Why transition from monitoring to observability?

Written by Annie Badman

As IT environments grow more complex, traditional monitoring tools are struggling to keep up. The rise of cloud native architectures, microservices, and containerized applications has created highly interconnected systems that need a more comprehensive approach to visibility. These trends have driven the evolution of observability as a discipline, which goes beyond tracking system metrics to provide full insight into system behavior.

By correlating telemetry data across distributed environments, observability solutions help teams:

?? Identify root causes faster

?? Resolve issues proactively

?? Improve system reliability

The transition to observability is also being driven by necessity. Legacy monitoring tools are being retired in favor of observability platforms that can handle today’s technology demands. For example, IBM’s own Tivoli? is being phased out for Instana?, a next-generation observability solution.

Whether you’re actively migrating or just evaluating options, the following discussion can help clarify the state of play today.??

Monitoring vs. observability

At a high level, monitoring tells you what is happening, but observability explains why. Monitoring detects symptoms of a problem, while observability provides the context needed for deeper diagnostic analysis.

Traditional monitoring captures predefined metrics such as?CPU?usage and network?latency, offering a snapshot of system performance but little insight into why an issue is occurring. For example, monitoring might flag high CPU usage during performance degradation, but it won’t explain the root cause.

Observability takes system intelligence further by correlating multiple telemetry data types—metrics, events, logs and traces (MELT data)—to provide a complete, real-time view of IT environments. This view enables organizations to not only detect issues but also pinpoint their causes, anticipate failures and analyze complex behaviors across distributed systems.

At a high level, monitoring tells you?what?is happening, but observability explains?why.

Read more about the difference between observability and monitoring?→

Benefits of observability

Because observability extends beyond traditional monitoring, it can offer real-time insights that improve system performance, enhance resilience and optimize costs.

Key benefits include ?

Why now is the time to make the transition

While observability solutions have been on the market for years, many organizations are choosing now to make the move from traditional monitoring to observability.

Organizations that delay the transition to observability risk technical debt and a competitive disadvantage, while organizations that make the move gain faster issue resolution and greater efficiency.?McKinsey highlights?how observability can transform IT resilience, with one organization cutting incidents by 90% and slashing response times from hours to seconds.

Aside from the withdrawal of many legacy monitoring tools from the market, two of the most important factors driving observability adoption include increasing IT complexity and AI innovation.

Increasing IT complexity

With the complexity of modern IT environments—including hybrid cloud infrastructures, microservices and containerized workloads—traditional monitoring tools are no longer cutting it. These solutions, designed for stable, monolithic applications, cannot effectively manage the sophisticated technological ecosystems of modern enterprises.

Common limitations of traditional monitoring include:

• Gaps in visibility?across distributed systems, leading to undetected failures and unexpected downtime
• Slow incident resolution, delaying recovery efforts and increasing operational disruptions and costs
• Increased MTTD and MTTR, making it harder to meet?service level agreements (SLAs)?and maintain reliability
• Limited insight into cascading failures, resulting in misdiagnoses, recurring outages and prolonged performance issues

Observability solutions help address these limitations by providing comprehensive, real-time insights into technology infrastructure. These insights make it easier to spot and address issues faster, reducing downtime, protecting revenue and maintaining customer trust.

AI innovation and AIOps

Artificial intelligence (AI)?is transforming observability by helping teams analyze vast amounts of telemetry data, filter noise and surface critical issues in real time without manually sorting through logs and alerts.

Artificial intelligence for IT operations, or AIOps,?takes it a step further by using?machine learning?to detect patterns, reduce false positives and correlate events across complex systems. As a result, IT teams can cut through alert fatigue and isolate real issues more quickly.

By integrating observability with AIOps, organizations can streamline?incident response, reduce downtime and improve system reliability without extra manual effort. This shift moves teams from reactive troubleshooting to proactive system optimization, leading to faster insights and fewer disruptions.

Here's how to put observability into action →