For Gartner, AIOps is “the application of machine learning (ML) and data science to IT operations problems. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.”
An AIOps system provides a unified end-to-end view of all business transactions flowing through a system. It collects thousands of configuration and operational data points, then automatically produces a dynamic inventory, topology, and application map of the IT environment.
The functions of an AIOps platform include:
- Ingesting data from multiple sources
- Enabling data analytics with machine learning
- Real-time analysis on data ingestion
- Historical analysis of stored data
- Storing and providing access to the data
- Suggesting prescriptive responses to system issues
- Initiating a plan of action based on prescriptive analysis
Obviously, these are all time-consuming processes. Without automation being a big part of the methodology, AIOps would be overwhelmed by the workload, especially on high-load systems. Thankfully, automation is a big part of the system.
Once data is ingested, it is catalogued, indexed, metadata tagged, graphed, stored, and documented. The system automatically builds a baseline of intelligence that can be utilized and augmented going forward. In effect, an AIOps system builds a blueprint of how an efficient system works effectively and then tries to proactively ensure everything in the IT estate follows this blueprint.
As the data is flowing in, the AIOps system uses ML techniques like pattern matching to detect anomalies. Real-time data correlation helps drive new insights from the raw data sets. Time-series models are also built, and these allow operations to ‘go back in time’ to better understand root causes and patterns leading up to service issues.
Other analytics like predictive and prescriptive analysis, historical data analysis, and causal analysis is also utilized to examine, understand, and improve agent performance. This all happens automatically, and it continuously increases efficiencies. All of this analysis can proactively help the AIOps systems to detect problems before they occur, thereby reducing the mean time to repair. AIOps can trigger automatic system responses that address problems in real-time, oftentimes the users isn’t even aware a problem existed.
AIOps can spot problems, triage them, or prioritize them for IT. AIOps can implement diagnoses to help the system self-heal or provide actions that can be taken by operations to resolve the issues, either directly or based on procedures that have proven effective in the past.
AIOps platforms are able to detect the naturally occurring seasons in data. They can learn when a particular behavior is no longer irregular. In a large-scale deployment, there will always be anomalies, and some will matter much more than others. An AIOps system can be programmed to send out alerts on the deeply problematic one, while either noting some or logging others for remediation but at the next check-up.
Chatbots can be used as virtual support assistants (VSAs) to democratize access to knowledge and automate recurring tasks. Chatbots can even create responses customized to any issue that comes up. A deep description of the problem can be included, with any potential solutions provided as well.
AIOPs can be used to identify the root causes of problems as well as propose the solutions. Using industry-specific models, AIOps can correlate abnormal events with other event data across the IT estate, then drilldown into the cause of an outage or performance problem and suggest remedies.
One of the great ironies of the modern IT estate is that systems have become so complex that they need an additional layer of monitoring tools to keep track of everything going on underneath the hood. This is where AIOps shines. It helps keep track of everything that is up and running and ensures there will be no technical issues with IT functions.
AIOps are proactive, capturing data as it is ingested, which makes it much more useful to the operation. AIOps offers a proven, pragmatic approach to increasing service quality, reducing system downtime, while vastly increasing operational efficiency. In today’s competitive environment, it’s simply not good enough to let system problems become customer-facing problems.
Although an AIOps system provides a unified end-to-end view of all business transactions flowing through a system, AIOps succeed best when united with the right operations culture. A data-driven culture where most decisions are based on data, not experience is imperative. Automation helps but it’s not a be all and end all.