Imagine this heartfelt conversation between a cloud architect and her customer who is a DevOps engineer:

Cloud architect: “How satisfied are you with the monitoring in place?”

DevOps engineer: “It is all right. We just monitor our servers and their health status – nothing more.”

Cloud architect: “Is that the desired state of monitoring you are looking at?”

DevOps engineer: “Not at all. We want to have an end-to-end single-pane-of-glass view for all our running systems.”

Cloud architect: “Then where is the hold-up?”

DevOps engineer: “Every team uses their own monitoring tool and we do not have a streamlined process for monitoring where we can have an overall understanding of how all our workloads are performing, and how we can debug better and improve the performance of our systems.”

Cloud architect: “And does the higher management know that there is a scope of improvement where their teams and others can have a transparency model for monitoring or observability, to term it properly?”

DevOps engineer: “The emphasis on monitoring is less. Although we would want to have a better solution in place, we are doing the bare minimum, and this topic gets less traction than the deployment of applications, securing the environment, or our other big data projects.”

Cloud architect: “I see.”

—————————-end of discussion—————————

The next day, the cloud architect conjures up an email and sends it across to the DevOps engineer and his team, proposing an approach to get started with the end-to-end observability layout. 

What does she hear back? Crickets.

Soon, she realizes that this is the case with a few other customers as well. There is a lack of standard processes, and monitoring or observability is perceived as a “metric/health tool” for their running systems from the higher management/C-suite. It led her to wonder how she could kick-start the discussion with the engineers and their higher management.

She recalled a quote by Frank Sonnenberg: “If you want to get anywhere, you have to start somewhere.” 

So, with that positive statement in mind, let’s help the cloud architect get started with “where to begin.” In this blog post, we will discuss the observability maturity model, what the different stages mean, and steps to move further up the maturity stages.

Although the term “observability” has been around for quite some time now, it is mistaken as monitoring but with a fancier name. Thereby, the observability maturity model will not only help you understand the difference between monitoring and observability but also provide you with an assessment of the current observability stage.

Understanding the Observability Maturity Model

The observability maturity model serves as an essential framework for organizations looking to optimize their IT infrastructure monitoring and management processes. This model provides a comprehensive roadmap for businesses to assess their current capabilities, identify areas for improvement, and strategically invest in the right tools and processes to achieve optimal observability. In the era of cloud computing, microservices, and distributed systems, observability has become a critical factor in ensuring the reliability and performance of digital services. 

At its core, observability is the ability to understand the internal state of a system by analyzing its external outputs. This concept has evolved from traditional monitoring approaches that focus on predefined metrics or events, to a more holistic approach that encompasses the collection, analysis, and visualization of data generated by various components in an IT environment. An effective observability strategy allows teams to quickly identify and resolve issues, optimize resource usage, and gain insights into the overall health of their systems. 

The first stage in an observability maturity model typically involves establishing a baseline understanding of the organization’s current state. This entails assessing existing monitoring tools and processes, as well as identifying any gaps in visibility or functionality. At this stage, organizations can take stock of their current capabilities and set realistic goals for improvement. 

Next, organizations can move towards a more sophisticated approach by adopting advanced monitoring techniques and tools. This may include implementing distributed tracing to gain insights into the interactions between microservices, or leveraging artificial intelligence and machine learning technologies to automate anomaly detection and root cause analysis. At this stage, organizations can begin to reap the benefits of increased visibility and more efficient troubleshooting processes. 

As businesses progress through the observability maturity model, they can leverage additional capabilities such as automated remediation and proactive alerting. These advanced features enable organizations to not only detect issues but also take corrective actions before they impact end-users or disrupt business operations. By integrating observability tools with other critical systems such as incident management platforms, organizations can streamline their incident response processes and minimize the time it takes to resolve issues. 

The most mature stage of an observability maturity model involves leveraging the wealth of data generated by monitoring and observability tools to drive continuous improvement. This can involve using advanced analytics to identify patterns and trends in system performance, as well as feeding this information back into development and operations processes to optimize resource allocation, architecture, and deployment strategies.

Let us expand on the stages of the maturity model in detail.

Stages of the Observability Maturity Model

The observability maturity model is directly proportional to the capability of the current infrastructure – as capability grows, so does the observability maturity level.

Stage 1: Basic monitoring – Collecting Logs, Metrics, and Traces

What does this stage mean?

Adopted as the bare minimum and worked in silos, basic monitoring doesn’t have a clear definition of what is required to monitor the totality of the systems or software in an IT organization. Most of the time, teams use different monitoring tools to assess the logs, metrics, or traces; however, these events are of little value in terms of debugging across or for optimization of the environment. 

How can you improve?

Assess the current state of maturity, which involves evaluating existing monitoring and management practices across disparate teams, identifying gaps and areas for improvement, and determining the overall readiness for the next stage.

