Your BI Platform Has Five Monitoring Tools and No Observability

The report explains the challenges faced when using a global business intelligence platform that consists of multiple components, each with its own monitoring tools. Despite having monitoring in place, the true operational problem lies in the inability to observe the accuracy of the results. The author describes a common scenario where a small change in a data source leads to discrepancies in forecasts, causing confusion and the need to dig through logs to identify the root cause.

The monitoring system focuses on the fact that the job ran successfully, rather than verifying if the results are accurate.

The author argues that traditional approaches, such as relying on runbooks and senior engineers, have limitations. Correlating issues across multiple systems becomes increasingly difficult as the number of systems grows, and it is a task that becomes even harder for humans to handle as the complexity increases. The author believes that generative AI can be useful in this context, specifically for triage purposes rather than generating new content.

By using AI to read and correlate heterogeneous machine output across different systems, it becomes possible to identify unusual patterns and provide ranked hypotheses with supporting evidence. This approach allows engineers to focus on analyzing the correlations and making informed decisions, rather than spending time on manual correlation tasks.

The measurable result of implementing this approach is a significant reduction in the time taken to locate the root cause of an incident, freeing up resources for more critical tasks. The same reasoning can be applied to security posture, where correlating vulnerability findings with the system's context can improve the effectiveness of security measures.