ADC03 Incomplete Observability

Observability is a critical aspect of modern application delivery, providing visibility into the health, performance, and usage of applications and infrastructure. However, incomplete observability—due to inadequate logging, monitoring, or alerting mechanisms—can lead to delays in detecting issues, prolonged disruptions, and a lack of insight into application performance. 

This lack of visibility becomes particularly problematic in complex environments, such as AI-driven applications, where real-time insights are essential. Below, we explore the impact of incomplete observability on key areas of application delivery and discuss best practices to overcome these challenges.

Consequences of Incomplete Observability

Impact on Performance

Incomplete observability directly impacts application performance by reducing the ability to proactively detect and resolve issues. Without proper visibility into system metrics, such as latency, response times, and resource utilization, it is difficult to pinpoint performance bottlenecks or identify the root causes of slowdowns. For example, insufficient monitoring can cause delays in detecting a CPU or memory spike, resulting in degraded application responsiveness. Real-time performance monitoring allows teams to track these metrics and make informed decisions, but without it, the application may suffer from prolonged response times and increased latency, ultimately affecting user experience.

Impact on Availability

Availability is another key area affected by incomplete observability. Systems with inadequate monitoring and alerting mechanisms may fail to detect outages or other critical issues promptly, resulting in extended downtime. When issues go unnoticed or unresolved, it leads to service interruptions, impacting users’ ability to access the application reliably. 

Critical application outages also cost more than $500,000 per hour for 32% of respondents. In contrast, those with full-stack observability reported a median outage cost that was 37% lower (New Relic).

For distributed systems, incomplete observability can cause cascading failures where an issue in one component triggers a chain reaction throughout the system. Without comprehensive observability, these types of failures may go undetected until they significantly disrupt availability.

Impact on Scalability

Incomplete observability also hinders scalability, as it limits the ability to understand resource usage patterns and predict demand accurately. For applications that experience variable traffic, such as AI applications with fluctuating workloads, real-time insights into traffic patterns and resource requirements are essential for scaling infrastructure effectively. Without these insights, organizations may under-provision or over-provision resources, leading to inefficient scaling and increased costs. Effective observability supports scalability by providing a clear understanding of usage trends and enabling automated scaling based on real-time demand.

Impact on Operational Efficiency

Lack of observability leads to reduced operational efficiency by increasing the time and effort required for troubleshooting and issue resolution. Incomplete logging and monitoring mean that IT teams may need to spend hours or days manually investigating problems, instead of quickly identifying and addressing them. This not only increases downtime but also diverts resources away from other critical tasks. 60% of IT professionals stated that fragmented monitoring tools hinder their ability to maintain a unified view of current operating conditions ​(CDInsights). ​

Furthermore, incomplete observability limits the ability to optimize applications and infrastructure based on performance data, reducing the organization’s ability to operate efficiently and cost-effectively.

Best Practices for Mitigating Incomplete Observability

To mitigate the challenges associated with incomplete observability, organizations should implement comprehensive monitoring, logging, and alerting solutions, as well as utilize tools like OpenTelemetry for standardized observability data collection. These solutions provide the visibility needed to ensure optimal performance, availability, and scalability.

Enhanced Monitoring and Logging

Implementing enhanced monitoring and logging practices is essential for achieving complete observability. Comprehensive monitoring tools, such as Datadog and Prometheus, offer real-time insights into system metrics, application performance, and resource usage. By setting up detailed logging for all critical components, teams can ensure they have a complete record of events and transactions. This data is invaluable for troubleshooting, as it enables teams to quickly identify and address issues, improving overall operational efficiency.

Standardized Observability with OpenTelemetry

Using OpenTelemetry allows organizations to standardize observability data across multiple environments, ensuring consistent data collection and analysis. OpenTelemetry provides a unified approach to collecting traces, metrics, and logs, which simplifies observability and enables more accurate cross-system comparisons. This standardization is particularly valuable in multi-cloud or hybrid environments, where consistency in data collection is crucial for comprehensive observability. By adopting OpenTelemetry, organizations can gain a holistic view of their applications, enabling them to identify issues across distributed systems and improve performance.

Dynamic Alerting and Automated Responses

Effective observability requires dynamic alerting mechanisms that notify teams of potential issues based on predefined thresholds and conditions. Dynamic alerting tools can automatically trigger alerts when anomalies are detected, allowing teams to respond swiftly to issues before they impact users. Additionally, integrating automated responses with alerting mechanisms enables systems to take corrective actions automatically, such as scaling resources during traffic spikes or rerouting traffic in response to a server failure. These automated responses improve availability and performance by reducing the reliance on manual intervention.

Conclusion

Incomplete observability in application delivery can lead to performance degradation, reduced availability, limited scalability, and operational inefficiencies. By implementing comprehensive monitoring and logging, adopting standardized observability with OpenTelemetry, and utilizing dynamic alerting with automated responses, organizations can overcome these challenges.

Strengthening observability is essential for maintaining high-performance, resilient, and scalable applications that meet the demands of today’s digital environment. Emphasizing complete observability not only enhances the user experience but also supports efficient, data-driven operations, positioning organizations for long-term success.