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What is AIOps: Prevent and resolve IT Outages

What is AIOps: Prevent and resolve IT Outages

The definition of AIOps continues to evolve, but understanding the fundamentals of how it works can help you keep up and invest in the right AIOps platform, tools, and features.

According to Gartner, AIOps “combines big data and machine learning to automate IT operations processes”. Specifically, Gartner explains that “AIOps platforms analyze telemetry and events, and identify meaningful patterns that provide insights to support proactive responses”. Enterprises investing in AIOps platforms must look for six critical AIOps characteristics:

  1. Ingests data from any and all sources of observability, monitoring, and change data.
  2. Topology that captures dependency mapping information from CMBDs and other sources.
  3. Correlates related events associated with an incident.
  4. Detects incidents in real time and surfaces their probable root cause.
  5. Helps with remediation.
  6. Provides analytics and reports.

AIOps platforms with these characteristics can help ITOps, NOC, and SRE teams detect, investigate, and fix incidents quickly, before incidents escalate into outages that impact their end-users and customers.

In simple terms, AIOps is artificial intelligence for IT operations. Data volume and service complexity are only increasing as organizations transform to take advantage of cloud scalability and cost, refactor to use microservices architectures, embrace CI/CD change velocities, or simply extend and expand services into new regions with new features. These trends have made it impossible for humans to keep up at the pace and scale necessary, causing incidents to go unnoticed in the unmanageable volumes of data.

This has led many organizations to embrace AIOps so their IT teams can stop spending their time firefighting and focus instead on initiatives that drive innovation for the business.

How Can AIOps Help?

Data volume and service complexity are only increasing as organizations transform to take advantage of cloud scalability and cost, refactor to use microservices architectures, embrace CI/CD change velocities, or simply extend and expand services into new regions with new features. These trends have made it impossible for humans to keep up at the pace and scale necessary, causing incidents to go unnoticed in the unmanageable volumes of data..

This has led many organizations to embrace AIOps so their IT teams can stop spending their time firefighting and focus instead on initiatives that drive innovation for the business.

How does AIOps work?

AIOps works by ingesting data from multiple sources while simultaneously triaging and analyzing it using advanced machine learning algorithms. This helps it detect anomalies in data streams, allowing IT teams to identify potential problems before they become critical issues. It then automatically escalates alerts and provides detailed insight into how they can be addressed quickly, reducing downtime significantly. AIOps also provides Incident Intelligence, which correlates observability, topology, and change data to catch problems in real time, helping teams proactively prevent and resolve outages. AIOps allows organizations to streamline processes and maximize efficiency for tasks.

Data Aggregation

The first step to delivering value to ITOps, NOC, and SRE teams—and preventing and resolving outages—is to aggregate different ITOps datasets such as observability and monitoring data and change events and change data. This includes aggregating data from a wide variety of observability and monitoring tools, including commercial, homegrown, legacy, and custom observability and monitoring tools. But because these tools were designed to generate a very large volume of events and alerts at different levels of granularity, this data can often be noisy and non-actionable.

Once this data is ingested, AIOps platforms must therefore filter, deduplicate, and normalize this data to reduce the noise. Then, because observability and monitoring data often lacks useful operational and business context, AIOps platforms must enrich this data with operational context that is often buried inside these very alerts. Finally, AIOps platforms must aggregate this data into a single alert. This entire process turns noisy, low-quality observability and monitoring data into actionable alerts. This also prevents ITOps, NOC, and SRE teams from having to rely on a dozen or more different observability and monitoring tool consoles, and it helps them from wasting time on duplicate, redundant, or merely informational events and alerts.

Because incidents and outages in modern IT environments are often caused by planned and unplanned changes, AIOps platforms must also be able to ingest change events and change data from a variety of change tools and sources, including, but not limited to, CI/CD, change management, orchestration, change logging, and change audit tools. By accumulating a comprehensive dataset of change data, AIOps platforms can later use it to identify the likely change that caused an incident or outage.


The dependencies between various nodes, servers, network devices, applications, and other IT devices means that ITOps, NOC, and SRE teams struggle to determine how several different events and alerts are potentially related to each other, and they struggle to separate symptoms events from root cause events.

It is critical for AIOps platforms to therefore ingest topology data from different sources such as CMDBs, APM flow and service maps, virtualization management, service discovery, EMS, NMS, and asset management systems. Because CMDBs in modern IT stacks are often incorrect or out-of-date, it is especially important for AIOps platforms to have the ability to tap into all the other sources of topology data listed above.

Once this data is ingested, AIOps platforms must use this data to create a full-stack topology model and then keep this model up-to-date by frequently syncing with the sources of this data. This helps ensure that an out of data topology model does not result in weak or ineffective correlation downstream. Such a scenario could make all the difference between a P3 incident being identified correctly and in time, before it escalates into a P0 outage, versus a P0 outage that the AIOps could not identify in time because the VM topology changed, resulting in a reservation system that could not process customer orders for minutes or even hours during one of the busiest shopping seasons of the year.

