The definitive guide to event correlation in AIOps: Processes, tools, examples, and checklist

by BigPanda | Oct 24, 2023

Are you tired of sifting through a sea of IT events and alerts? Or perhaps you’ve found yourself overwhelmed by the volume of data flooding your monitoring systems and challenged to identify the incident root cause. 

There’s a better way to manage the chaos: using AIOps to unite disparate tools, data, and teams for event correlation. This blog post will explore how event correlation can help you sort through information overload, reduce alert fatigue, and boost your organization’s overall operational efficiency. 

In this guide on event correlation in AIOps, we’ll address the following topics: 

• What is event correlation?
• What are the benefits of event correlation?
• Benefits of event correlation tools
• Steps in event correlation
• Event types in event correlation
• Event correlation KPIs and metrics
• What are event correlation use cases by industry?
• Event correlation approaches and techniques
• How to pick the right event correlation tool for your company
• Checklist of key event correlation features

What is event correlation?

Event correlation automates the analysis of monitoring alerts from networks, hardware, and applications to detect incidents and issues, improving system performance and availability. 

Event correlation tools monitor alerts, alarms, and other event signals, detect meaningful patterns amid the deluge of information, and identify incidents and outages. It also speeds up problem resolution, enhancing system stability and uptime. Another primary use is to identify abnormal events that indicate problems. 

AI, including machine learning, enhances event correlation by continuously improving algorithms using data and user input. This is part of how AIOps makes event data analysis and problem detection more efficient.

What are the benefits of event correlation?

Businesses depend on IT systems to serve customers and generate revenue. Some IT issues threaten efficiency, customer service, and profitability. That makes event correlation a critical tool to support performance because the practice increases reliability and decreases problems and outages.

The stakes are high. Enterprise Management Associates’ survey found that the average cost for unplanned outage downtime is $12,900 per minute. 

Beyond service availability, here are the main benefits of effective event correlation:

• Manages complex IT environments: Event correlation consolidates diverse alerts and data sources, providing a unified view for efficient oversight and management of intricate IT setups.
• Improves incident detection and resolution: Event correlation accelerates incident detection and resolution by identifying patterns and root causes through constant alert monitoring, minimizing downtime.
• Reduces alert fatigue: Event correlation filters and prioritizes alerts, reducing the overwhelming volume of notifications that IT teams receive. This gives them more time to focus on critical issues.
• Enhances reliability and uptime: By speeding up problem resolution and proactively identifying issues, event correlation contributes to higher system and service reliability, resulting in improved uptime for businesses and users.

Benefits of event correlation tools

The right event correlation tool improves an organization’s resilience and moves it from a firefighting, purely reactive mindset. Additional downstream benefits include automating key processes, faster resolution, and smarter root-cause analysis.

Best-in-class event correlation tools ingest event data, perform deduplication, identify significant events from noise, analyze root causes, and prioritize IT response based on business objectives. The ability to collect all types of data from all sources helps IT teams breakthrough siloes that limit their ability to see the full picture. So let’s dive into some of the main way event correlation tools are used:

1. Event correlation and observability tools
2. Event correlation in integrated service management
3. Events and system monitoring

1. Event correlation and observability tools

Observability platforms offer anomaly detection. However, this is anomaly detection is different from the event correlation offered by AIOps tools. Anomaly detection examines individual metrics and identifies abnormal states. When anomalies are spotted, the observability tools generate an event to signal the anomaly, which is used as input for event correlation. 

Observability-based event correlation does a wonderful job of reducing alert noise and correlating related events together that originate from its platform and telemetry data. However, observability platforms are not as skilled at ingesting other observability data and correlating seemingly disconnected events together like BigPanda.

BigPanda AIOps surpasses simple observability by employing AI and ML to analyze event data comprehensively. AIOps platforms can draw on data from multiple network resources, including storage devices, servers, user devices, and cloud infrastructure. AIOps use data aggregation to gather data and centralize it – breaking down the silos between your sources and providing system-wide event insights. 

2. Event correlation in integrated service management

ITIL is a framework of best practices for ITSM service management. However, integrated service management offers organizations a streamlined approach to applying ITIL concepts efficiently. Essentially, integrated service management distills the core principles of ITIL, omitting any extra elements for a more focused application.

