Learn how artificial intelligence for IT operations (AIOps) uses data and machine learning capabilities

What is AIOps?

Today, IT operations face increasingly complex challenges as the digital transformation of enterprises accelerates. In order to better manage and monitor IT infrastructure, AIOps technology came into being. AIOps is a solution that leverages artificial intelligence (AI) and machine learning techniques to automate IT service management and operational processes to improve efficiency, reduce costs, and reduce fault repair time.

At the heart of AIOps technology lies data processing and analysis. The AIOps platform can automatically collect, consolidate, and analyze large amounts of data from multiple IT infrastructure components, application requirements, and performance monitoring tools. By using machine learning and artificial intelligence techniques, AIOps can identify important events and patterns, helping IT teams quickly locate the root cause of performance and availability issues.

AIOps can also automate some common IT management tasks such as troubleshooting, predictive maintenance, and automated response. These features help improve the efficiency and reliability of IT operations and reduce labor costs.

In addition to improving efficiency and reliability, AIOps can help IT operations teams better anticipate and plan for future IT needs. By analyzing historical data and trends, AIOps can provide insights into IT resource usage and help IT teams better plan and optimize IT infrastructure.

Implement AIOps

AIOps is a solution that leverages artificial intelligence (AI) and machine learning (ML) technologies to help businesses better manage and monitor their IT infrastructure and improve efficiency and reliability. AIOps can automate IT service management and operational processes to improve efficiency, reduce costs, and reduce downtime.

Implementing AIOps requires a few key capabilities, including observability, predictive analytics, and proactive response. Observability refers to software tools and practices that capture, aggregate, and analyze the continuous flow of performance data generated by distributed applications and the hardware that runs them to more efficiently monitor, diagnose, and debug applications to meet customer experience expectations, service-level agreements (SLAs), and other business needs. Provides a holistic view of applications, infrastructure, and networks through data aggregation and consolidation, but does not take corrective action to address IT issues.

Predictive analytics is another key capability of AIOps that analyzes and correlates data for better insights, better automation, and helps IT teams stay on top of increasingly complex IT environments and ensure application performance. Organizations benefit from automated anomaly detection, alerting, and resolution recommendations that reduce overall downtime and the number of incidents and tickets.

Proactive response is the third key feature of AIOps that helps IT teams detect issues and resolve them faster. Some AIOps solutions proactively respond to unexpected events, such as performance degradation and operational disruptions, bringing together application performance and resource management in real time. With the ability to predict IT issues before they occur, AIOps tools can initiate relevant automated processes to respond and quickly correct problems.

Before implementing AIOps, companies need to assess their current situation in this area and choose a tool with these three key functions. These tools can collect and aggregate data from multiple IT domains, helping IT teams make better decisions and respond to technical issues. AIOps technology can help companies improve the employee and customer experience, ensure timely resolution of IT service issues, and provide a safety net to address issues that can lead to human oversight oversight, such as organizational silos, insufficient team resources, etc.

Advantages of AIOps

AIOps (Artificial Intelligence Operations) is a solution based on artificial intelligence and machine learning technology that helps businesses better manage and monitor their IT infrastructure and improve efficiency and reliability. The advantages of AIOps are mainly reflected in the following aspects:

1. Reduced mean time to resolution (MTTR)

AIOps uses artificial intelligence and machine learning to automate IT service management and operational processes to quickly detect, process, and resolve performance degradation and operational disruptions. It eliminates useless information from IT operational data, correlates operational data across multiple IT environments, and identifies the root cause of problems and proposes solutions more quickly and accurately than manual operations. In this way, AIOps can reduce mean time to resolution (MTTR), helping organizations achieve previously unimaginable MTTR goals.

1. Reduce operating costs

AIOps automatically identify operational issues and rescript responses to help reduce operational costs and allocate resources more efficiently. This also frees up human resources, allowing them to do more innovative and complex work, improving the employee experience. At the same time, by automating operational processes and service management, efficiency and reliability can be improved and the error rate of manual intervention can be reduced. For example, Providence saved more than $2 million in cost through optimization measures while ensuring application performance during peak business periods.

1. Greater observability and better collaboration

The integration capabilities in AIOps monitoring tools enable more efficient cross-team collaboration across DevOps, ITOps, governance, and security functions. Greater visibility, communication, and transparency help these teams improve decision-making and react faster to issues. Through real-time monitoring and comprehensive analysis of IT infrastructure, AIOps can provide more accurate and reliable data, helping teams better understand the root cause and impact of problems.