Event Correlation

Once different types of ITOps data such as observability and monitoring data, change data, and topology data have been ingested, cleaned, normalized, enriched, and aggregated, it’s time for event correlation to kick in. Event correlation is at the heart of most AIOps platforms and uses artificial intelligence (AI) and machine learning (ML) to correlate the different types of ITOps data together.

Event correlation uses topology and contextual data to understand how different alerts are related to each other. To use a simple example, if a dozen alerts all belong to a certain VM cluster and occurred within a specific time window, event correlation groups these alerts together into a single incident and uses priority signal data from the individual alerts to determine the incident’s priority.

Advanced AIOps platforms can further enrich the incident post-event correlation to add business logic and context. This means that if there are three incidents that are all deemed to be major incidents based on the underlying alerts, by looking up business logic and context in external sources, the AIOps platform is able to tag one of the incidents to be business critical because it affects the largest number of customers, a business-critical payment processing service, etc.

Real-time detection, triage, and probable root cause

Event correlation implies three critical stages of the incident lifecycle: detection, triage, and investigation.

ITOps, NOC, and SRE teams that struggle to detect incidents in real time and often learn about a problem when a customer or user opens a ticket or calls the problem into the NOC can greatly benefit from event correlation inside AIOps platforms. By correlating several different, related alerts into a single incident in real time, these teams immediately know that there is a problem, and they can focus on it instead of wasting time on symptom alerts. Real-time incident detection also prevents problems from escalating into outages that impact users and customers.

Next, by adding valuable business context and other business logic to the incidents resulting from event correlation, ITOps, NOC, and SRE teams are able to rapidly triage an incident and take action on it. Based on the operational context that was added to the underlying alerts, or based on the business context that was added to the incident, these teams can either resolve the incident right away, they can tag it for additional investigation, or they can route it to the right domain experts, L3 teams, or other subject matter experts. This means that AIOps platforms with event correlation and incident enrichment capabilities can accelerate incident triage instead of wasting precious seconds or minutes deciding what to do with an incident that was detected and surfaced to them.

Teams with the probable root cause of the incident. Historically, the root cause of incidents used to be infrastructure-related issues such as a hardware failure, but today, root causes are most often planned and unplanned changes. That means that AIOps platforms must be able to identify infrastructure-related root causes, and they must be able to identify change-related root causes—aka root cause changes. This is the reason why AIOps platforms must be able to ingest topology from different sources (to identify infrastructure-related root causes) and change data from different sources (to identify change-based root causes). Armed with this information, ITOps, NOC, and SRE teams can investigate and resolve a significant majority (as many as 94%) of incidents themselves, without indiscriminately escalating them to expert times and interrupting other projects.

Remediation and resolution

In modern IT environments, a subset of incidents can be safely auto-remediated. If ITOps, NOC, and SRE teams are not able to auto-remediate these incidents, it means that either they—or other experts—must manually remediate those incidents over and over again, wasting critical time instead of focusing on other incidents that require their attention. That’s why AIOps platforms must be able to integrate with a wide variety of open source, commercial, or homegrown runbook and auto-remediation tools. This is also why it’s important for AIOps platforms to clean and enrich noisy observability data with valuable context. The resulting high-value payload data make it easier for organizations to route their incidents to the right tools quickly to accelerate remediation and resolution.

Where and when ITOps, NOC, and SRE teams are not able to resolve an incident themselves or auto-remediate it, they must be able to route the incident to a collaboration tool such as an ITSM/ticketing tool, a notification tool, or a chat system. AIOps platforms must therefore also be able to easily integrate with a wide variety of ITSM/ticketing tools, notification tools, and chat/messaging systems. This makes it easy for organizations to mobilize the right experts when needed, kick off advanced workflows in those systems, and ultimately accelerate remediation and resolution.

Analytics and reports

At different stages of the incident lifecycle, ITOps, NOC, and SRE managers and leaders need to understand the quality of their observability and monitoring data and the tools generating that data, the productivity of their teams, and the efficiency of their incident management workflows.

That’s why AIOps platforms must provide interactive ITOps dashboards, reports, metrics, and KPIs. In addition to providing a robust set of these dashboards, reports, and KPIs out of the box, AIOps platforms must also provide the ability to customize all of it for different business units, application or service owners, geographies, etc.

A secondary benefit of AIOps platforms that provide easy-to-consume analytics and dashboards is that it helps ITOps, NOC, and SRE managers communicate the value created by their teams to other critical stakeholders and creates unparalleled transparency across the organization.

What are the benefits and capabilities of AIOps?

For enterprises to succeed with AIOps, their AIOps platforms must deliver the following set of capabilities.

Integrates with existing tools

Every enterprise uses and depends on several different tools that span observability and monitoring, change, topology, collaboration and remediation. In almost all cases, these tools reflect years of investment, development and customization. Often these tools are deeply embedded into critical ITOps workflows and processes. Your chosen AIOps platform must not require a long and painful long rip-and-replace project. Instead, it must integrate with all of your existing tools. Ideally, it must also provide APIs to future-proof your future tool choices.