Within integrated service management, there are six key processes: 

1. Service level management
2. Change management
3. Operations management
4. Incident management
5. Configuration management
6. Quality management

Event correlation falls under incident management but relates to virtually all six processes. Event correlation is crucial for integrated service management because it consolidates and links related incidents, ensuring a cohesive understanding of system health and performance. This holistic approach enables timely responses, minimizes disruptions, and promotes seamless service delivery.

3. Events and system monitoring

System monitoring produces data about events. Making sense of this information stream grows harder as an enterprise’s IT systems become more complex because the volume of event data grows. Challenges come from:

• Changing (the arrangement of nodes, devices, and connections, their relationships to each other, and their interdependencies)
• Combining cloud-based and on-premises software and computing resources
• Practicing decentralization, virtualized computing, and processing of increasing data volumes
• Adding, removing, or updating applications as well as integrating them with legacy systems

IT operations staff and DevOps teams cannot keep up with the volume of alerts and detect incidents and outages in time before they affect revenue-generating applications and services or other critical back-end systems. These factors raise the risk of incidents and outages hurting the company’s business.

Event correlation tools tackle this challenge by collecting monitoring data from across the managed environment and using AI to consolidate those monitoring alerts into clusters related to the same issue. As part of that process, the event correlation platform uses the latest, up-to-date topology data to identify and compare these clusters with system data on changes and network topology. The software uses this information to identify the causes and solutions for issues much faster and more thoroughly than human technicians could.

Steps in event correlation

Now that we understand the different event correlation tool options lets discuss the steps of event correlation. Knowing the steps of event correlation allows us to efficiently process vast event data streams, allowing timely anomaly detection and IT operations response. 

Step 1: Event aggregation

This process gathers monitoring data from different monitoring tools into a single location. Enterprises integrate various sources into the solution so all data is easily accessible as-needed.

Step 2: Event filtering

Many solutions filter the data before processing. This step can be done before event aggregation, but is generally more accepted within the solution post-aggregation.

Step 3: Event deduplication or deduping

This step identifies events that are repeat occurrences of the same issue. Say 1,000 users encountered a particular error message over two hours. This process would generate 1,000 alerts, but there aren’t 1,000 problems. In reality, there were 1,000 instances of one issue. 

Similarly, if a monitoring tool generates an alert about a problem dozens of times, there is just one problem despite the dozens of notifications. Deduping makes this clear. Duplication can occur for many reasons. For example, a disk drive may be full. The monitoring tool checking it may generate hundreds or even thousands of alerts about this issue until the problem is resolved.

Step 4: Event normalization

The normalization step ensures monitoring data collected from many different sources is put into a single consistent format that allows the solution to use AI to correlate this data. For example, one monitoring tool calls something a “host” and another one calls them “servers.” Normalization may use “Affected Device” to refer to the contents of the “host” and “server” fields so the solution interprets it the same way regardless of the monitoring tool sending the data.

Step 5: Root cause analysis

Next, the event correlation tool analyzes the data to determine the underlying cause by looking for relationships and patterns among events. AI-driven machine learning accelerates and automates this analysis. The system compares the event information with log information on IT architecture, configuration, and software changes. This system-wide visibility is critical, given that some experts estimate that changes cause 85% of incidents. The best AIOps platforms offer automated root cause analysis to identify the factors driving IT incidents and suggest actions in real-time to resolve them. 

Event types in event correlation

Businesses correlate different event types based on their IT environments and needs. However, there are several common types, such as events in operating systems, data storage, and web servers.

• System events: These are events that describe unusual states or changes in computing system resources and health, such as high CPU load or disk full.
• Operating system events: These events are generated by operating systems, such as Windows, UNIX, and Linux, as well as embedded operating systems, including Android and iOS. These operating systems are an interface between hardware and application software.
• Application events: These events arise from software applications and include transactions such as e-commerce purchases or entry of visit notes by a healthcare provider. Events may occur in business activity monitoring software, which presents and analyzes real-time data on critical company processes. This data is then fed to the event correlation tool.
• Database events: These are events that occur in the reading, updating, and storing of data in databases.
• Web server events: Events in the hardware and software that deliver content to web pages.
• Network events: Events at a network level involving devices (routers, switches, etc.) such as the health of network ports, switches, or routers — or events generated by network traffic going over or under certain thresholds, as an example.
• Other events: Other types of events include synthetic checks, or probes, that check functionality from the outside in. Also, includes real-user-monitoring and client telemetry that generate specific events as users interact with the service.

