AIOps
What is AIOps?
Artificial intelligence for IT operations, or “AIOps” is a term created by Gartner that describes how big data and machine learning technology can advance IT operations through insights for continuous improvement. AIOps was previously known as “Algorithmic IT Operations” because AI and ML algorithms were used to automate repetitive tasks.
A webpage from Gartner’s archives provides the full definition of AIOps as it’s known today:
“AIOps platforms utilize big data, modern machine learning[,] and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies...”
The image below is a recreation of Figure 1 from Gartner. The graphic visually represents how AIOps provides continuous insights for IT Operations Management (ITOM) functions.
ITOM functions in artificial intelligence for IT operations consist of three areas: monitoring, service desk, and automation. All three functions contribute to increasing overall business value.
Monitoring (to observe)
This function serves to gather and correlate data. Use cases for IT teams include automated behavior prediction and causal analysis. See detailed use cases for monitoring from Gartner.
Service desk (to engage)
Discovery of root causes and risk analysis are part of the service desk function, which includes real-world applications such as intelligent notifications and intelligent collaboration. See detailed use cases for service desk from Gartner.
Automation (to act)
Automation uses scripts and workflows to take action on any issues that were previously detected. Examples of automations include intelligently adaptive automation, along with machine-generated and managed automations. See detailed use cases for automation from Gartner.
Business value
Using both IT operational and business data, business opportunity discovery and dynamic decision support capabilities can provide new insights to IT teams. Read more on use cases for AIOps in business intelligence from Gartner.
Here are some other quick facts about how AIOps can benefit IT teams:
- AIOps can assist in executing tasks that require human intervention (such as analyzing data quickly) and augment human capabilities (such as uncovering new insights).
- AIOps platforms are able to collect data from a variety of domains and sources, which can include events, customer data, application logs, and more.
- AIOps platforms are able to ingest historical and real-time data while analyzing the data at the point of ingestion (rather than storing it in a database for analysis at a later time).
Is AIOps overhyped?
The Pulse community engaged in a few different conversations around whether AIOps is overhyped. The general consensus from our community across the various discussion boards is that AIOps is great, but it has to be understood well, implemented properly, and have overall clean data to use it to its full potential.
If you’d like to add your input about AIOps, join the conversation on the Pulse community, and participate in AIOps-related polls and discussions, such as those listed below.
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