In 2016, Gartner coined the term Artificial Intelligence for IT Operations (AIOps): the practice of applying analytics and machine learning to big data to automate and improve IT operations. Since then the category has exploded. But there’s much more to AIOps than shoehorning new AI into an old solution. In the just-released 2018 Market Guide for AIOps Platforms, Gartner adds an important distinction between vendors who offer AIOps features and the ones who offer an AIOps platform.
The Difference Between AIOps Features and AIOps Platforms
In the guide, Gartner notes that, “AIOps platforms add important capabilities beyond what a monitoring tool with embedded AIOps can provide.” A true AIOps platform is “able to combine big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT.” An AIOps platform “enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies.”
In other words, AIOps is much more than just AI or machine learning features. Legacy monitoring tools and solutions can add new features and call themselves an AIOps provider, but they can’t deliver the benefits of a true AIOps platform (like Splunk).
For IT Operations, Data is Still the Differentiator
In the guide, Gartner defines the Four Phases of IT Operations: descriptive IT, anomaly detection and diagnostics, proactive operations, and avoiding high-severity outages entirely. They stress the importance of a platform that can handle large amounts of data as well as data in many different categories and classifications.
An AIOps platform needs to be able to both analyze stored data and provide real-time analytics at the point of ingestion. (At Splunk, we describe this as the shift from reactive to predictive IT, and it’s how we’ve helped customers like TransUnion predict outages as much as 45 minutes in advance.)
The Most Important Data Categories for AIOps
According to Gartner, the data categories that matter most are: logs, text, wire, metrics, API, and social-media-derived user sentiment. Legacy monitoring tools and solutions that add on a few AIOps features can’t handle the volume, variety and velocity of complex structured and unstructured data. Plus, add-ons and afterthoughts don’t scale as data demands grow. Splunk’s AIOps platform can handle these categories and more, for historical and real-time data, and is designed to scale.
How to Get Started in AIOps, According to Gartner
In their 2018 Market Guide for AIOps Platforms, Gartner recommends adopting an incremental approach. Start small, by reorganizing your IT domains by data source. Learn how to work with large persistent datasets from a variety of sources. Let your IT Operations team “become fluent” with the big data aspects of AIOps.
But ultimately the platform you choose will determine your success. Gartner recommends that enterprises “prioritize those vendors that allow for the deployment of data ingestion, storage, and access, independent from the remaining AIOps components.” An AIOps platform like Splunk gives you everything you need to master your data, expand throughout Infrastructure and Operations and join other Splunk customers seeing AIOps success.
Do you want to understand how our AIOps platform can help solve your operation management use cases faster? Join our "5 Steps to a Predictive IT Strategy" webinar with Vodafone and Accenture on February 21st to understand the 5 critical components of an AI strategy.
Disclaimer: Gartner "Market Guide for AIOps Platforms" by Pankaj Prasad and Charley Rich, Nov 12, 2018
This guide was published by Gartner, Inc. as part of a larger research note and should be evaluated in the context of the entire report. The Gartner report is available upon request from Splunk.
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