Data preparation and cleansing

“Garbage in, garbage out” is a well-known maxim in IT, and it applies to IT Operations as well. Force-feeding IT alerts to your AIOps platforms’ artificial intelligence and machine learning algorithms without adequate normalization, enrichment and tagging – aka data preparation and cleansing – results in low-quality results at best. At worst, it can result in AIOps failure, if your teams aren’t presented with any actionable insights. That’s why your AIOps platform must deliver built-in normalization, enrichment and tagging that can work at scale and be able to process millions of IT alerts every day.

Match changes to incidents (identify root cause changes)

As applications modernize and enterprises migrate to the cloud, developers are able to continually enhance their apps and services, and release new features and enhancements, creating thousands of daily changes, any one of which can cause an incident or outage. A key requirement for any AIOps platform is the ability to ingest change data and rapidly correlate it with alerts from observability tools for greater context about an incident.

Explainable AI

Many enterprise AI-powered systems obscure the “why” behind the artificial intelligence and machine learning decisions. This is a recipe for reduced trust and adoption, and can severely limit the value enterprises gain from their AIOps investments. Your AIOps platform’s machine learning-generated logic must be expressed in clear, easy-to-understand language; your teams must be able to edit it and incorporate their institutional knowledge, and they must be able to preview the effects of any changes they make. Finally, your ITOps teams must be able to manage this without relying on, or requiring, expensive and scarce data scientists or machine learning engineers.

Reporting Capabilities

AIOps platforms are the hub of IT operations data. In addition to data collected from observability/monitoring, change and topology tools, AIOps platforms capture data related to each stage of the incident management pipeline (e.g. enrichment and correlation rates, or root cause change matching rates), incident outcomes, team performance and efficiencies, operational workload. Your AIOps platform must be able to contextualize, extract and present this data natively or easily send its contextualized data to your preferred BI platform of choice. That’s the only way you can measure, track and improve your ITOps KPIs and metrics.

Democratized AIOps

Today, some enterprises have large, centralized ITOps and NOC teams, whereas others have dozens or even hundreds of distributed DevOps and SRE teams. Enterprises also different in their level of ITOps maturity, with some enterprises having “grown up” in the cloud, while others are mid-way or even just getting started, with their modernization initiatives. Finally there are several different – and equally important – stakeholders that can benefit from AIOps, from NOC Managers and L1 users to VPs of ITOps to service owners to the heads of business units and CIOs. Your AIOps platform must be able to present data in easy-to-understand dashboards and reports that everyone can use to make decisions and take action.

How to get started with AIOps

Adopting AIOps can seem like a daunting task, especially because of the excessive hype and confusion around AIOps. However, it doesn’t have to be daunting if you separate AIOps reality from the hype and make sure that you pick an AIOps platform with the right characteristics. Here are some recommendations for getting started on your AIOps journey:

Are you ready for AIOps?

Look for vendors that offer a pragmatic take on AIOps instead of using a word salad to describe what they do. Also, be wary of AIOps vendors that promise either AI magic or claim to do all-things-ITOps. It is exceedingly hard to be good at observability and monitoring, and event correlation, and auto-remediation, and ticketing, and notifications, and … Several organizations around the world, including some of the largest and most complex enterprises, have successfully adopted AIOps. Talking with your peers in other organizations that have successfully adopted AIOps and meaningfully reduced MTTx can be a good starting point.

What’s the best time to start?

Often organizations prevent themselves from benefiting from AIOps because they want to fix their observability and monitoring tools first, or rationalize their 23 different tools into a single suite (a project that can last years, if not more, and deliver wholly unsatisfactory results at the end of it). As Sanjay Chandra from Lucid Monitors shared at Gartner’s IOCS conference in December 2022, “don’t wait.” AIOps platforms can help you analyze the quality of your observability and monitoring tools and give you a data-driven basis for retiring redundant tools or identify gaps in your monitoring fabric. That’s on top of the other capabilities discussed above.

BigPanda’s Event Correlation and Automation platform, powered by AIOps

Built from the ground-up for large scale and complex IT environments, BigPanda’s Event Correlation and Automation platform, powered by AIOps, helps organizations prevent and resolve IT outages.

Designed to go live and into production in just 10-12 weeks, BigPanda’s AIOps platform delivers three key capabilities: Event Correlation, Root Cause Analysis and Level-0 Automation.

Event Correlation uses explainable AI to correlate disparate streams of observability, monitoring, change and topology data into context-rich incidents in real time.

Root Cause Analysis uses explainable AI to surface probable root cause and root cause changes in real time, inside today’s complex and dynamic IT environments.

Level-0 Automation eliminates repetitive manual incident response tasks to accelerate incident response, remediation and resolution.

The result is a solution that processes diverse datasets, supports multiple use-cases and connects diverse teams like ITOps, NOC, DevOps and SREs – all while enabling cost reduction, increased performance and availability, and accelerated digital transformation.

Learn More About BigPanda’s AIOps