Currently, 91% of IT decision-makers recognize that automation is a game changer for tomorrow: they anticipate that over the next three to five years, all IT systems will be capable of reacting autonomously in alignment with business goals. Key to automated operation (AO) in IT is the use of artificial intelligence via AIOps. It is a system that employs AI technology to anticipate/identify proactive and reactive occurrences from unstructured data generated by various monitoring instruments and other sources. This has major implications for IT as we know it today.
What is AIOps?
Gartner defines AIOps as a means to “combine big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” Gartner coined the phrase in 2016 as an industry classification for machine learning analytics technologies that enhance IT operations analytics.
Since then, we have witnessed the rise of AIOps not only as a methodology, but also as a type of software platform that packages all the tools IT needs for event/machine data analysis and handling, without having to build it from scratch.
Simply put, AIOps merges diverse manual IT operations solutions into a single intuitive, intelligent, and automated IT ops platform. Powered by end-to-end visibility and context, your team and you can react faster — even preemptively — to slowdowns and disruptions. At its core, lies a deluge of data (now properly organized) and advanced data analysis algorithms.
(Also Read: What is Application Programming Interface)
What Are the Components of AIOps?
AIOps makes use of the following elements to enhance IT operations:
1. Data consolidation from different sources
AIOps collects data from several IT infrastructure streams, like event records, system monitoring, apps, job data, and tickets. Eliminating information silos makes it simpler to manage, monitor, and connect network events to identify causation.
2. AI algorithms
It covers ML and AI algorithms that are industry- or IT-specific. The primary goals and resources of an IT firm determines its contents and structures. These algorithms establish the operational objectives that artificial intelligence will prioritize.
3. Business rules
AIOps employs business logic and pattern classification to reliably identify events demanding a reaction. It can even employ machine learning methods that enable them to create unique rules for spotting abnormalities dependent on sets of training data. The distinction between “regular” and “anomalous” network activity is established via rules and patterns.
4. Data processing
Real-time data processing enables ITOps teams to fulfill their performance optimization objectives and aids security analysts in deploying countermeasures. AI allows for the effective ingestion and analysis of enormous quantities of data at scale and also in real-time. As a consequence, you may detect abnormalities and react to events recognized by the AIOps tools more rapidly.
5. Cognitive technologies
This is the feature that defines AIOps. Intelligent examination of vast amounts of data is accomplished by artificial intelligence. Through mathematical equations that correlate and sift through machine data to generate histograms, charts, and visuals, it carries out in-depth analysis. In addition, machine learning can “learn” from your actions and tweak the model accordingly, autonomously. The insights are presented using dynamic (and often real-time) dashboards.
6. Connected workflows
AIOps may be used to automate and coordinate several IT operations. It may, for instance, conduct real-time evaluation of newly introduced functionalities or detailed log inspection to discover faults and abnormalities. To enable this, AIOps platforms are connected to other components of the IT monitoring ecosystem through application programming interfaces (APIs).
How Does AIOps Work?
The working of AIOps can be broken down into three steps.
- First, it gathers and aggregates the massive and ever-growing amounts of data generated by different IT infrastructure components. This may contain application requirements, performance monitoring instruments, or service ticketing systems.
- Second, it intelligently differentiates between”signals” and “noise.” It then organizes and connects this relevant information according to various parameters, such as language, chronology, and topology. This facilitates the identification of critical incidents and patterns associated with system performance and availability concerns — along with minimal false positives and false negatives.
- Thirdly, it identifies the underlying causes of incidents and informs the IT and DevOps departments for timely remediation. In certain instances, it may even address these difficulties automatically without human intervention.
- Finally, it facilitates collaboration between individuals who manage IT infrastructure. Not only will AIOps alert the relevant operatives and groups, but it will also encourage collaboration between them, especially when people are geographically dispersed. In addition, it maintains event data that helps expedite future diagnosis of similar circumstances.
6 Top Benefits of AIOps
Both tech companies and enterprises with large IT teams are now increasingly adopting AIOps for the following reasons:
1. Greater observability
Observability is the capacity to consume, aggregate, and analyze a continuous flow of performance data from dispersed programmes and the hardware on which they operate. This enables more efficient monitoring, troubleshooting, and debugging of the application in order to satisfy service level agreements (SLAs) and other business needs.
2. Automate predictive actions
AIOps systems can analyze and correlate data to provide advanced analytics and automated action. Using predictive analytics, you may automate dynamic resource optimization, ensuring application performance while safely decreasing resource cost, even during significant demand unpredictability.
3. Minimize downtime
System and application downtime may be expensive because of lost revenue, reduced productivity, and reputation damages. AIOps enables IT, DevOps, DevSecOps, or site reliability engineering (SRE) teams to recognise and respond to developing problems before they become significant and calamitous problems.
4. Keep up with security threats
As environments develop in terms of complexity and size, the number of hazards to address also increases. Manual techniques cannot keep up with the pace of change, but AIOps solutions let you to identify, evaluate, prioritize, and remediate vulnerability concerns.
5. Optimize the use of human resources
Automatic detection of operational problems and reprogrammed reaction scripts will decrease operational costs by allowing for efficient resource allocation. This also frees up human resources to focus on new and complicated tasks, resulting in a better employee experience.
6. Improve outcomes
By eliminating IT operational clutter and integrating ops data from diverse IT settings, AIOps can identify root problems and suggest remedies more quickly and accurately than a human could. This allows enterprises to establish and achieve mean time to resolution (MTTR) targets that were previously unattainable. This makes a massive difference to shared service organizations and managed service providers.
Is There a Downside to AIOps?
AIOps has several advantages; however, its deployment has its share of disadvantages. AIOps implementation necessitates the introduction of substantial modifications to IT processes. Additionally, it changes the roles and duties of IT staff. Employees could consider this to be a threat since they fear it might result in relocation or termination.
In addition, you need a comprehensive grasp of AIOps to automate activities effectively. Although this technology automates the majority of processes, it is not entirely independent. This necessitates the presence of an individual within the organization who is fully acquainted with its operations.
AIOps mostly automates regular operations that do not need specialized knowledge. This allows IT employees to concentrate on other productive pursuits, such as process enhancements and system optimization. Conversely, if people are now confined to activities that AIOps may easily accomplish, this could create a resource issue.
Some of the top options to consider are AppDynamics, Splunk Enterprise, Moogsoft, Sumo Logic, and In order to reap the full advantages of AIOps, organizations must do more than embrace tools with statistical correlation-based algorithms. Organizations must deploy an AIOps platform that delivers end-to-end transparency, observability, and accountability. Instana; your decision will depend on the exact business benefits of AIOps that you are looking to achieve, the use cases, and your current IT landscape.