Event correlation KPIs and metrics

Event correlation focuses on compressing numerous events into a reduced number of incidents, aiming for clarity on root causes and their symptomatic effects. The primary KPI, compression, ideally approaches 100%, but perfect rates can compromise accuracy—leading to misgrouped events or missed connections. 

In practice, aiming for a balance between accuracy and a high compression rate (typically between 70 to 85%) is advised. Though achieving rates of 85% or even 95% is possible in certain contexts, it’s essential to prioritize business value. Analytics in event correlation software further help assess the effectiveness of event management by examining metrics like raw event volumes, deduplication improvements, and signal-to-noise ratios.

Analytics in event correlation software provide insights into event-driven metrics, enhancing enterprise event management effectiveness. By analyzing raw event volumes, deduplication improvements, enrichment statistics, and signal-to-noise ratios, businesses can proactively address common hardware and software issues.

Sample Hot Spot report

Other metrics can be a byproduct of good event correlation. These metrics are typically found in IT Service Management and are intended to evaluate how automated repairs, service teams, engineers, and DevOps staff handle these incidents. 

Among these is a group of KPIs called MTTx because they all start with MTT, standing for “mean time to.” These include:

• MTTR – Mean Time to Respond, Mean Time to Recovery, Mean Time to Restore, Mean Time to Resolve, Mean Time to Repair: While there are slight differences among these, they all aim to describe how long an outage lasts and how long the problem takes to fix.
• MTTA – Mean Time to Acknowledge: The average amount of time between an alert and an operator acknowledging it (before starting work to address it.)
• MTTF – Mean Time to Failure: The duration between non-repairable failures of a product.
• MTTD – Mean Time to Detect: How much time it takes to discover an incident.
• MTBF – Mean Time Between Failures: How much time between failures; A higher number indicates greater reliability.
• MTTK – Mean Time to Know: The average time to discover an important issue.

For event management metrics, look at raw event volume, then note the decreases in event volume through deduplication and filtering. For event enrichment statistics, use the percentage of alerts enriched and degree of enrichment, signal-to-noise ratio, or false-positive percentage. 

Specific event frequency is useful for identifying noise and improving actionability. Overall monitoring coverage, in terms of the percentage of incidents initiated by monitoring, is also valuable.

Sample MTBF report

Operationally, these numbers are meaningful. You can gauge how well your operations staff performs by monitoring and tracking these KPIs. However, each organization’s specific levels and changes from using event correlation software are unique. Event correlation software vendors, at best, can forecast a range of potential percentage improvements in MTTx metrics for a customer.

What are event correlation use cases by industry?

Event correlation platforms help enterprises’ operations teams achieve greater IT application and service reliability, a better experience for internal and external customers, and stronger business outcomes such as increased revenues. 

Wondering how event correlation can work for your vertical? Here are some examples of how event correlation works across various industries:

• Major airline: A prominent U.S. airline facing costly downtime streamlined its monitoring tools and implemented AI-driven event correlation. This led to centralized monitoring, reduced escalations, and a 40% drop in MTTR.
• Athletic shoe manufacturer: An athletic apparel giant improved its incident identification and correlation by adopting machine learning-based solutions. Within 30 days, MTTA decreased from 30 minutes to one minute.
• Enterprise financial SaaS provider: Struggling to resolve incidents, this SaaS provider saw a 400% increase in resolution rate, a 95% reduction in MTTA, and a 58% decrease in MTTR within the first 30 days of implementing AI-based event correlation.
• Retail chain: A nationwide home improvement retailer reduced outage duration by 65% and average outage duration by 46% through event correlation. Major incidents decreased by 27%, root cause identification increased by 226%, and MTTR improved by 75%.

