What is AIOps?

AIOPs Explained

AIOps stands for artificial intelligence for IT operations and describes the use of big data, analytics, and machine learning that IT teams can use to predict, quickly respond to, or even prevent network outages.
  • “AIOps” was originally coined by Gartner in 2017 and refers to the way data and information from an application environment are managed.
  • AIOps combines the automation of management tasks and the oversight of network experts, with the expertise of skilled IT pros to improve efficiencies.

How does AIOps work?

AIOps uses telemetry collected from each network and client device to create baselines that automatically help identify issues, determine root causes, and deliver optimization guidance in real-time.

AIOps includes the following:

  • Big data – Structured and unstructured data that is collected in large volumes.
  • Machine learning – Algorithms with the ability to learn about and adapt to changes in the environment. With the ability to then change or create new ones to identify problems earlier and recommend effective solutions.

Why AIOps?

Traditional IT tools lack the intelligence and automation needed to handle the dramatic increase in new services, remote users, IoT devices, cloud technologies, and data.

AIOps provides the following benefits:

  • Enables IT teams to respond to and prevent outages before they happen.
  • Reduces Mean Time To Resolution (MTTR) for improved IT efficiency.
  • Identifies and filters out noise so IT operations doesn’t spend time on low priority issues.
  • Provides optimization tips to improve network, security and application expectations.

How can AIOps benefit you?

The following table describes common network challenges and how AIOps can solve them.

ChallengeHow Traditional Tools FailHow AIOps Solves It

Maintaining network configuration compliance

Static device settings do not keep up with changing business needs.

AIOps continuously monitors network operations and recommends or automatically makes optimization changes.

Addressing changing business needs

Service Level Expectations (SLEs) must be manually configured, which is costly and time-consuming.

Important network thresholds are automatically defined, monitored, and adjusted based on environmental changes.

Resolving network issues quickly

Help desk calls are the primary form of identifying problems, which are expensive and inefficient.

Preemptive insights help identify issues before they impact users or IoT devices for a reduction in help desk calls.

Replicating intermittent issues

Hours or days are spent tracking down intermittent problems because they are difficult to replicate.

Automated, always-on monitoring pinpoints persistent versus obvious problems, with built-in data capture.

Increasing network complexity

Troubleshooting and optimization tasks consume over 50% of ITs time.

Insights include reasons for failures, root cause analysis, and repair recommendations.

Lacking resources and skills

Lack of resources and training are a constant point of contention.

Insights and search features are designed to assist and enhance the team’s knowledge base.

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