Monitoring and observability are two distinctive practices yet they complement each other. Monitoring tells you what is happening and observability tells you why it’s happening. Neither one is replaceable. 

Observability has paved the way for operations to be driven by Artificial Intelligence, also known as AIOps.   As data collected has increased massively and become more complex, technologies utilizing data science and machine learning have led to the creation of the term AIOps.   What is AIOps?  According to Gartner, “AIOps platforms analyze telemetry and events, and identify meaningful patterns that provide insights to support proactive responses.”

Traditional IT operations teams collect and monitor metrics data from on-premise IT devices.  The ops teams typically respond and manage alerts reactively, leading to the IT specialists working on mundane tasks.   AIOps allow the collection of data beyond metrics data, to logs and traces, and beyond on-premise to hybrid and multi-cloud infrastructure, to allow for a holistic view of what is truly happening in the enterprise IT.   AIOps promises to take in all the massive data, automate the mundane tasks, and provide actionable insights to the IT ops teams.

Gartner further explains that AIOps platforms have five characteristics: “Cross-domain data ingestion and analytics; Topology assembly from implicit and explicit sources of asset relationship and dependency; Correlation between related or redundant events associated with an incident; Pattern recognition to detect incidents, their leading indicators or probable root cause; Association of probable remediation.”

Some real life examples of how AIOps help the IT operations team include:

 

The monitoring system is generating hundreds of alerts.   The IT operator is flooded with the alerts as he or she tries to understand the underlying issues.  AIOps helps to lessen the noise while helping to get to the root cause.

 

 

 

 

 

Another example is that with in running multiple systems, the human operator may not be able to detect subtle differences in performance. AIOps help to identify anomalies that are not visible to the human eye.

 

 

 

AIOps – helping users automate monitoring and gathering useful insights

By applying AIOps to the three pillars of observability – metrics, logs and traces, help improve operations management by gathering useful insights from massive data collected. Here’s how we applied AI to observability to help:

Feel free to reach out to us to find out more!