Infrastructure Monitoring with AIOps for Effective IT Management

For years, organizations have collected and analyzed huge volumes of data to identify issues with IT infrastructure either manually or with conventional monitoring tools. Now, more companies are investing in AIOps (Artificial Intelligence for IT operations) to automate monitoring and streamline data sets. By fusing AIOps with traditional infrastructure monitoring, we can create a more comprehensive solution for maintaining IT performance and enhance the four stages of monitoring:

1. Data collection enhanced by AI-driven analytics
2. Streamlined event processing with AIOPs
3. Accelerating incident management through AIOPs automation
4. Fostering continuous improvement with integrated technologies

Learn more about the convergence of infrastructure monitoring and AIOPs below:

Introduction: The fusion of infrastructure monitoring and AIOps

Fusing AIOps and conventional infrastructure monitoring provides an end-to-end overview of all the systems and components in an IT setup. With a clearer perspective of our IT environment, we can achieve the following:

Generate a single source of truth

Incorporating AIOps into infrastructure monitoring allows us to generate a single source of truth for all the monitoring data from on-premises and multi-cloud servers, databases, containers, data centers, virtual machines, and other sources. Instead of using different tools to monitor infrastructure, we can generate insights about systems and components from one location, identifying issues that impact these technologies. That’s thanks to the capabilities of AI, which uses algorithms to collect data from far more sources than conventional monitoring tools.

Identify problems in systems and components quicker

The convergence of AIOps and infrastructure monitoring can prevent downtime and other incidents in a quicker timeframe. The combination of these technologies makes it simple to identify problems in systems and components that might lead to latency issues or containers and hosts failing. For example, machine learning algorithms can detect issues with systems and alert us when we need to take action.

Free up time

With a 360-degree overview of systems and components, IT staff can spend more time on tasks other than data analysis. AI-powered infrastructure monitoring tools generate powerful dashboards, reports, and other data visualizations that improve connectivity and tell us what we need to know about our IT environment. There’s no need for manual calculations or spreadsheets when analyzing monitoring data, which improves the user experience for IT teams.

Stage 1: Data collection enhanced by AI-driven analytics

AI and data collection make a perfect match, with AI-driven analytics able to accumulate real-time intelligence and actionable insights about server logs, IoT devices, networking resources SaaS tools, and apps from multiple data sources. These metrics improve observability by allowing us to identify patterns and trends in data sets, resulting in smarter decision-making.

Traditional monitoring tools collect data through manual or rudimentary statistical methods, with data originating from spreadsheets or basic analysis software. Data engineers might need to build complex data pipelines involving code when moving information about IT infrastructures into monitoring applications, which can increase human error and sometimes render data analysis useless. Data scientists then need to interpret data and present their findings to team members.

Conversely, AI analyzes large amounts of data through automated machine learning algorithms, making data analysis quicker and more reliable. This approach provides a holistic overview of system performance and health without any of the hard work. All of this is possible by fusing AIOps and infrastructure monitoring.

Stage 2: Streamlined event processing with AIOps

Tracking and analyzing information about events, such as system crashes, network outages, and downtime, can be challenging. Traditional event processing makes it difficult to identify critical events when this technology raises false flags, such as non-critical errors, or doesn’t provide enough context about events that should warrant our attention, such as cybersecurity flaws.

An infrastructure monitoring and AIOps platform streamlines event processing in the of ways. First, it prioritizes events based on their importance, helping us learn which events require action. Again, this process is possible because of clever algorithms that understand the real-world impact of things happening in our IT environments. Over time, these algorithms become smarter and generate even more valuable insights for monitoring.

Secondly, AI streamlines event processing by reducing the number of false flags generated by infrastructure monitoring tools. For example, we can tailor an AI-driven monitoring platform to only alert us about events that need resolving at that moment in time. This might eliminate a phenomenon called "alert fatigue," which happens when IT teams receive too many false positives and eventually overlook or ignore all notifications.

Stage 3: Accelerating incident management through AIOps automation

As well as identifying critical events, AI-powered infrastructure monitoring software can discover the root cause of problems in an IT environment. This can prevent the same issue from happening in the future. These platforms can also provide actionable insights from root cause analysis, helping the end-user resolve problems quickly.

Say your organization experiences a downtime event. AI can learn whether a technical glitch, network outage, natural disaster, or other factor contributed to the downtime, making it easier for your IT team to fix the issue in your environment.

While AI automates root cause analysis, IT teams can proactively identify and address recurring issues themselves with AI-driven analytics and workflows. AI technology will quickly identify ongoing problems and their likelihood of happening again.

Stage 4: Fostering continuous improvement with integrated technologies

AI not only analyzes historical data about IT environments but predicts events that might jeopardize infrastructure weeks, months, or even years from now. AI-powered predictive analytics, for example, forecast future problems that might result in a critical event, allowing teams to take action now and prevent worst-case scenarios from happening. This action might involve improving security standards and safeguarding physical infrastructure.

When using AI for infrastructure monitoring, it’s critical to establish a feedback loop to detect issues within code. For example, IT teams can use feedback from monitoring tools to increase the effectiveness of systems and components. A feedback loop can also lead to continuous improvement, where IT teams learn how to make more positive changes to IT environments in the future.

Conclusion: Embracing the convergence for optimal IT performance

Converging AIOps and infrastructure monitoring can revolutionize IT management. By integrating these two technologies across the four monitoring stages, we can enhance data collection, streamline incident management, and foster a culture of continuous improvement.

Improve digital transformation in your enterprise by converging AIOps and infrastructure monitoring. Contact us to learn more about our expertise and experience in both of these technologies.

‍