Data Observability for AIOps: Best Practices for Monitoring and Managing Telemetry Data

Modern software systems generate an enormous amount of operational data including logs, metrics, traces, and events that flow across several layers of applications, platforms, and infrastructure. AIOps (Artificial Intelligence for IT Operations) promises to turn this constant stream of telemetry into insights that can help teams detect issues faster and fix them quickly, reducing downtime and ensuring smooth operations.

But there’s a basic question that often goes unasked: Do we really understand the data we are feeding into these systems?

In many environments, AIOps struggle not because the AI is flawed, but because the telemetry data itself is unreliable, incomplete, or poorly understood. This is where data observability comes into the picture.

What Is Data Observability in AIOps?

Generally, data observability focuses on understanding the health and behavior of data as it moves through systems. In the AIOps context, it means being able to answer practical questions about telemetry data:

• Is telemetry arriving when it should?
• Is anything missing?
• Has its structure changed?
• Does it still reflect how the system actually behaves?

Traditional observability tends to focus on system performance using metrics like latency, errors, and availability. Data observability shifts attention to the data describing those systems. It treats telemetry as a data asset that should be continuously monitored, not just something to store and query whenever an incident occurs.

Why Telemetry Data Reliability Matters

AIOps systems learn from historical patterns. If telemetry data is inconsistent or incomplete, those patterns become misleading and can become irrelevant. Over time, this can reduce trust in alerts, anomaly detection, and automated recommendations.

For example, imagine a service that silently stops emitting certain logs after a deployment. From telemetry alone, the system may still appear healthy, but the data feeding AIOps no longer tells the full story. This leads to alerts that feel random, missed warning signs, or root cause suggestions that don’t make sense.

When this happens, teams often blame the AI. In reality, the issue started much earlier with the data.

Key Aspects of Telemetry Data to Observe

You don’t need an overly complex framework to start thinking about data observability. Many teams begin by paying attention to a few fundamental characteristics of telemetry data.

Freshness is one of the most important. Telemetry that arrives late can distort real-time analysis. Even small delays can matter when systems change quickly.

Structure is another critical signal. Format of the telemetry data evolve as applications evolve. Developers add new fields, rename or delete fields. When these changes happen without visibility, downstream observability systems can break silently.

There’s also volume and uniqueness. Sudden drops in log/metric volume or any unexpected spikes often point to instrumentation issues rather than real system behavior. These signals are easy to overlook if teams only focus on performance metrics.

Finally, consider distribution. Telemetry data usually follows recognizable patterns over time. When those patterns shift significantly, it’s worth asking why. Is the system behaving differently or is the data being captured differently? When the underlying data distribution changes, any pretrained ML model needs to be retrained to avoid model drift.

Best Practices for Improving Data Observability in AIOps

Improving data observability doesn’t require a complete overhaul. Small, intentional practices can significantly improve how telemetry data is understood and trusted.

1. Start with consistency. Clear and consistent naming for services, environments, and components makes telemetry easier to reason about. When logs, metrics, and traces share common identifiers, correlations become more reliable.
2. Observe the data pipelines themselves. If data ingestion fails or data is dropped along the way, those issues should be visible. Otherwise, data problems can be mistaken for system problems.
3. Establish simple baselines. What does normal telemetry look like during regular operation? Once that baseline is understood, deviations become easier to detect and investigate.
4. Most importantly, treat changes in telemetry as meaningful signals. When data changes, it deserves attention, even if no customer-facing issue has been reported yet. With the recent rise of reasoning models, these semantic shifts in system behavior can be identified by understanding the context behind these signals.

A Simple Example

Consider a microservices environment where a deployment unintentionally changes the schema and structure of error logs (maybe the telemetry SDK introduced a new format). The application continues running, and users may not notice any immediate issues. However, the logs feeding AIOps no longer match historical patterns.

Without data observability, anomaly detection may trigger confusing alerts or miss early warning signs altogether. With basic observability checks in place, the schema change is noticed quickly, the issue is traced back to the deployment, instrumentation is corrected and no major incident occurs which is the goal here.

Bringing It All Together

AIOps works best when telemetry data is treated as a first-class data asset. Data observability helps teams understand not just what their systems are doing, but whether the data describing that behavior can be trusted.

By paying attention to how telemetry data flows, changes and behaves over time, organizations can make AIOps more reliable and more explainable. This builds confidence in the observability stack and reduces the time spent questioning whether the data itself is the problem.

Before asking whether your AIOps system is working as expected, it’s worth pausing to ask a simpler question:

Do we really understand our data?