of telemetry data is generated by IT systems, complicating root cause analysis.

Traditional monitoring tools that are designed for stable, predictable environments, will be insufficient to address the above scenarios. We need a comprehensive approach which provides deep insights into the internal state of a system through a combination of metrics, logs, and traces. This is the essence of observability.

Robust observability is the bedrock of modern IT operations. For Site Reliability Engineering (SRE) teams, it offers the indispensable bird’s-eye view required to manage complex systems, meet Service Level Objectives (SLOs), and rapidly diagnose issues. Ultimately, every metric, log and trace entry serves one purpose: delivering a reliable, secure and delightful digital experience for your customers.

Gaining insights from data deluge

Let’s take an example of a smart retail store operations use case. Here you have a network of Smart Cameras capturing log events like queue times. The raw data is routed through an API Gateway, which serves as the secure entry point, logging request details and managing application traffic. A service mesh manages how microservices like “Foot Traffic Analysis” talk to one another, ensuring that the camera data flows reliably and securely between them for processing. Finally, the processed insights are stored in a central database, which logs query and transaction data. This complete data flow enables store managers to generate real-time reports and optimize store operations.

Imagine there is a system outage. IT teams will be overwhelmed by a flood of error notifications from various sources. The API Gateway logs failed requests, the Service Mesh reports connectivity errors, microservices continuously generate stack traces, and the database records timeouts and failed connections.

Extracting meaningful insights from the sheer volume of logs is a nightmare. It makes root cause analysis and troubleshooting incredibly difficult, prolonging recovery. Excessive logging leads to high costs, data noise and impact application performance. On the other hand, logging too little may leave critical blind spots.

A modern observability platform addresses this by automatically linking your large and disparate datasets into a single view. This lets IT teams quickly trace a transaction’s journey and identify the root cause, drastically reducing recovery time.

Aligning to proactive and business-aligned outcomes

In many organizations, the conversation is shifting beyond basic logging, monitoring and troubleshooting to more strategic approaches that drive proactive and business-aligned outcomes.

• Prognostics: Instead of just asking “What broke?”, we’re beginning to ask, “What is about to break?” By applying AI and machine learning models to historical telemetry data, organizations can identify anomalies and potential failures before they impact your users.
• Chaos Engineering: Chaos engineering involves intentionally injecting failures into a system to test its resilience. To do this safely, you need clear, detailed visibility so you can watch what happens, check if your assumptions are correct, and make sure safety measures are working.
• Business-Centric Metrics: The ultimate goal is to align observability efforts with top-down business metrics. While an SRE might track the uptime percentage, the C-suite leader may be interested in lost revenue per minute of downtime. According to this 2025 Observability Survey, 75% of respondents say observability is business-critical at either the CTO, VP, or director level, with CTO being the most common response (33%). Thus a mature observability strategy should be able to demonstrate how a technical metric impacts a business metric.

Promoting collaboration and shared responsibility

The story of the blind men and the elephant reflects the “observability culture” in many organizations. Development, operations, SRE, and security teams rely on their own tools and dashboards, giving them only a partial view of the system. When an incident occurs, everyone points fingers at one another, delaying recovery.

The most significant barrier to observability excellence is often organizational. Silos between teams result in friction, fragmented tooling, and limited insight.

We have to break down these silos and standardize our approaches so that we can promote a culture of collaboration and shared responsibility. Here’s how to make it happen:

• Establish a dedicated platform engineering team to centralize observability tools, freeing other teams to focus on their core responsibilities rather than becoming observability experts.
• Build standardized dashboards that serve the needs of multiple teams e.g. developers, SREs, business etc. This promotes a common lingo for discussing system performance across different teams.
• Adopt open standards like OpenTelemetry which ensure there is a standardized way of collecting observability data. This results in interoperability, effective analysis and clear communication. The 2025 Observability Survey shows that 79% of respondents have invested in OpenTelemetry.
• Unify data collection and correlation so that every team sees consistent, correlated data.

With standardization, observability stops being “someone else’s job” and becomes a team sport where everyone works together to keep systems healthy. With this proactive, business-aligned approach, the modern enterprise can gain clear, actionable visibility into its systems!

SUSE observability for practical and actionable insights

SUSE Observability provides a real-time pulse of your entire environment, converting raw telemetry into actionable insights. By leveraging precise monitoring with AI-driven techniques, it empowers you to anticipate problems proactively and recover from incidents faster. Its unified data model and open standards cut through data overload, making complex signals easy to interpret while streamlining and standardizing the diverse tools across your organization.

Download this e-book to explore how SUSE Observability helps IT address the practical aspects of observability.