Understanding Observability: Core Concepts, SRE Methodology, AIOps, and Business Architecture

Since last year, the concept of observability has become a hot topic in the operations field, rivaling AIOps in popularity. It originates from control theory, where observability measures how well internal system states can be inferred from external outputs.

In IT, observability is commonly presented as the combination of Metrics, Traces, and Logs, often illustrated with diagrams such as the following:

A Splunk demo is shown to give a concrete feel for observability, though real‑world scenarios are far more complex.

The author identifies three essential components that give observability its "spirit":

SRE methodology – using Service Level Objectives (SLOs) at business, system, and application layers to detect issues without triggering alert storms.

AIOps algorithmic capability – applying anomaly‑detection algorithms to metrics, traces, and logs (e.g., KPI anomaly detection, tracing anomaly detection, log anomaly detection).

Understanding of business architecture – deep domain knowledge that informs which metrics matter, how they behave during events, and which critical paths should be monitored.

These pillars are illustrated with another diagram:

Effective observability requires mapping a business SLO → Critical Path → core application SLOs → component SLOs (containers, IAAS, etc.). Only with this hierarchy can AIOps provide meaningful insights.

The article concludes that current observability products only expose metrics, traces, and logs, but without the three pillars they lack true value; the depth of business‑scenario understanding ultimately determines the success of SRE and AIOps in practice.