Everything You Need To Know About AI For IT Operations (AIOps) Solutions (FAQs)
What Is AIOps?
AIOps— short for Artificial Intelligence for IT Operations, which is sometimes also known as IT data analytics, IO operations analytics, or cognitive operations—refers to a type of software that uses machine learning and big data analytics to automate IT operations processes such as anomaly detection, root cause determination, event correlation, and issue resolution.
Software and hardware are becoming increasingly powerful—but with more capabilities, they’re also becoming increasingly complex to manage. Previously, IT teams would simply have to hire more staff to deal with this complexity. However, with a rising talent gap in the IT and cybersecurity industries, hiring new technical talent isn’t always an option. AIOps solutions can help teams to resolve these challenges by automating data analysis to proactively identify risks and recommend or automate remediation actions.
To do this, AIOps solutions collect big data from numerous IT infrastructure components, such as ticketing systems, performance monitoring tools, and application demands. They then monitor and analyze that data to identify anomalies, issues, and disruptions to services, as well as areas for performance improvement across software and hardware systems.
Finally, once an issue is identified, the AIOps solution diagnoses its root cause and alerts the IT team so that they can quickly respond to it, and take steps to prevent it from happening again. And in some cases, AIOps tools can actually remediate issues automatically without human intervention. This means that AIOps solutions enable IT teams to proactively identify and respond to issues in their environments, before they become critical problems that may impact the end user.
How Do AIOps Solutions Work?
There are three key components to any AIOps solution: big data, machine learning, and automation. The AIOps solution extracts IT operations data from across the network or application and aggregates it in a single, central platform. This data can include historical performance and event data, real-time performance and event data, configuration data, system logs and metrics, network traffic and packet data, application demand data, infrastructure data, and incidents/ticketing.
One the data is collated, the AIOps solution begins its analysis. It applies machine learning algorithm, such as anomaly detection and predictive analytics, to detect patterns, relationships, and anomalies within the data, enabling it to identify issues and trace events back to find the root cause of each issue. This means that IT teams are able to cut through the noise to get to the heart of issues once alerted, allowing for faster remediation, and it also enables them to prevent the reoccurrence of any issues by fixing what caused them in the first place.
Finally, the solution deploys automated responses to the issues it detects. This could involve sending alerts to relevant teams and individuals, including contextual information to help them remediate the issue more quickly and facilitating efficient collaboration and remediation. Additionally, the solution could respond by remediating the issue using a pre-programmed response script.
As the system analyzes more data and uncovers more incidents, it learns about how the environment “normally” operates. This enables it to increase in accuracy over time, as well as adapt to changes in the environment such as new hardware or software being deployed, or reconfigurations.
What Are The Benefits Of Implementing AIOps?
There are a few key benefits to implementing an AIOps solution:
- Increase the value of your data: AIOps solutions collate data from across your IT infrastructure and use intelligent data analytics to identify patterns in casual data relationships and hidden connections across your environment that you might otherwise be unaware of. By increasing visibility into your data, AIOps improves the usability of your data so you can get more value from it.
- Reduce costs: By automatically identifying operational issues and running pre-programmed response scripts, AIOps solutions can reduce the time that IT teams spend on routine, repetitive, and time-consuming tasks, so they can spend their time and resources on more complex tasks. This better allocation of resources reduces operational costs, and it can also lead to improved employee satisfaction.
- Streamline your IT operations: AIOps solutions provide a central, contextualized view of the whole IT environment, then provide different IT teams with data and perspectives that are relevant to them. This eliminates the need for disparate teams to share and process information manually, helping to reduce human error and improve data privacy. It also ensures that the relevant teams are alerted to any issues, so that they can be remediated swiftly and effectively.
- Improve your mean time to respond (MTTR): By correlating data from multiple sources and applying ML and predictive analytics, AIOps solutions can identify issues—including their root cause—, prioritize them, and suggest remediation actions more quickly than if a person were manually scanning the environment for issues. They can also automatically respond to certain threats. For example, if the AIOps solution detects malware, it can automatically run an anti-malware function, remediating the issue immediately. At the same time, it can alert IT teams to anomalous activity—such as unusual downloads or logins—that they might not have been looking out for if carrying out manual scans, helping them to identify and remediate potential attacks before they start causing real damage.
What Features Should You Look For In An AIOps Solution?
When evaluating AIOps solutions, there are several key features you should look for:
- Data collection and ingestion: Support for collecting and ingesting data from various sources, including logs, metrics, events, and performance data from diverse IT environments.
- Data normalization and correlation: The ability to normalize and correlate data from different sources to provide a unified view of IT operations. This helps in identifying relationships and dependencies between different components across your environment.
- Anomaly detection: Robust anomaly detection capabilities using machine learning algorithms to identify unusual patterns or behaviors that may indicate potential issues or threats.
- Root cause analysis: Advanced analytics that help to identify the underlying issues causing incidents and problems in the IT environment.
- Predictive analytics: Predictive analytics capabilities to forecast potential issues before they occur, allowing proactive measures to prevent disruptions and downtime.
- Automated response: Automation capabilities to execute predefined responses or actions based on identified patterns or anomalies. This helps resolve issues faster, while freeing up IT resource to work on more complex remediation tasks.
- Incident management: Automatic alerting that notifies the IT team when there’s an issue they need to look into, alongside any useful, contextual information that could help inform their response. This can also be bolstered by integrations with incident management systems (e.g., ticketing systems and change management tools) to facilitate seamless communication and collaboration among IT teams. This ensures that incidents are tracked, prioritized, and resolved efficiently.
- Scalability: The ability to scale the solution to handle increasing data volumes and growing IT environments. This is crucial for large enterprises or organizations with complex IT infrastructures.
- User-friendly interface: An intuitive and user-friendly interface that enables IT teams to easily access and interpret data, perform analyses, and take necessary response actions.
