Succeeding with observability in the cloud

In its 2024 report, Splunk breaks down observability practices into four stages: foundational visibility, guided insights, proactive response, and unified workflows. Based on that, it classifies companies into four stages of observability maturity: beginning, emerging, evolving, and leading.

Splunk found that just 11% of organizations in the study have reached that final stage, and only 8% in the United Kingdom, suggesting that British businesses need to catch up to other countries regarding observability.

Observability versus monitoring

Keep in mind that Splunk is not a disinterested third party; it sells observability tools. I’m not saying they are spinning their statistics, but you’ll never hear a cloud observability company say that cloud observability is not an issue. As always, you need to consider the source to decide if their analysis has some wisdom. In this case, it does.

Observability is the ability to understand what is happening inside a system based on the external data it generates, such as logs, metrics, and traces. This data offers insights into the system’s internal state without directly interacting with it.

In the rapidly evolving world of cloud computing, observability has emerged as a critical asset for organizations navigating the complexities of cloud ecosystems. Cloud architectures have become increasingly dynamic and diverse. Multicloud deployments that span multiple cloud providers and on-premises environments have made gaining meaningful insights into system behavior not just advantageous but essential.

Observability is distinct from traditional monitoring and transcends mere reactive metrics and static dashboards. Monitoring often involves passively consuming information to assess system states. Observability takes an integrative and proactive approach to evaluate the holistic state of systems. It leverages the vast array of data, enabling IT teams to understand current system conditions, anticipate future states, and optimize performance accordingly.

Complexity makes observability a necessary evil

The complexity of modern cloud environments amplifies the need for robust observability. Cloud applications today are built upon microservices, RESTful APIs, and containers, often spanning multicloud and hybrid architectures. This interconnectivity and distribution introduce layers of complexity that traditional monitoring paradigms struggle to capture. Observability addresses this by utilizing advanced analytics, artificial intelligence, and machine learning to analyze real-time logs, traces, and metrics, effectively transforming operational data into actionable insights.

One of observability’s core strengths is its capacity to provide a continuous understanding of system operations, enabling proactive management instead of waiting for failures to manifest. Observability empowers teams to identify potential issues before they escalate, shifting from a reactive troubleshooting stance to a proactive optimization mindset. This capability is crucial in environments where systems must scale instantly to accommodate fluctuating demands while maintaining uninterrupted service.

The significance of observability also lies in its alignment with modern operations practices, such as devops, where continuous integration and continuous delivery demand rapid feedback and adaptation. Observability supports these practices by offering real-time insights into application performance and infrastructure health, allowing development and operations teams to collaborate effectively in maintaining system reliability and agility.

The steps to observability success

The path to effective observability travels through a landscape of complex dependencies, distributed systems, and rapidly changing technologies. To effectively implement observability for their cloud deployments, enterprises should consider the following actions:

Cultivate a culture that prioritizes observability as a fundamental part of the development and operations processes. This involves recognizing the value of data-driven insights for optimizing performance and reliability.

Deploy robust observability tools that can collect, analyze, and visualize data from logs, metrics, and traces across all cloud infrastructure components. Ensure these tools integrate seamlessly with existing systems and support multicloud environments.

Leverage artificial intelligence and machine learning technologies to process large volumes of data and identify patterns or anomalies that could indicate potential issues. A proactive approach can help preempt problems before they impact users.

Share insights and dashboards to encourage collaboration between development, operations, and business teams. This alignment helps rapidly address issues and improves overall system performance.

Gain visibility into the entire application stack, from infrastructure to user experience. With a comprehensive view, you can understand how applications perform in real-world scenarios.

Assess observability practices and tools regularly to ensure they meet evolving business needs. Stay updated with the latest advancements in observability technologies and methodologies to continually enhance capabilities.

Equip teams with the necessary skills and knowledge to effectively utilize observability tools. Investing in employees can lead to better analysis, troubleshooting, and system optimization.

By taking these steps, enterprises can harness the full power of observability, leading to improved system reliability, faster incident response, and a more robust overall cloud strategy. This is way more complex than the current studies will lead you to believe, so you’re going to have to make some strides on your own.