1. Move from reactive to proactive and predictive management

AIOps continuously learns with built-in predictive analytics capabilities to discover and prioritize the most urgent alerts, enabling IT teams to address potential issues before they cause performance degradation or operational disruption events. This allows IT teams to move from reactive to proactive management and enable predictive management. In this way, AIOps can reduce mean time to detection (MTTD), reducing the resolution cycle of IT issues from weeks to hours, and saving significant time and resources. For example, Electrolux has reduced the resolution cycle for IT issues from 3 weeks to 1 hour by reducing mean time to detection (MTTD) and saved more than 1,000 hours per year by automating repair tasks.

AIOps Examples

Today's enterprises are facing the complexity and unpredictability of IT infrastructure operations, which is also a major challenge that needs to be addressed through new technologies and methods. AIOps (Artificial Intelligence Operations) is one such technology that is based on capabilities such as big data, advanced analytics, and machine learning that can help businesses better manage and monitor their IT infrastructure and improve operational efficiency and reliability.

AIOps has a wide range of application scenarios, including root cause analysis, anomaly detection, performance monitoring, cloud adoption/migration to the cloud, and DevOps adoption. Among them, AIOps can help enterprises quickly identify and solve problems in IT operations. By eliminating useless information from IT operational data and correlating operational data across multiple IT environments, AIOps identify root causes and propose solutions faster and more accurately than manual operations. This can reduce mean time to resolution (MTTR), helping organizations achieve previously unimaginable MTTR goals.

In addition to this, AIOps can also help enterprises reduce operating costs. AIOps automatically identify operational issues and rescript responses to help reduce operational costs and allocate resources more efficiently. By automating operational processes and service management, efficiency and reliability can be improved and the error rate of manual intervention can be reduced. This also frees up human resources, allowing them to do more innovative and complex work, improving the employee experience.

In addition, AIOps can also improve the observability and collaboration ability of enterprises. The integration capabilities in AIOps monitoring tools enable more efficient cross-team collaboration across DevOps, ITOps, governance, and security functions. Greater visibility, communication, and transparency help these teams improve decision-making and react faster to issues. Through real-time monitoring and comprehensive analysis of IT infrastructure, AIOps can provide more accurate and reliable data, helping teams better understand the root cause and impact of problems.

Finally, AIOps can also help enterprises move from reactive to proactive management and enable predictive management. With built-in predictive analytics continuously learning, AIOps can discover and prioritize the most urgent alerts, enabling IT teams to address potential issues before they cause performance degradation or operational disruption events. This allows IT teams to reduce mean time to detection (MTTD), reducing IT problem resolution cycles from weeks to hours and saving significant time and resources.

How does AIOps work?

AIOps (Artificial Intelligence Operations) is an IT operations management method based on big data, machine learning, and automation technologies, which aims to integrate siloed IT operational data and improve the efficiency and reliability of IT infrastructure by analyzing and learning from this data.

The way AIOps works mainly includes the following steps:

1. Data consolidation: AIOps leverages a big data platform to bring together a variety of IT operational data, including historical performance and event data, streaming real-time operational events, system logs and metrics, network data, incident-related data and tickets, application requirements data, and infrastructure data. This data comes from disparate data sources and tools, and there may be silos, and AIOps can consolidate them into a single platform for management and analysis.
2. Signal and noise separation: In the integrated data, some data is valuable and can provide useful information, while other data is noise and has no practical meaning. AIOps uses focused analytics and machine learning techniques to separate signals from noise, focusing only on data that is meaningful to IT operations management.
3. Root cause analysis: AIOps can correlate anomalous events with other event data in the environment to determine the cause of an outage or performance issue and recommend appropriate remediation. By analyzing multiple data sources and tools, AIOps can more accurately identify the root cause of problems, improving the efficiency and accuracy of fault fixes.
4. Automated response: AIOps can automatically route alerts and recommended solutions to the appropriate IT team, and can even assemble a response team based on the nature of the problem and the solution. In many cases, it can process the results of machine learning, trigger automated system responses, or even resolve issues in real time before the user is aware of the problem.
5. Continuous learning: AIOps uses machine learning techniques to continuously learn and improve their analytical capabilities to better adapt to changes in the environment and new data sources. By learning from historical and real-time data, AIOps can continuously improve its own models, improve prediction precision and accuracy, and better support future IT operations management.