AIOps (Artificial Intelligence Operations) - Definition & Overview

AIOps represents the cutting edge of innovation in IT operations technology, with the term having been coined by Gartner in a 2017 report. According to that report, enterprise organizations are undergoing an unprecedented period of digital transformation that is characterized by the widespread implementation of new technologies, migration of on-premise infrastructure to the cloud and rapid innovation.

Digital transformation has yielded many positive benefits for enterprise organizations, including reduced service delivery costs, reduced costs associated with accessing and scaling IT infrastructure and increased availability of data storage and computing power on an on-demand basis. At the same time, the large scale expansion of web-based services in a hybrid cloud environment has created significant challenges for the IT operators and analysts charged with maintaining the security and operational efficiency of IT systems.

AIOps is the newest type of software tool meant to address the needs of IT operations teams in today's cloud-based enterprise IT environment. AIOps standards for "artificial intelligence for IT operations", and it essentially refers to the use of artificial intelligence, machine learning, and pattern recognition to perform and automate tasks that would normally be executed by IT operations.

Gartner predicts that large enterprise use of AIOps and digital experience monitoring tools will increase from 5% in 2018 to 30% in 2023, making this one of the fastest-growing market areas for digital transformation. AIOps software tools vary significantly, but they may follow the same basic workflows and possess the same core features. Successful AIOps software implementations can help enterprise IT organizations increase their oversight of hybrid cloud environments, detect and respond to network security events more quickly and save time by automating routine tasks and processes.

AIOps software platforms use cutting-edge computing technologies like machine learning and advanced analytics to support IT operations in three areas: monitoring, automation, and service desk.

AIOps software helps facilitate IT infrastructure monitoring by collecting and aggregating data from the network. Data sources include event log files from servers, applications, and other network endpoints. Capturing data from multiple sources that were previously silo-ed and integrating them into a single database makes it easier for machine learning algorithms to assess network characteristics and performance in real-time.

Response automation is one of the most value-driving features of AIOps software tools. AIOps software can be configured to track specific KPIs for a given server or application. IT operators may conduct performance tests to establish a baseline for each metric or KPI and define acceptable thresholds for the ones they intend to prioritize. When a KPI breach is detected, AIOps software can perform an automated root cause analysis to automatically determine why a problem occurred and implement a solution if one is available.

Incident management is a core function of the service desk in any IT organization. AIOps software tools effectively support the incident management process by automating responses to routine alerts, which significantly reduces the amount of time that IT operators spend doing mundane, low-value tasks. AIOps tools can also feed data directly into the incident management and problem management processes, acting as valuable sources of data and analysis that drive business and IT improvements.

AIOps is best described as a set of technologies that make up a platform, rather than a single application. Today's available AIOps platforms certainly differ in their feature offerings, but the commonality they all share is that they use artificial intelligence to support the responsibilities and activities of an IT operations team. The basic components and features of an AIOps software tool can be summarized as follows:

• Data Aggregation - One of the core capabilities of AIOps software is that it aggregates data from a variety of sources within the cloud infrastructure, including events logs, job data, tickets and more. The removal of data silos makes it easier to maintain oversight of IT infrastructure and correlate events that happen on the network to determine their root cause.
• Real-Time Processing - There are significant business advantages associated with the real-time processing of data. With artificial intelligence, enterprise IT organizations can effectively analyze large volumes of business data at scale and in real-time. As a result, these organizations can respond more quickly to anomalies or security events that are picked up by their AIOps tool.
• Rule and Patterns - To accurately detect network events that warrant a response, artificial intelligence use rule application and pattern recognition algorithms. They may even use machine learning algorithms that allow them to develop their own rules for detecting network anomalies based on training data sets. Rules and patterns are used to distinguish between network activity that is considered "normal" and that which is deemed "anomalous".
• Domain Algorithms - Domain algorithms are specific to an industry or IT environment, and their contents and structure are dictated by an IT organization's unique goals and data. These algorithms define the specific operational goals that will be prioritized by the artificial intelligence.
• Artificial Intelligence and Machine Learning - The defining feature of AIOps. When it comes to AIOps software, artificial intelligence implementations are geared towards "intelligent analysis" of large volumes of data and the capability of determining which circumstances require a security alert and which do not. Machine learning uses predictive analysis to enhance the AI application's ability to accurately identify anomalous network activity over time.
• Automation - Reducing workload for IT operators is one of the main reasons that AIOps tools exist, making automation one of their most important features. AIOps can be used to automate real-time testing of new software features and user stories or to perform in-depth log analysis and detect errors and anomalies.

With applications that include operational, security and business analytics, Sumo Logic is helping IT operations do more by leveraging artificial intelligence. With Sumo Logic's patented machine learning technologies, LogReduce® and LogCompare, IT organizations can aggregate large volumes of logs, events and time-series metrics, identify and predict anomalies in real-time and deliver crucial security and operational data to where it can be used to guard against data breaches and optimize the customer experience.