Early Problem Detection & Causal Analysis

Today's advanced application and service environments are too large and complex for yesterday's rule based management approaches. As a result operations teams are either deluged with false alerts or glued to dashboards. They learn about too many problems from end-users and spend too much time troubleshooting instead of improving service levels.

But Anomaly Detective customers are changing that equation. Instead of wasting time writing 'known bad' behavior rules that generate useless alerts, they let machine learning algorithms automatically baseline normal behaviors and identify real problems in real-time. Instead of trial and error hunting through gigabytes of forensic data, they let advanced analytics cross-correlate causal data and slash troubleshooting times and team sizes up to 90%.

With all the free time they gain, Prelert customers use Anomaly Detective to focus their efforts on continual improvement processes that reduce incident rates and boost service levels.



 

 


 
Find Problems Before End Users Do
 

ISP_Thumb
  • Advanced behavioral analyses identify abnormal developments long before threshold and rule based approaches can
  • Troubleshooting teams get the data they need to resolve issues before large numbers of users are impacted

 

 


 
Slash Troubleshooting Time & Team Sizes
 

Credit_Services_Thumb
  • Powerful analytics cross-correlate data sources to provide alerts with the detail needed to establish cause
  • Triage teams involve fewer SMEs and resolve issues faster when they know exactly where to start to resolve an issue


 


 
Turn Thousands of False Alerts into an Accurate, Actionable Handful
 

ISP_Thumb
  • Anomalies are scored by severity and impact reducing thousands of largely false alerts per day to an accurate, actionable few a week
  • Cross-correlation provides detailed causal data so operations teams spend less time fixing problems and more time improving service levels