AIOps platform

LogicMonitor’s AIOps platform enables businesses to see what’s coming before it happens. For engineers, this includes spending less time troubleshooting and more time innovating. AIOps delivers AI and machine learning that provide context, meaningful alerts, illuminate patterns, and enable foresight and automation.

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The AIOps early warning system

LogicMonitor’s Early Warning System will detect the warning signs and symptoms that precede issues, such as patterns or anomalies in alerts or performance data, and warn users accordingly. These early warnings will be able to trigger actions, such as integrations and custom scripts, to prevent issue occurrence. By warning users sooner, this Early Warning System will help enterprises prevent outages, saving them time, money, and avoid negative impact on their brands.

The AIOps early warning system
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Enabling streamlined operations with AIOps

Data Forecasting Icon

Data forecasting

Get insights into future trends to proactively prevent issues before they occur.

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Root cause analysis

Automatically discover correlations between resources to find the source of issues faster and increase MTTR.

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Dynamic thresholds

Using powerful anomaly detection, only get alerted for issues that arise outside of the operating range of a resource.

The future of IT operations is now

At LogicMonitor, we believe technology should enable businesses to see what’s coming before it happens. For engineers, this includes spending less time troubleshooting and more time innovating. LogicMonitor’s o11y platform delivers AI and machine learning that provide context, meaningful alerts, illuminate patterns and enable foresight and automation.

Dynamic thresholds

Dynamic thresholds use anomaly detection algorithms to detect a resource’s expected range based on past performance and limit alert notifications to those that correspond to values outside of this range (i.e. anomalies). Dynamic thresholds will catch anomalies in metric values, metric rate of change, and even metric patterns (such as a drop in traffic where it isn’t normal). In addition to ensuring that alerts are generated for anomalies, dynamic thresholds can be used to reduce noise where static thresholds aren’t tuned well, so you can ensure your team is focusing on what’s really important.

Increase IT efficiency: Customizing thresholds can be time-consuming & difficult for large environments. With dynamic thresholds, we’ll ensure that alerts are sent for anomalies, eliminating the need for manual management of monitoring thresholds & enabling you to increase your monitoring ROI.

Detect issues sooner: Dynamic thresholds enable teams to understand expected performance and where performance deviates from what’s normal & needs attention before these deviations are caught by traditional thresholds.

Root cause analysis

With root cause analysis, LogicMonitor uses automatically discovered relationships between monitored resources to identify the root cause for triggered alerts and notify users of the originating issue, while preventing notifications for dependent resources in alert. When a core or root device goes down affecting connectivity for downstream devices, Root Cause Analysis (RCA) will identify the originating and dependent resources and subsequent alerts and disable notifications for dependent resources.

Avoid alert fatigue: Users are only notified for the root cause issue allowing them to focus on what’s important without getting overwhelmed by dependent side-effect issues.

Improve MTTR: Alert notifications that identify the root cause and filterable in-app alert data enable your team to zero in on resources that play a key role in outages & more quickly identify and resolve issues.

Forecasting

LogicMonitor’s data forecasting allows you to predict future trends for your monitored infrastructure, using past performance as the basis. Forecasting is an AIOps tool that is very helpful for issue diagnosis and mitigation and can help you determine whether an alert represents a one-time anomaly, requires immediate attention, or will require attention in the near future.

Proactively prevent issues: Forecasting can help you identify upcoming issues before they trigger alerts, so you can prevent downtime.

Budget planning and resource management: Infrastructure components that have lifetimes or capacity associated with them, forecasting based on the predicted health and performance of your monitored devices can provide insight into the timeframe and magnitude of recurring events, as well as upcoming expenses.

The future of IT operations is now

At LogicMonitor, we believe technology should enable businesses to see what’s coming before it happens. For engineers, this includes spending less time troubleshooting and more time innovating. LogicMonitor’s o11y platform delivers AI and machine learning that provide context, meaningful alerts, illuminate patterns and enable foresight and automation.

Dynamic thresholds

Dynamic thresholds use anomaly detection algorithms to detect a resource’s expected range based on past performance and limit alert notifications to those that correspond to values outside of this range (i.e. anomalies). Dynamic thresholds will catch anomalies in metric values, metric rate of change, and even metric patterns (such as a drop in traffic where it isn’t normal). In addition to ensuring that alerts are generated for anomalies, dynamic thresholds can be used to reduce noise where static thresholds aren’t tuned well, so you can ensure your team is focusing on what’s really important.

Increase IT efficiency: Customizing thresholds can be time-consuming & difficult for large environments. With dynamic thresholds, we’ll ensure that alerts are sent for anomalies, eliminating the need for manual management of monitoring thresholds & enabling you to increase your monitoring ROI.

Detect issues sooner: Dynamic thresholds enable teams to understand expected performance and where performance deviates from what’s normal & needs attention before these deviations are caught by traditional thresholds.

Root cause analysis

With root cause analysis, LogicMonitor uses automatically discovered relationships between monitored resources to identify the root cause for triggered alerts and notify users of the originating issue, while preventing notifications for dependent resources in alert. When a core or root device goes down affecting connectivity for downstream devices, Root Cause Analysis (RCA) will identify the originating and dependent resources and subsequent alerts and disable notifications for dependent resources.

Avoid alert fatigue: Users are only notified for the root cause issue allowing them to focus on what’s important without getting overwhelmed by dependent side-effect issues.

Improve MTTR: Alert notifications that identify the root cause and filterable in-app alert data enable your team to zero in on resources that play a key role in outages & more quickly identify and resolve issues.

Forecasting

LogicMonitor’s data forecasting allows you to predict future trends for your monitored infrastructure, using past performance as the basis. Forecasting is an AIOps tool that is very helpful for issue diagnosis and mitigation and can help you determine whether an alert represents a one-time anomaly, requires immediate attention, or will require attention in the near future.

Proactively prevent issues: Forecasting can help you identify upcoming issues before they trigger alerts, so you can prevent downtime.

Budget planning and resource management: Infrastructure components that have lifetimes or capacity associated with them, forecasting based on the predicted health and performance of your monitored devices can provide insight into the timeframe and magnitude of recurring events, as well as upcoming expenses.

AIOps benefits

Efficiency

Perform

Identify

Improve

Prevent

Transform

Increase IT efficiency

Customizing thresholds can be time-consuming and difficult for large environments. With dynamic thresholds, we’ll ensure that alerts are only sent for anomalies, eliminating the need for manual management of monitoring and enabling you to increase your monitoring ROI.

AIOps frequently asked questions

What is AIOps?

AIOps which stands for Artificial Intelligence for IT Operations, is a method for analyzing and displaying data for IT teams based on machine learning algorithms. The AI used in AIOps is often based on historical patterns coupled with current learned data trends.


Is LM’s AIOps really using AI?

Yes. LogicMonitor’s AIOps goes beyond simple machine learning and pattern detection to learn and report based on individual relationships within each company’s tech stack.


What is root cause analysis?

Root cause analysis is the process of finding the core of an issue that caused a chain reaction effect ending in problems.


What is anomaly detection?

Anomaly detection is the identification and notification of outliers within gathered datapoints. An anomalous datapoint is something that significantly deviates from a normal data range without reason.


What are dynamic thresholds?

Dynamic thresholds are dataranges that show an acceptable changing range of datapoints based on similar historical factors.


What is machine learning?

Machine learning is the use of algorithms that improve automatically through historical analysis and experience.


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