Predictive AIOps Monitoring

With ServiceNow’s Predictive AIOps, your organization can identify, predict, and resolve issues in its environment before they cause widespread issues that impact operations. With RapDev, go beyond the data logs that the ITOM Health module provides to access unparalleled anomaly detection and resolution insights.

Predictive AIOps and RapDev: Crush departmental data silos

The problem

In IT service operations, quickly diagnosing and resolving problems is crucial to keeping end users and resolutions teams happy.

To accomplish this, many organizations use ServiceNow to track and maintain the health of their infrastructure. While the real-time log data offered via ServiceNow’s ITOM Health module is helpful, DevOps teams can still struggle with solving and predicting system issues before they become widespread.

The solution

As a ServiceNow Elite Partner, Rapdev can help you get the most out of your investment. With every implementation, we take our customers’ unique infrastructure and needs into consideration.

Then, we provide recommendations that tailor optimization to best suit each team’s goals. Finally, we ensure that their Predictive AIOps instances are correctly integrated with other ITOM modules. This means your teams will be able to instantly receive alerts when an event occurs. You will have the ability to remediate through an automated, guided process. Not only will this reduce IT tickets and cut response times, but it will also boost productivity. 


Your ServiceNow Predictive AIOps Investment, Optimized 

Step 1

Automated data classification 

If your organization is already using ServiceNow’s ITOM Health application, then you’re familiar with the module's ability to connect to a variety of monitoring tools. This allows you to pull data to help gauge your infrastructure’s health.

Predictive AIOps automates this process. The tool ingests the data and classifies it. The result is simplified, scrubbed data that provides clear insight into issues. 

Step 2

Automated anomaly detection

Remember those data logs? Beyond classifying the data, Predictive AIOps also applies machine learning and advanced analytics algorithms to uncover patterns and correlate events in near real-time.

What does that look like? Predictive AIOps performance monitoring captures examples of healthy performance behavior in your infrastructure. Then, using that baseline data, AIOps monitoring identifies anomalous behavior before it turns into a major issue. 

Step 3

Automated remediation

In addition to Predictive AIOps’ performance monitoring capabilities, users can act on the insights pulled from ITOM’s Health Log Analytics via automatic, guided remediation recommendations.

Now, your organization can respond to and resolve errors faster. Predictive AIOps’s automated remediation allows your team(s) to collaborate within the platform. This avoids escalating errors to senior developers. Developers can spend time on what really matters, instead of putting out small fires. 


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