What is AIOps?

What does AIOps mean? AIOps is short for Artificial Intelligence for IT Operations. Other names you might recognize include Cognitive Operations, Algorithmic IT Operations and IT Operations Analytics (ITOA).

AIOps is the multi-layered application of big data analytics and machine learning to IT operations data. The goal is to automate IT operations, intelligently identify patterns, augment common processes and tasks and resolve IT issues. AIOps brings together service management, performance management and automation to realize continuous insights and improvement.

Industry analysts have defined a set of capabilities that an AIOps platform should provide. These include:

• Collecting and aggregating data from many sources such as: networks, applications, databases, tools and cloud as well as in a variety of forms including metrics, events, incidents, changes, topology, log files, configuration data, KPIs, streaming and unstructured data like social media post and documents (natural language processing).
• Managing the data, storing the data in a single place accessible for analysis and reporting, also including functions like indexing and expiration.
• Analyzing the data through machine learning including pattern detection, anomaly detection and predictive analytics. Separate significant alerts from ‘noise.’
• Conducting root cause analysis (RCA) which involves reducing the volumes of data to the few (or one) most likely causes. Correlate and contextualize data together with real-time processing for problem identification.
• Acting as a strategic overlay that aggregates multiple monitoring tools and other investments. Codify knowledge into automation and orchestration of response and remediation.
• Continuous learning to improve handling and resolution of problems in the future.

Why is AIOps needed?

Many organizations have transitioned from the static, disparate on-site systems to a more dynamic mix of on-premises, public cloud, private cloud and managed cloud environments where resources are scaled and reconfigured constantly.

More devices (most notably Internet of Things, or IoT), systems and applications are providing a tsunami of data that IT needs to monitor. For example, a locomotive can produce terabytes of data during a trip. In IT terms this explosion is called Big Data.

No human can process the explosion of data IT Operations is expected to handle. IT teams cannot prioritize different issues for resolution in a timely fashion. They are inundated with a large volume of alerts many of which are redundant. This negatively impacts user and customer experience.

Traditional IT management solutions cannot keep up with this volume. They cannot intelligently sift through events from the sea of information. They cannot correlate data across interdependent but separate environments. They cannot deliver the predictive analysis and real-time insight IT operations needs to respond to issues quickly enough.

To identify, resolve and prevent high-impact outages and other IT operations problems faster, organizations are turning to AIOps. AIOps enables IT operations teams to respond quickly and proactively to outages and slowdowns while expending much less effort. It bridges the gap between a dynamic, diverse and difficult IT landscape on the one hand and user expectations for minimal or no interruption in system availability and performance.

Benefits of AIOps

The benefits users have found using AIOps include:

• Improved employee and customer experience
• More efficient use of infrastructure and capacity
• Better alignment with IT services and business service outcomes
• Faster time to deliver new IT services
• Reduced firefighting and avoid costly disruptions
• Better correlation between change and performance
• Improved efficiencies in managing change
• Reduced workload on IT Operations staff because AI is helping with the analysis
• Reduction in false alarms. Faster root cause analysis (RCA) because AI pinpoints the problem or reduces the number of items operators must look at to a small set
• Prevent problems before customers are impacted via anomaly detection
• Achieving faster Mean Time to Resolve (MTTR)
• Reducing the skills gap
• Reduction of human error
• Unified view of the IT environment
• Insights into what workloads drive costs
• Support for traditional infrastructure, public cloud, private cloud and hybrid cloud
• Moving from reactive to proactive to predictive problem management
• Modernizing IT operations and the IT operations team
• Higher levels of security-to-operations collaboration