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A deep-dive into event correlation

Event Correlation and AIOps

Event correlation is a powerful capability that can help reduce IT noise, detect incidents in real-time, and improve the performance of critical applications and services. Read on for a deep dive into event correlation as we explore everything from its origins to its current state-of-the-art techniques. We’ll also discuss how event correlation fits into the bigger picture of integrated service management.

What is event correlation?

As our world becomes increasingly digitized and interconnected, the need for effective event correlation across all systems grows exponentially. Event correlation is the process of identifying relationships between events, in order to group them together and better understand their causes and effects. It’s a way of making sense of all the “noise” that comes with managing complex digital systems.

There are many different approaches to event correlation, but at its core, the goal is always the same: to help us detect problems in real-time and make better decisions related to uptime in the face of increasing complexity.

In the world of IT, event correlation can be used to detect and diagnose problems before they cause outages or other disruptions. By understanding the relationships between events, we can identify patterns that might indicate an impending issue. This helps us take proactive measures to prevent problems before they occur.

Event correlation is also a valuable capability for understanding the impact of changes to our systems. By tracking the events that occur before and after a change is made, we can better understand the cause-and-effect relationships at play. This information can help us optimize our processes and avoid making changes that break things.

What is event correlation in integrated service management?

Event correlation is a key component of integrated service management (ISM). ISM is a holistic approach to managing the end-to-end delivery of services. It involves bringing together all of the people, processes and technologies involved in delivering a service and coordinating them in a way that maximizes value for the business.

Event correlation is a critical part of ISM because ISM provides visibility into the relationships among events across all the different systems involved in delivering a service. This visibility is essential for making informed decisions about how to optimize service delivery.

There are six key processes involved in integrated service management:

  • Service level management
  • Change management
  • Operations management
  • Incident management
  • Configuration management
  • Quality management

Event correlation is tied to the incident management process, but it is interwoven with all six processes because event correlation provides the contextual information needed to understand the impact of a change, diagnose a problem or track the progress of a service delivery.

Traditional system management tools are designed to manage individual silos of information. They lack the ability to see relationships among events across different systems. This can make it difficult to understand the impact of a change or identify the root cause of a problem.

With the aid of event correlation software powered by artificial intelligence (AI), business and IT professionals can gain the visibility they need to make informed decisions about service delivery. This software is designed to ingest data from all the different systems involved in delivering a service, and then analyze that data to identify connections among events.

What are the steps involved in event correlation?

There are several steps involved in event correlation:

    1. Event Aggregation: The first step is to collect data on all the events that have occurred. This data can come from a variety of sources, including systems, applications, monitoring tools and so on.
    2. Event Filtering: Once the data has been collected, it is then filtered to fine-tune the results. This filtering can be done based on a number of criteria, including time, event type, severity and more.
    3. Event Deduplication: The next step is to remove any duplicate entries from the data set. This helps to ensure that only unique events are being considered.
    4. Event Normalization: The next step is to normalize the data. This ensures that all of the events are described in the same way and is done by mapping the different fields in the data set to a common format.
  • Event Enrichment: Many events lack the context necessary to make them actionable. The enrichment step adds that context, either by extracting or mapping it from existing payload data, or applying it from other data sources (such as a CMDB).
  • Event Grouping: Finally, once events have been through all the preceding steps, they are ready to be grouped based on correlation patterns defined by the IT organization. These correlation patterns group alerts based on whatever criteria the team deems important, such as host or affected application.

Once the event correlation steps are complete, some correlation solutions can provide information on the root cause of the issue. Root cause analysis entails identifying the underlying issues that led to the problem in the first place. This is done by tracing the events back through time to identify which ones precipitated the incident.

Enterprises today are reliant on a complex web of systems and applications to deliver their services. This can make it difficult to understand the impact of a change or identify the root cause of a problem. By leveraging AI-powered event correlation software, enterprises can make sense of all the data generated by their systems and applications in real time, allowing them to proactively identify and resolve issues before they cause disruptions.

Want to see BigPanda event correlation in action? Request a demo here.