What Observability Trends are Next for Cloud Native Observability

Hybrid IT infrastructure today spreads across multi-cloud environments growing in complexity. The cloud native proliferation has made it impossible to view, understand, and act on the vast amounts of data generated by distributed systems and hundreds to thousands of microservices. Containers are lightweight and have spurred massive innovation paired with Kubernetes that has made systems and services easy to integrate. If you can’t see what’s going on in your environments, you risk slower performance, outages, and longer resolution times. If you don’t have proper observability you could also be leaving money on the table because if you can’t see whats going on under the hood you can’t find ways to easily save.

To go beyond these challenges, organizations need a strategic approach to observability that accounts for emerging technologies, including AI.

The new paradigm is observability

Observability is a step beyond traditional monitoring. While monitoring focuses on predefined metrics and alerts, observability is about understanding the internal state of a system by analyzing its outputs like logs, metrics, and traces. Proper observability should tell you a story about your entire system with the ability to dig down into the details for precise measurements. 

A surge in the volume of telemetry data generated by cloud environments and its complexity, combined with the dynamic nature of microservices and serverless architectures, has introduced a level of intricacy that traditional monitoring tools simply can’t manage. 

Observability, on the other hand, provides a more comprehensive and flexible approach to understanding and managing these environments, enabling IT leaders to make data-driven decisions that align with both technical and business objectives. By the way observability tools are designed to integrate naturally with monitoring tools that you use providing a singular view.

Evolving challenges in observability

Data volume and cost

One of the most pressing challenges in the observability space is the sheer volume of data generated by cloud native environments, its expensive to ingest and retain. As Gartner® notes in Prepare for the Future of Observability, “The cost of observability has become a significant focus, influenced by prevailing macroeconomic conditions, consumption-based pricing, and increased scrutiny applied to cloud spend.”

To meet this challenge, IT leaders need to implement robust data management strategies. This includes creating policies for retaining different types of telemetry data based on compliance and operational requirements. For example, some data may need to be retained for longer periods due to regulatory compliance, while other data can be discarded more quickly to reduce storage costs. 

Additionally, adopting telemetry pipelines can help organizations control their data more effectively. Telemetry pipelines allow for filtering, discarding, routing, and transforming data, ensuring that only the most valuable insights are retained and analyzed.

Infrastructure complexity

Another significant challenge is the complexity of modern infrastructure, especially as organizations increasingly rely on a mix of virtual machines (VMs) and cloud native workloads. This hybrid environment can lead to silos and inconsistencies in monitoring, making it difficult to gain a unified view of system performance and health.

To address this, IT leaders should monitor virtualized workloads the same way they monitor cloud native ones. This involves using a consistent set of observability tools and practices across all environments. By doing so, organizations can ensure that they have a holistic understanding of their infrastructure, regardless of where the workloads are running. This consistency is crucial for maintaining application resilience and speeding up product delivery times.

Trends in observability strategies

As organizations face these evolving challenges in observability, several key trends emerge. Modern observability approaches involve centralizing teams and consolidating tools, adopting data standards like OpenTelemetry, and leveraging AI for deeper insights.

Centralizing teams and consolidating tools

The sprawl of IT systems can too quickly lead to monitoring sprawl. Gartner shares two recommendations on the theme of centralizing, one for teams and the other for tools.

Gartner recommends creating a Center of Excellence (COE) or a centralized observability team to build and continuously evolve observability strategies. This team can determine which data to collect from sources, eliminate the collection and retention of redundant data, and implement best practices for observability. By centralizing these efforts, organizations can ensure that their observability practices are consistent and aligned with business goals.

Additionally, Gartner advises standardizing the observability toolset to reduce tool sprawl. Standardization not only improves efficiency but also ensures that all teams are using the same tools and practices, leading to better collaboration and faster issue resolution.

Data standards: Open Telemetry (OTel)

Speaking of standardization, OpenTelemetry (OTel) is a Cloud Native Computing Foundation (CNCF) framework for observability. It enables microservices to provide metrics, logs, and traces, making it easier to collect and analyze telemetry data across different applications. One of the key benefits of OpenTelemetry is that it is an open, industry standard, supported by most observability vendors.

Unlike proprietary solutions, OpenTelemetry is actual code that can be customized and controlled. This flexibility allows organizations to tailor their observability practices to their specific needs, ensuring that they are not limited by the constraints of a closed-box solution. By adopting OpenTelemetry, organizations can also reduce software licensing costs and improve data management efficiencies.

Leveraging AI

AI plays a pivotal role in the strategic potential of observability. In Prepare for the Future of Observability, Gartner states, “AI plays a key role in observability’s strategic potential. AI/machine learning (ML) helps enterprises analyze the huge volume of data collected by observability tools efficiently, providing insights that are not humanly possible.”

The challenge lies in making sense of the ever-growing volumes of data and understanding the “why” behind deviations and anomalies. Traditional monitoring tools often fall short in this regard, leading to delays in getting the insights needed to inform action.

AI makes it easier to analyze the massive amounts of data collected by observability tools. By taking small, tangible steps in AI adoption, organizations can enhance their observability practices and stay ahead of the curve.

Source: Gartner

Some use cases of AI in observability include:

• Anomaly detection: AI can automatically detect unusual patterns in telemetry data, alerting IT teams to potential issues before they become critical.
• Probable cause analysis: AI can help identify the root cause of issues by analyzing multiple data sources and providing a comprehensive view of the system.
• Triage: AI can prioritize issues based on their severity and impact, allowing IT teams to focus on the most critical problems first.

By leveraging AI, organizations can quickly identify and resolve issues before they impact users.

Stay ahead of the curve

The future of observability is both exciting and challenging. As IT environments become more complex and data volumes continue to grow, IT leaders must adapt their strategies to stay ahead. By addressing key challenges and following strategic centralization and standardization, organizations can ensure they are well-prepared for the future.

To learn more about the latest trends and best practices in observability, download the Gartner “Prepare for the Future of Observability” report. This comprehensive guide will provide you with the insights and strategies you need to navigate the observability landscape and drive success in your organization.

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Gartner, Prepare for the Future of Observability Foundational, 18 September 2024, By Mrudula Bangera

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