Global communications infrastructure platform Zayo faced challenges with their event management and chasing false positives, duplicates, and benign alarms across various tools and terminals. Zayo unified their technology stack with BigPanda to filter out 99.9% of events. Zayo now has a clean and manageable data flow that equips them to scale their IT and grow their business like never before. 

If you’re building a business case for investing in event correlation and want case studies relevant to your industry, we encourage you to schedule a personalized demo to experience firsthand how AIOps can support your industry.

Event correlation approaches and techniques

Event correlation techniques focus on finding relationships in event data and identifying causation by looking at characteristics of events, such as when they occurred, where they occurred, the processes involved, and the data type. AI-enhanced algorithms play a large role today in spotting those patterns and relationships and pinpointing the source of problems. 

Here’s an overview:

• Time-based event correlation: This technique looks for relationships in the timing and sequence of events by examining what happened right before or at the same time as an event. You can set a time range or latency condition for correlation.
• Rule-based event correlation: This approach compares events to a rule with specific values for variables such as transaction type or customer city. Because of the need to write a new rule for each variable (New York, Boston, Atlanta, etc.), this can be a time-consuming approach and unsustainable over the long term.
• Pattern-based event correlation: This combines time-based and rule-based techniques by looking for events that match a defined pattern without needing to specify values for each variable. Pattern-based is much less cumbersome than the rule-based technique. But it requires machine-learning enhancement of the event correlation tool to continuously expand its knowledge of new patterns.
• Topology-based event correlation: This approach is based on network topology, or the physical and logical arrangement of hardware, such as servers and hubs or nodes on a network, and an understanding of how they’re connected to each other. By mapping events to the topology of affected nodes or applications, it is easier to visualize incidents in the context of their topology.
• Domain-based event correlation: This technique takes event data from monitoring systems that focus on an aspect of IT operations (network performance, application performance, and computing infrastructure) and correlates the events. Some event correlation tools ingest data from all monitoring tools and conduct cross-domain or domain-agnostic event correlations.
• History-based event correlation: This method compares new events to historical events to see if they match. In this way, a history-based correlation is similar to a pattern-based one. However, history-based correlation is “dumb” in that it can only connect events by comparing them to identical events in the past. Pattern-based is flexible and evolving.
• Codebook event correlation: This technique codes events and alarms into a matrix and maps events to alarms. A unique code based on this mapping represents issues. Events then can be correlated by seeing if they match the code.

How to pick the right event correlation tool for your company

The right event correlation solution enables your ITOps to deliver better business value. However, competing claims and opaque technology can make it hard to know which tool best matches your needs.

Tool integration setup should be quick and easy with the right platform. A small internal or vendor team should be able to establish integrations in days rather than weeks or months, without support from experts.  

Checklist of key event correlation features

Here’s an overview of the main event correlation features. Our free downloadable evaluation scorecard compares vendors on different dimensions, weighted by importance to your company.

• User experience: Assess security, ease of access, intuitive navigation, modern user interface, unified console, native and third-party analytics, and user-friendliness.
• Functionality: Evaluate data sources, event types, observability, monitoring, changes, topology tools, data ingestion, interpretation, normalization, suppression, enrichment, deduplication, root cause detection, correlation methods, and incident visualization.
• Machine learning/AI: Consider level-0 automation, scalability, agility, performance, integration capabilities, extensibility, and security.
• Strategic factors: Examine alignment with vision, roadmap, business model, company culture, industry strength, financial stability, and customer satisfaction.
• Partners: Evaluate integration with observability, topology, and collaboration tools, systems integrators, resellers, and cloud providers.
• Service: Review proof of value, implementation timeline, education/training, advisory services, customer success programs, and customer support SLAs.

Harness the power of BigPanda AIOps for event correlation

The BigPanda AIOps offers best-in-class event correlation capabilities to help modern enterprises reduce IT noise by 95%+, enables you to detect incidents in real-time as they form and before they escalate into outages, and empowers your ITOps team to focus on high-value work. 

Unlock the power of AIOps for your event correlation to connect related incidents, simplifying the complex array of alerts. With AI/ML you can automate your root cause analysis and improve your IT operations. As you aim to enhance your operational responses, selecting an appropriate event correlation tool is essential to revolutionize your IT management experience. See BigPanda event correlation in action by taking a self-guided tour or doing a personalized demo.