What are AIOps platforms?
Over the last couple of decades, organizations have been adopting digital technologies at lightning speed. By migrating their apps to the cloud and developing new microservices-based applications, they have added layers to their technology stacks that have made life increasingly challenging for IT operations (ITOps). As more digital services are brought online, ITOps has greater difficulty sifting through all the alert noise, detecting incidents, investigating them and responding to them—resulting in outages that are time-consuming and stressful to resolve.
This is where artificial intelligence for IT operations (AIOps) has emerged as the solution. AIOps platforms apply technologies such as artificial intelligence (AI) and machine learning (ML) to ITOps’ datasets in order to clean the event data coming out of observability tools, correlate those events into incidents, enable ITOps to respond quickly, prevent the number of outages and maintain uptime. AIOps solutions require a number of capabilities such as event data aggregation and enrichment, event correlation, root cause analysis, Unified ITOps Analytics, Workflow Automation, and more.
In today’s IT environment, AIOps platforms are the only way organizations can effectively manage alert noise and stop outages before they happen. Below, we dive into the capabilities of AIOps platforms to explain more about how each one works in your IT environment.
The difference between event aggregation and alert correlation
Event aggregation and alert correlation are related but not exactly the same. Event aggregation is the process by which you aggregate multiple relative events generated by your monitoring and observability tools into a single alert. An event can be something simple and harmless such as a user changing their login, or it can be something that signifies a problem within the infrastructure. Once events are normalized, deduplicated, filtered and enriched, event aggregation groups related events into alerts. These alerts are now ready for correlation.
Alert correlation is the process by which you group related alerts into one high-level incident. Using pattern recognition, AIOps dynamically clusters alerts into meaningful incidents and provides patterns. In BigPanda’s AIOps platform, an alert correlation engine clusters or correlates alerts into actionable incidents based on common patterns in topology, time and context.
Collecting data from multiple sources
AIOps platforms pull data from multiple sources, vendors and technology domains to perform event aggregation and alert correlation. Your monitoring and observability tools—including those providing application performance monitoring, network performance monitoring, server monitoring, infrastructure monitoring and others—are the sources of telemetry data.
By bringing data together from all these sources, AIOps platforms facilitate the consolidation and aggregation of this data while maintaining its integrity. Ingesting data from multiple sources allows you to normalize and enrich your data with operational context as soon as it’s collected, so your ITOps teams have rich context at their fingertips throughout the incident management process.
Gathering data from all of your monitoring and observability tools and centralizing it is the first step to breaking down the silos that once existed among the sources—and this is the data aggregation part of AIOps. Gathering and centralizing this data also helps AI/ML models to sift through it and uncover hidden patterns and insights.
In addition to collecting data from monitoring and observability tools, AIOps platforms can also aggregate data from various topological sources such as configuration management databases (CMDBs), app performance monitoring (APM) service maps, cloud orchestration systems, network flow maps and more—in real time.
AIOps platforms can also increasingly aggregate data from different change data sources and tools, including continuous integration and continuous delivery (CI/CD) systems, configuration tools, change management tools and orchestration systems.
Enrichment of the data
Data enrichment is the process of leveraging data hidden in alerts or held in external sources to add contextual information to IT alerts. Your AIOps platform will enrich event and alert data in order to make sense of incoming information. Contextual data helps AIOps platforms correlate related alerts into incidents and discover root causes, and it enables human ITOps workers to evaluate the resulting incidents with actionable context.
Contextual data for the purposes of enrichment is categorized either as operational context data or topological context data. Operational data is relevant to an alert, such as alert priority, alert category or alert owner. The topological context is about the physical and logical relationships between the resource generating the alert and the rest of the infrastructure, such as the associated server, upstream node, datacenter rack, environment or alert cluster.
Enriching raw alerts that AIOps collects—and adding this crucial context from multiple sources—prepares those alerts as inputs for the AI/ML models.
It’s important to note that many solutions advertising themselves as AIOps appear to offer strong enrichment capabilities, but they can fall short of truly doing so because of their limited ability to aggregate data, limited handling of contextual data and inability to scale. True AIOps platforms offer cross-domain enrichment.
BigPanda’s Event Enrichment Engine combines contextual data with normalized alerts to produce context-rich alerts ready for correlation. Once the engine processes and enriches alerts, they have all the data they need to enable BigPanda’s AI/ML engine to correlate and reduce noise and determine probable root cause with a high degree of accuracy.
