What is AIOps?
Artificial Intelligence for IT operations (AIOps) is a new way to address changing user and IoT requirements for today’s modern and complex campus, branch and WFH networks. Visibility and automation provide IT organizations with the insights needed to improve efficiency and enhance user experiences.
AIOPs Explained
- “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.
- The use of AI/ML is used for network insights, endpoint profiling for security purposes and the visibility needed to ensure the proper performance of local and cloud applications.
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.
What are AIOps use cases?
The following table describes common network challenges and how AIOps can solve them.
| Challenge | How Traditional Tools Fail | How 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. |
