Consolidate Your Monitoring Stack with AIOps.

Apr 1, 2021. By Aleena Mathew

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Consolidate Your Monitoring Stack with AIOps

The era of digital transformation created a significant impact on IT organizations. Business and IT systems were intact as IT operations no longer needed to rely on traditional IT systems and actions. So this change was a must. However, the joy of this transformation did not last long. As the digital era took place, the entire IT system got digitized. The adoption of modern technology and architecture came into prominence. Simultaneously with that came the use of multi and hybrid clouds. All of this adoption just started to increase the complexity of the entire IT system. After adopting the digital transformation, the IT organizations end up with multiple tools that increase the complexity to a much higher level. They needed to use multiple monitoring methods to consolidate their complete IT systems. This scenario increased the IT operators' burden as they were responsible for ensuring the entire system's smooth flow. The whole IT organization needs the right solution here, and they need to reduce operational cost and time due to this complexity.

AI-based Monitoring Tool Consolidation:

Modern problems need modern solutions, and Artificial intelligence is the right solution here. AI helps the IT team to automate the entire IT operations completely. With the introduction of AIOps, IT system monitoring was automated by AI-based models. AI models helped in eliminating the use of legacy monitoring tools. All IT resources and elements were consolidated into one single platform, wherein the AI-based observability took place. AI-based Observability helped in analyzing the entire system and providing the right inference and insights. With this implementation, monitoring of the entire system was made simple. Managing the data generated was also one other difficult task. The amount of data generated was high, and monitoring these data and identifying outliers was difficult. But with AI-based observability, this process was also made easy. The AI models were capable of ingesting every IT data from these tools and resources. With this, the system will automatically predict if there is any abnormality such as an anomaly or an event and intelligently alert the IT operators.

AIOps helps in consolidating the entire monitoring process into one single platform. The AI-based mechanism helped pinpoint where the exact problem is and how the IT operators can immediately act on the issue. Moreover, IT operators were able to get end-to-end visibility of the entire IT system, and eventually, the mean-time-to-resolve was also reduced to a great extent. AI helped in automating most of the IT system and thus increasing the speed of business.

Benefits of AI-based Monitoring Tools Consolidation:

The adoption of AI in ITOps is really on the go as it poses large benefits for IT and business. Similarly, AI holds a potential set of benefits in the case of monitoring tools consolidation. Some are mentioned below:

1. Reconciliation of legacy monitoring tools: One of the main challenges faced was the use of several legacy monitoring tools. With the evolvement of the digital era, the utilization of IT resources increased. Legacy monitoring tools were not able to keep track of and monitoring the continuous changes with these resources. Moreover, the proper inference could not derive from them. That's where the entire legacy monitoring system can replace with AI. AI-based observability helped in deeply analyzing the whole system and provide the right and accurate inference. In this way, the IT team was able to consolidate every monitoring tool into one single platform.

2. Operational Cost-saving: The use of multiple tools just led to a situation where the operational cost was high. The use of numerous tools to monitor the entire IT system was just not being efficient. This just added up more time and more people. AI-based models helped in consolidating the whole monitoring tools into one single platform. In this way, the need for using multiple tools was avoided and eventually reduced the operational cost.

3. Intelligent analysis and inference from data: The use of multiple tools made it difficult for the IT operators to pinpoint any system issues. It was difficult for the team to identify what the problem is and where exactly did it occur. It wasn't easy to track and consolidate the proper inference from the data. Moreover, if any issues occur in the system, the IT team needed to spend hours identifying the issue. AI-based consolidation helped in automating this process. The AI-based monitoring tools or platforms helped provide the right inference from every IT data, and intelligent analysis was made.

4. Ease in identifying anomaly and root cause events: With an AI-based consolidated monitoring system, every IT data was correlated and monitored in one single platform. The AI-based models helped in pro-actively identifying any anomaly or abnormal events from the system. This pro-active analysis method helped identify unknown issues from a considerable volume of data and enabled the system to perform auto-remediation or auto-fulfillment.

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

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