Event correlation tools
Event correlation tools help ITOps teams detect, investigate and resolve incidents in real time by correlating the enriched data using AI/ML. This is where the noise reduction benefit of AIOps comes into play. By correlating collected alert and topology data into a handful of context-rich incidents, AIOps platforms dramatically reduce noise and enable teams to take action on incidents as they form.
Ultimately, event correlation helps organizations prevent those incidents from escalating into outages. Through helping ITOps more easily identify and resolve incidents, event correlation powers improved availability and infrastructure stability.
Root cause analysis
Root cause analysis quite literally “roots out” the causes of incidents using AI in your AIOps platform. Without root cause analysis, ITOps teams never quite determine why or where an issue occurred, which means they can’t actively prevent it from happening again. This AIOps capability is the key to permanently lowering mean time to resolve (MTTR).
Root cause analysis surfaces the probable root cause of an incident, including potential infrastructure or application changes that led to the incident—enabling ITOps to isolate the issue.
There are several root cause analysis techniques: identifying the common denominator between a group of alerts, identifying the earliest symptoms of a problem, identifying the root cause change that led to the incident and identifying the parent-child relationships between affected nodes or servers and upstream nodes that could have caused the problem. A true root cause analysis solution consists of a range of features and capabilities to help identify changes in your infrastructure.
Automate manual IT tasks
One of the greatest benefits of AIOps is the elimination of time-consuming, manual ITOps work. Organizations’ ITOps, network operations center (NOC), DevOps and site reliability engineering (SRE) teams are constantly bogged down with tasks involved in manual triaging and manual incident response. Manually sharing information through ticketing systems, notifications or other delivery systems slows down incident response workflows, and it can even result in inconsistent syncing of information among tools. AIOps automates all these processes.
By streamlining incident management with automatic triage, ticketing and notifications, your teams no longer spend hours on manual tasks involved in incident lifecycles. AIOps platforms that integrate with your ticketing and/or chat systems make it easy to automate actions when they detect an incident. Automatic triggers for tickets or notifications bring the team together to start working on an incident immediately.
AIOps platforms will also automatically synchronize updates throughout the lifecycle of an incident so every ITOps team member has the same information, context and real-time view to help accelerate resolution. With automatic incident triage, you can calculate and incorporate business context into incidents, which helps to route incidents to the right teams.
Level-0 Automation is a level of automation unique to BigPanda’s AIOps platform that automates manual IT tasks and streamlines manual incident triage—allowing ITOps to efficiently handle high incident volumes. It enables ITOps teams to automate processes including:
- Creating tickets
- Sending relevant notifications
- Setting up war rooms with the right teams
- Ensuring all teams have access to the latest incident information and updates
Level-0 Automation also connects to Runbook Automation tools to run workflows that resolve incidents more quickly and shave critical time off incident response.
Unified Analytics translates ITOps data into insights that prevent outages and offer ways to improve incident management workflows and outcomes over time.
Unified Analytics provides end-to-end visibility into your observability and monitoring tools, ITOps teams, applications, servers, and infrastructure. Unified Analytics helps you gain visibility into key ITOps key performance indicators (KPIs) and trends, and it helps you create your own dashboards or customize existing metrics to simplify KPI visualization and measurement. This kind of self-service approach to analytics and reporting enables organizations to better derive valuable insights from data and drive operational improvements.
Select an AIOps platform with all of these features
The BigPanda AIOps platform has set the standard for what true AIOps tools should offer so organizations can keep digital services running while transforming IT data into incident insights and actions. By ingesting alerts from any monitoring source, BigPanda enriches them with topology data that provides the right contextual information. Event aggregation and alert correlation enables ITOps to identify incidents in real time—before customers notice them. Root cause analysis and automation empower ITOps to prevent incidents from recurring, reduce MTTR and gain time back in their day.
AIOps from BigPanda encapsulates the above capabilities in five methods:
- Ingesting events from multiple sources
- Discovering and assembling a unified topology of IT assets
- Correlating all alerts and events to reduce events by as much as 95%
- Recognizing incidents and patterns in real time
- Automating remediation
We would love to show you exactly how the BigPanda AIOps platform works using a live demo. Reach out to get a customized review of our platform and see if BigPanda is a fit for your organization.