Cutting down MTTR with AIOps.

Mar 30, 2021. By Aleena Mathew

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Cutting down MTTR with AIOps

The adoption of digital transformation is gearing to a great extent. Businesses cannot keep up with the race with the legacy mode of operations. That's where the digital era helped them. With digital transformation, the business and IT team started to see noticeable benefits and growth. But this transformation did not bring long-term joy for the CIO. During their transformation journey, they started facing a lot of challenges. With the adoption of the digital era, the complexity of the IT infrastructure started to shoot-up. The IT team could not handle the large volume of data generated. Also, they lacked in identifying insights from these data. This lead to a situation where the number of unknown issues started to pile up. There was a tremendous amount of chaos created in the system because the unknown problems persisted in the system, and the user began to file IT tickets. Most of these IT tickets were unresolved, increasing the Mean Time To Repair (MTTR) of the system. Breaching the SLA is not a negotiable situation in any IT organization. So we need to step-up in this situation.

Introduction to AIOps:

This is where the evaluation of AIOps took its place in IT organizations. AIOps is a term first coined by Gartner. It's the application of advanced analytics, which is applying ML and AI in IT operations. The hype of AI is high, and the adoption is widespread. Artificial Intelligence for IT Operations helped in automating most of the ITOps tasks. With this automation, the IT team was able to focus more on other critical activities. Let's see some of the scenarios where AIOps helped the IT team. One challenge faced was in detecting unknown problems from the large volume of IT data. With the amount of data generated, it was difficult for the IT operators to discover unknown issues. When these problems are left unresolved, they persisted to issues that became massive chaos. So identifying these issues was a challenging task for the IT team. That's where AIOps helped the IT team. AI-based models were capable of ingesting a large volume of structured and unstructured data. AI models analyzed and provided insights from the data collected and automatically identify any unknown issues by the mechanism of anomaly detection. This is where the capability of AI-based observability worked. AI-based observability helped to ingest every KPI and log data and provide accurate insights from them. By adopting observability, the IT team was to pin-point out what exactly the issues were and take the right actions to prevent them.

Let's check into one other scenario where AIOps helps in IT. We have seen how efficiently the AI-based models can trigger and identify any anomaly in the system. All of these anomalies are logged as IT tickets. Apart from that, IT users will register their concerns as tickets based on priority. This leads to a situation where the IT tickets logged start the pile-up and make IT operators difficult to resolve. By this, the SLA for each ticket set starts to breach. This eventually starts to increase the MTTR. AI-based models are capable of automatically routing tickets and perform auto-remediation and auto-fulfillment of these tickets. In this way, MTTR can be reduced to a great extent. Let's take a deeper look into how the implementation of AI can reduce MTTR.

Cutting Down on MTTR:

Incident's occurrence in IT systems is unavoidable. With the occurrence of an incident comes the term Mean Time To Repair(MTTR). The MTTR measures the time it takes a team to solve the issues that occurred in the IT system. The longer the time taken to resolve IT tickets, the higher is risk in the system. We need an effective mechanism capable of identifying problems proactively and resolving them automatically to reduce MTTR. Let's take a look into some of the steps:

1. Proactive Detection of Anomalies: Proactively identifying anomalies helps in uncovering unknown issues before they affect the production system. Identifying these issues enables the IT team to analyze where the problem is and allows the team to take the required actions before affecting the business and IT operations. Every IT data is observed, and effectively KPI and log anomaly detection will take place based on the AI-models. This pro-active analysis helps in reducing MTTR.

2. Noise Reduction: With the large volume of data generation, chaos and IT issues started to rise. Most of the issues were filed as IT tickets, whether it be an incident or a service request. This created a lot of noise in the system, and it is difficult for the IT operator to prioritize the tickets and resolve them. Not handling these tickets led to a scenario where SLA was getting breached and increased the MTTR. Noise reduction helped the IT team to waste their time on unwanted and false-positive events. In this way, actual problem tickets were resolved on time based on their priority to meet the SLA.

3. Intelligent Alerting Intelligent alerting is one of the great ways to route incidents. The altering mechanism helps in automatically notify the incidents to the IT operators as they occur. In this way, the IT team does not need to monitor the system continuously. Based on the alerts that are occurred, they need to resolve them. In this way, no issues will get unresolved and left unseen.

4. Automated Incident Remediation Incident remediation or auto-fulfillment of IT service tickets are significant in reducing MTTR. AI-based auto-remediation enables in automatically resolving IT incident tickets as they occur. IT operators do not need to worry about resolving them as the AI-based models can handle the IT tickets. These models analyze the tickets and automatically initiate a workflow and perform the remediation of the ticket. In this way, the tickets get resolved without breaking the SLA, and the MTTR is reduced.

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

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