9 Elements of AIOps.

Apr 20, 2021. By Aleena Mathew

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9 Elements of AIOps

The adoption of digital transformation bought a significant change in the IT industry. Traditional IT systems got transformed into digital elements, which brought in the use of advanced technology. The advancement of technology made it difficult for IT operators to handle and maintain the entire IT system. The use of more multi cloud-based applications came into the picture and, the changes just started to create chaos in the system. As the number of resources increased, IT operators found difficulty in monitoring all these resources. There were situations where IT operators found it difficult to understand what happened to the system, where is the problem and so on. All of this impacted the business and IT operations at a noticeable scale. All of the above situations are overcome by the application of Artificial Intelligence(AI).

Introducing AIOps:

The implementation of AI in IT organizations brought in a tremendous change. Automation of entire ITOps activities is achieved with AIOps. AIOps platform could ingest in every structured and unstructured IT data and monitor all these and identify if there are any outliers in them. Cognitive automation is one other capability achieved by AIOps. AI enables the IT team to achieve cognitive automation by automating the complete IT Operations from identifying anomalies to remediate them automatically. AIOps platform uses analytics and machine learning to improve the IT organization's ability to make quick decisions and automatically act on by contextualizing large volumes of varied and volatile data. With the adoption of AIOps, IT operators can pro-actively receive alerts of the issues and remediate them before they persist into a bigger problem. The AIOps platform is divided into different elements. Let's look into the nine different elements of AIOps.

Elements of AIOps:

The integrated capabilities of the AIOps platform enable IT organizations to support business organizations on a proactive basis instead of reactive mode. AIOps platform can be broadly classified into nine different layers.

1. AI-based Observability

The AIOps layers begin with the observation of the entire IT system. AI-based observability helps in capturing a huge volume of structured and unstructured IT data. AI-based observability helps in data analysis and intelligently alerts the IT operators if there is an unknown problem within the data set. This implementation eventually brings more visibility across the IT organization's physical and virtual environment. The observability helps consolidate every application and resource into a unified platform that helps provide better accessibility and productivity.

2. Data Management System:

One of the crucial factors in an IT organization is the data management part. With the shift to the digital era, data evolvement is really at its peak. A large volume and variety of data are generated every second. Managing and storing these data is a tedious task. That's where a multi-model data lake comes in. It acts as a repository for storing configured items, topology, performance data, log data, event data, incidents, service requests, changes, releases, problems, errors, knowledge, and actions. The repository can be used for handling predictive analytics too. In this way, the evolvement of data is handled out smoothly.

3. Anomaly Detection System:

With AI-based observability, the system can automatically identify any unknown issues in the system that are anomalies. Anomaly detection is an advanced machine learning-based model serving a pipeline for detecting multivariate KPI anomalies and log anomalies. The anomalies in the system will be detected by the anomaly detection engine and alert the IT team. This mechanism ultimately helps the IT operators proactively identify issues and resolve them immediately to not persist to a bigger problem.

4. Event Reduction System

With the high use of IT resources, there is a chance that a lot of event noise will occur in the system. There can be false positives too. To identify which are the proper events that occurred in the system manually is a difficult task. The event reduction system uses machine learning models to identify any false positive in the system easily. In this way, the IT operators are proactively altered on the events and can perform auto-remediation.

5. Incident Recognition

Incident recognition is one other feature AIOps layers hold. This mechanism is capable of correlating multiple KPIs and log together to identify unknown issues. Based on this correlation, any unknown issues will be automatically detected by the system and, root cause analysis will be performed to determine the real cause of the problem.

6. Engagement System

The most significant part of any IT organization is the engagement among IT users and end customers to resolve any IT requests. AI-based models are capable of handling every IT ticket, whether it be incident tickets or service request tickets. Based on the ticket generated, AI-based models will automatically perform an auto-remediation or auto-fulfillment to resolve the ticket. By this, the IT operators can assure that no tickets will breach the SLA set.

7. AI-based Virtual Agents

AI-based virtual agents are capable of automating every L1-helpdesk activity. Virtual agents' implementation will benefit the organization, as IT operators do not need to do lean night shifts and wait to resolve every IT request. The cognitive agents are extended IT support team members who help provide 24x7 IT support using a conversational interface.

8. Cognitive Automation

Cognitive automation is one promise of AIOps. With the implementation of AIOps, IT operations were automated to the extent possible. This helped achieve cognitive automation, i.e., from observing the IT systems to identifying anomalies and automatically resolving them.

9. Change Management System

The change management system enables IT organizations to plan the change activities, assess the risks and impacts of the change, and execute the system changes with better outcomes.

To learn more about Algomox AIOps, please visit our AIOps Platform Page.

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