Most Enterprise IT organizations use numerous monitoring tools (system, database, application performance, etc.). While each of these is necessary, the cumulative effect is usually an overabundance of incidents and service desk tickets. Meanwhile, many of these same companies also use discovery tools to determine relationships between components. Typically, the fact that disparate technologies are used for monitoring and CMDB mapping makes integration challenging. But there’s a better way to approach ServiceNow event correlation.
Evanios unifies monitoring and event management directly on the ServiceNow platform, making it easy to bridge the gap between the CMDB and monitoring with no coding or complexity.
Our approach to ServiceNow event correlation includes configured logic and machine learning. Evanios normalizes data into a common event format, filters unwanted events close to the source, de-duplicates similar events using flexible criteria, provides robust event correlation capability (based on CMDB topology, time, and event content), and leverages packaged algorithms to prioritize events that are most likely to affect services.
First, Evanios provides complete visibility into the health of IT services by integrating all the data from disparate monitoring tools in ServiceNow, unifying visualizations, and setting the stage for correlation and automation to reduce operational cost and MTTR.
Next, Evanios dramatically reduces noise through de-duplication and correlation algorithms, using machine learning to score every event, so operations teams can identify and prioritize critical issues.
Evanios can correlate events based on a variety of use cases. Logic is flexible, and configurable through a simple form based interface, requiring no coding. Actions can be triggered on incoming events, including consolidation of multiple events into single, more meaningful incidents.
Plus, the same algorithms used for scoring can be applied toward Predictive Analytics, helping you identify significant threats before they become problematic incidents.