ServiceNow Machine Learning | Anomaly detection

Apply operational intelligence with

Algorithms & ML

The amount of data an enterprise is generating is staggering. To manage the digital complexity of a modern organization is not an easy task. Events, alerts, integrations, data-flows and thousands of devices are all feeding data; every minute of every day.

IT Operations today can generally be summed up in two ways, decentralized and complicated. Virtual servers, containers, multiple clouds, various monitoring tools and departments working at different speeds. Resources get created and destroyed in a matter of minutes.

This is where machine learning realistically can help. Algorithms can correlate current and historical data that operators can’t keep up with through the traditional manual processes and siloed tools. Enter the world of AiOps.

Real or hype?

The buzz around ML

We help organizations separate hype from reality.

700

Avrg. events per/m

40 %

Avrg. cost reduction per alert

300 K

Avrg. cost of downtime/h

ServiceNow Operational Intelligence & ML

ServiceNow Operational Intelligence and Machine Learning is a powerful set of capabilities built in to the platform. Through ML and algorithms you can route incidents automatically, correlate events, assisted root cause analysis and many more use cases. The primary basis of machine learning in ServiceNow is to enhance the workflow for the operators and humans working with issues. This is done through triaging data existing in the platform, external metrics, events and logs and creating correlations between them.

Anomaly Detection

Notice abnormalities in your flow of data and get anomaly alerts when a resource in the CMDB is not behaving normally.

IoT & Metrics

Leverage IoT devices in the CMDB of ServiceNow. Receive time-series data for the IoT devices and apply triggers on data series.

Human assisted help

It takes two to tango – have people teaching the machine learning and give feedback. This way more accurate data will be presented.

How does it work?

Process

Consolidate data

Build a solid CMDB including your devices, resources and data sources through discovery and integrations

Feed events & metrics

Start feeding events and metrics to your CMDB and get immediate ML-assisted correlation and analysis of the data.

Improve gradually

Over time the results will be refined and improved. Use human feedback and training for algorithms to raise accuracy.

Aiming to become a data-driven enterprise?

We'd love to help