A maturity assessment begins with business process discovery, infrastructure inventory and tool discovery, current challenges, and understanding business priorities and objectives.

The assessment will help identify the targeted metrics and KPIs that you expect to understand and see. It will also lay the foundation for further development and optimization of the current layout.

Stage 2: Intermediate Monitoring – Telemetry Analysis and Insights

What does this stage mean?

In this stage you can see organizations being more intentional in terms of collecting signals from their environments. They have devised mechanisms to collect application logs and created dashboarding and alerting strategies, and have the ability to prioritize issues based on well-defined criteria. When an issue arises, they are not totally shooting in the dark, rather they have a workflow that triggers multiple actions, and responsible teams are able to analyze and troubleshoot based on captured information and historical knowledge.

How can you improve?

Although monitoring seems to work well in most cases, organizations tend to spend more time debugging issues and as a result the overall mean time to resolution (MTTR) is not consistent or meaningfully improved over a period of time. Also, there is higher-than-expected wastage in terms of cost. There tends to be a data overload situation that overwhelms operations. We find most enterprises being caught in this stage without realizing where they could go next. Specific actions that can be taken to move the organization to the next level are:

• Review your systems’ architecture design on security at regular intervals and deploy least privilege access policies to reduce the attack surface, leading to fewer alerts.
• Prevent alert fatigue by defining actionable KPIs and add valuable context to the alert findings to help engineers resolve the issues faster.
• Analyze these alerts on a regular basis and automate remediation for common alerts.
• Use anomaly detectors to monitor anomalies and outliers that do not match the usual alert patterns.
• Share and communicate the alert findings with different teams and managers to get feedback on operational and process improvement.
• Develop a layout to gradually build a knowledge graph to step into correlation of different entities and understand the dependencies between different parts of a system. This enables you to visualize the impact of changes to a system, helping you to predict and mitigate potential issues.

Stage 3: Advanced Observability – Correlation and Anomaly Detection

What does this stage mean?

In this stage organizations can clearly understand the root cause of issues without having to spend a lot of time troubleshooting. When an issue arises, alerts provide highly contextual information to the DevOps teams. Users are able to look at an alert and immediately determine the root cause of the issue through signal correlation. They can look at a trace, find the corresponding log events while the trace was captured, and look at metrics from the infrastructure and applications – giving them a 360-degree view of the situation they are in. 

Teams can immediately take remediation action by having the appropriate developer or a DevOps engineer provide a fix that solves the issue. In this scenario, the MTTR is very small, the service level objectives (SLOs) are green, and the burn rate through the error budget is tolerable.

How can you improve?

Many high-tech organizations have achieved this level of sophistication and maturity in their observability environments. This stage already gives organizations the ability to support complex infrastructure, operate their systems with high availability, provide higher external service level availability (SLA) for their applications and ably support business innovation by providing quality infrastructure.

However, teams in such companies always want to go beyond the art of possibilities. Teams would like to understand repeated issues and create a knowledge base that they can use to model against scenarios, to predict issues that might arise in the future. That is where the next maturity stage comes in. To get there requires new tools, as well as new skills and techniques in storing and making use of the data needs to be identified. One can make use of machine learning to create systems that automatically correlate signals, identify root cause, and create resolution plans based on models trained using data collected in the past.

Stage 4: Proactive Observability – Automatic and Proactive Root Cause Identification

What does this stage mean?

This stage is essentially pushing observability to the left. Here, observability data is not only used “after” an issue occurs, rather make use of the data in real-time “before” an issue occurs. Using well-trained models, issue resolution can be made easier and simpler. By analyzing collected signals, the monitoring system here can provide insights into the issue automatically and also lays out resolution option(s) to resolve the issue.

While this is not a very common stage to find customers in, some enterprises have achieved such maturity in pockets where system complexity is minimal. Observability software vendors are expanding their capabilities into this space, and this has only accelerated with generative AI becoming a trend since ChatGPT became popular.

Once this stage matures and takes shape, we can see a situation where the observability services might automatically create dashboards dynamically based on issues presented at that moment. The dashboards could only contain information that is relevant to the issue at hand. This would save time and cost in querying and visualizing data that don’t really matter. 

While some of the capabilities in this stage might not be possible for most customers today, with large language models (LLMs) and compute to perform machine learning being democratized by the day, it may not be too long before we see such capabilities becoming more common.

Summary

The observability maturity model serves as a roadmap for organizations seeking to improve their ability to understand, analyze, and respond to the behavior of complex systems. By following a structured approach to assess current capabilities, adopt advanced monitoring techniques, and leverage data-driven insights, businesses can achieve a higher level of observability and make more informed decisions about their IT infrastructure. This model outlines the key capabilities and practices that organizations need to develop to progress through different levels of maturity, ultimately reaching a state where they can fully leverage the benefits of proactive observability.