A mashup of artificial intelligence (AI) and machine learning, combined with IT operations, AIOps is a growing tech segment that's helping IT Ops teams streamline how they maintain systems reliability.
Through the use of AI and machine learning, AIOps speeds up the analysis of IT problems and puts incident handling on the fast track to automation.
The development of AIOps comes in answer to the alert fatigue and the struggles to adhere to SLAs. Systems administrators and network operators can be overwhelmed by an extreme flood of inconsistent data from their disparate IT systems.
The sheer volume of IT monitoring data—combined with false positives, false negatives, and countless data types and formats—creates a level of informational noise that makes it nigh impossible to pinpoint the real problems and extract meaningful insights about them.
Additionally, numerous dashboards, ticketing systems, and incident-response tools clog the workflow with manual tasks required to both analyze the data and respond to incidents. All of this slows down mean time to resolution and reduces the number of incidents that IT teams can get to during any given week.
Here's what you need to know about AIOps and how it can make a difference in your organization.
How AIOps works
Similar to how business intelligence professionals lean on analytics and AI to make smarter and faster business decisions, AIOps has IT pros harnessing the power of machine learning and data science to automate analysis of data streaming from IT monitoring tools.
The power of AIOps comes from the combination of data collection, data management, and predictive analytics. This makes it possible to get a singular view of data based on all of the inputs. AIOps' main value proposition is the contextual analysis of how each piece of monitoring data relates to all the others.
This kind of holistic view is difficult to achieve using manual methods. It can make it possible to discover and prioritize anomalies that would have flown under the radar before bigger problems presented themselves, thus diagnosing problems more quickly. Layered on top of this are native capabilities and integration with existing ops tools that make it easier to orchestrate and automate problems after the diagnosis.
Common use cases
Gartner analysts break AIOps down into three major capabilities:
- Observe: Includes the ingestion and correlation of data.
- Engage: Includes task automation, risk analysis, and knowledge management to discover root causes and decide on action.
- Act: Includes scripts and runbooks to actually start to do something about the identified issues.
According to the analysts, few of today's tools can answer fully to all the promises within these three buckets. But as a class, AIOps is evolving to take on a number of the nagging efficiency and strategic problems faced by IT operations teams today.
At the most tactical levels, that includes cutting down on false positives and speeding up the correlation of data, especially as the volume of monitoring information grows with the mushrooming of IoT and edge systems.
Additionally, ops teams are using AIOps to help not only more quickly identify hair-on-fire incidents but also to start to move more into preventative maintenance and root-cause fixes. This will start to pay down technical debt and reduce the volume of the most serious kinds of incidents over time.
Additionally, more advanced organizations are using AIOps for IT service management improvements, including enhancements to inquiry management and self-service capabilities.
How AIOps will change IT
AIOps is still in its formative days, but as momentum builds, experts believe that it will begin to change the face of IT operations roles. Network operations centers (NOC) will need fewer people to manually touch alerts and perform first-response duties.
But the IT team will need more people to curate data, train algorithms, design workflows and runbooks, and intervene in complex root-cause analysis and preventative maintenance and architectural design.
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