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AIOps & Observability

Moving from proactive to predictive is possible. The ultimate state of an ITOM implementation for many is the holy grail of AIOps – AI for IT Operations. Being able to predict outages before they occur, work more smart with AI in monitoring teams, as well as automating root cause analysis and remediation is where the industry is heading.

AIOps & Observability is a complex area, where many companies have a scattered landscape of monitoring tools, metrics and logs. Many organizations make the mistake thinking that AIOps and Observability projects are mostly technically oriented sprinkled with a bit of Machine Learning. Truth is, it’s more about culture than technology.

Moving the IT department to observe states rather than simple alerts, to work with anomalies and predictions rather than reactive incidents – as well as seeing what the real business impact of an outage is a challenge. But most of the time, the challenge is not just in technology as much as it is in culture.

At Einar & Partners, we’ve pioneered and helped organizations to move from siloed ways of working to instead rely on AIOps for service delivery & IT.

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ServiceNow AIOps – Main Capabilities

“What is AIOps?” is a simple question but with sometimes a complicated answer. The product portfolio of ServiceNow AIOps gives a rich inventory of shelf-ready functionalities and capabilities to enhance IT Operations.

Logs & Traces

Through ServiceNow AIOps organizations can collect log data, traces and more to detect anomalies in systems, applications and services within the company. Through Machine Learning and statistical models, SRE teams can quickly notice when something is not behaving normal before it becomes a real issue.

Alert Clustering

Modern clustering techniques and algorithms to correlate alerts and events from multiple monitoring tools into common groups. Observe the domino-effect between different monitoring tools and environments, and how they affect each other. Cluster alerts based on CMDB data, text and more.

Metric Intelligence

Ingest metrics from critical infrastructure to generate statistical baselines of seasonality, expected behaviours, load and more. When something falls outside of the norm, ServiceNow AIOps generates instant anomaly alerts which can be acted upon.

Site Reliability Engineering

Map microservices, stateless architectures, containerized environments and more. SRE’s can use ServiceNow to quickly display and analyze the real impact when something goes wrong.

Pillars for a successful AIOps Implementation

Data Governance

What data to ingest, how is it formatted, and does it provide any value? Selecting the right data such as optimal metrics, traces and logs is key not to drown in too much data with too little value.

Data Model & System Impact

Anomalies and alerts can’t exist in a vacuum. For AIOps to be effective it must be connected to the business layer of IT. What services are ultimately impacted and how easy is it to see real system dependencies?

Business outcomes and use-cases

Where to apply AIOps and for what purposes? Ideally, AIOps should move beyond simple cases for the monitoring team but instead be correlated to business outcomes and drivers.

Connection to AI Strategy

What is the broader AI strategy at the company and how can AIOps play a part? Connecting an AIOps initiative to a strategic roadmap can make the difference between “experimenting” vs “central core capability” after the project is done.

Connection to ITSM & Beyond

Being able to connect the insights, analytics and recommendations of AIOps to ITSM and beyond is key. Seeing the correlation between changes, other open incidents, previous resolutions and knowledge base articles will boost an AIOps implementation.

Observability & AIOps Onboarding

How to onboard monitoring teams, application teams and other stakeholders to AIOps? Creating a clear and easy onboarding process is critical to be able to scale AIOps broadly across an organization.

More E&P Resources

Explore additional resources for more information

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