In our blog series, we’ve debunked the following observability myths so far:

• Part 1: You can skip monitoring and rely solely on logs
• Part 2: Observability is built exclusively for SREs
• Part 3: Observability is only relevant and beneficial for large-scale systems or complex architectures
• Part 4: Observability is always expensive
• Part 5: You can create an observable system without observability-driven automation
• Part 6: Observability is about one part of your stack

In this post, we’ll tackle another fallacy that limits the potential of observability—that you can create an observable system without observability driven by automation.

Why is this a myth?

The notion that you can create an observable system without observability-driven automation is a myth because it underestimates the vital role observability-driven automation plays in modern IT operations.

In today’s complex and dynamic environments, traditional manual approaches fall short in delivering the agility, accuracy and scalability demanded by site reliability engineering (SRE) and DevOps practices.

Observability-driven automation leverages real-time insights from monitoring and telemetry data to inform intelligent automation processes. This synergy enables teams to detect anomalies, predict issues and respond proactively, ensuring continuous service availability and reliability. By automating incident responses, resource scaling and configuration adjustments, organizations can streamline operations, reduce human error and achieve the rapid iteration and deployment essential to SRE and DevOps philosophies.

Fact: Automation plays a crucial role in observability (and in any modern IT organization)

High-performing IT departments tend to release software more frequently, and trying to keep up manually is neither sustainable nor scalable. The variety of technology in use also means you won’t always have a subject matter expert (SME) on hand to assist in the setup and configuration of new applications. The fact is, automated setup and installation eliminates manual errors, reduces deployment time and improves consistency across different environments.

Automation streamlines the root-cause analysis process with machine learning algorithms, anomaly detection techniques and predictive analytics, and it helps identify patterns and anomalies that human operators might miss. Automated analysis reduces the time required to pinpoint the root cause and improves the accuracy of detection, leading to faster resolution times. The following are some benefits provided by automation:

• Real-time insights: Many observation and monitoring tasks require real-time analysis to detect issues and respond promptly. Manual observation cannot match the speed and accuracy of automated systems in identifying anomalies and performance degradation in real-time.
• Reduced human error: Manual observation introduces a higher risk of human error. Observing and interpreting data manually can lead to inconsistencies and oversight, potentially causing critical issues to be overlooked.
• Cost-effectiveness: Manual observation may require dedicated personnel, leading to increased labor costs. Automation, once set up, can operate continuously and efficiently without incurring additional human resource expenses.
• Historical data and trends: Automated systems can efficiently store and analyze historical data, enabling trend analysis and pattern recognition. This information is vital for capacity planning and performance optimization.
• Integrations: Automation allows for easy integration with various tools and platforms, facilitating a more comprehensive and cohesive observation ecosystem. This level of integration is challenging to achieve through manual efforts.

IBM’s approach to enterprise observability

IBM’s observability solution, IBM Instana, is purpose-built for cloud-native and designed to automatically and continuously provide high-fidelity data—one-second granularity and end-to-end traces—with the context of logical and physical dependencies across mobile, web, applications and infrastructure.

Our customers have been able to achieve tangible results using real-time observability.

“Our team is able to dedicate more time towards new features and roadmap planning instead of smashing bugs all day.” – Eddie Castillo, Head of Marketing, ExaVault Inc.

The team also noted that since ExaVault started using Instana, the mean time to resolution (MTTR) for customer-impacting bugs dropped by 56.6%. In addition, the platform’s slowdowns and downtime decreased substantially. It was at 99.51% uptime before, and it’s now at 99.99%. “We’re accomplishing the goal that we set out to do,” Fite explains. “The reason we were able to do that is we had better visibility into our problems.”

“We love how easy it is to deploy and maintain the agent. There’s no operational overhead”  –Grégory Schiano, Chief Technical Officer, Altissia

If you want to enhance your observability practices with full-stack visibility and the ability to monitor your cloud dependencies in real-time, we invite you to request a demo.

What’s next

Keep an eye out for our upcoming blog post, where we unravel another prevalent myth: “Observability is about one part of your stack.”