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Nowadays, the number of software systems used by organizations has increased drastically. Since IT infrastructures comprise of complex software systems, the number of incidents has also increased. An incident response process requires collaboration between various IT teams in an organization. Better the communication between IT teams, faster is the incident resolution. Organizations need an effective incident response system to mitigate the issues within the IT infrastructure.
The traditional incident management systems are not able to cope with the increasing complexity and volume. Organizations are now looking towards AI for applications monitoring and effective incident response. New-age technologies have a huge role to play when it comes to incident management. Read on to know five steps to accelerate incident response within your IT infrastructure.
1. Find the root cause of the incident
The fundamental step in the incident response process is to find the root cause of an incident. The incident may occur at an endpoint device or any crucial software system. To identify incidents in real-time, you will have to ensure round-the-clock monitoring of your software systems. AI for application monitoring is the solution for identifying an incident in real-time. An AI automated root cause analysis solution will help you in the following ways:
- An AI-automated root cause analysis solution collects data from numerous software systems and endpoint devices. When an incident occurs, it can tell you the exact location of the cause within the IT infrastructure.
- AI for application monitoring can find the dependencies between IT issues. One IT incident leads to another and, they all ultimately lead to capacity exhaustion. With the help of dependencies between errors, an AI automated root cause analysis solution can report future incidences.
- AI platforms ensure round-the-clock monitoring without the need for constant manual intervention. In the wake of a global pandemic, organizations quickly moved to a remote working culture. An organization cannot hire system administrators to constantly monitor the crucial software systems. AI data analytics monitoring tools can ensure that you know about an incident even if it occurs outside working hours.
With AI data analytics monitoring tools, you can fix IT incidents quickly as you will know the root cause of the incident beforehand. MTTD (Mean Time to Detect) is a metric that can be used to determine the time taken by IT teams to find an incident. By using an AI automated root cause analysis solution, you can decrease your MTTD significantly.
2. Acknowledge the incident
Once you have determined the root cause of an IT incident, you must acknowledge it. There might be many IT teams in your organization and you need to choose the one with the appropriate resources to solve an IT incident. If your IT teams waste more time in deciding which one has the appropriate resources, your service availability will get hampered. MTTA (Mean Time to Acknowledge) is the metric used by organizations to measure the time required in choosing the right person/team to solve an IT incident. AIOps (Artificial Intelligence for IT Operations) is the recent solution being adopted by organizations to reduce their MTTA. An AIOps based analytics platform can help you in the following ways:
- An AIOps based analytics platform will know which IT team is responsible for fixing an incident.
- Along with determining the responsible IT team, AIOps can also provide actionable insights.
- With the help of actionable insights, IT teams can quickly start working on an IT incident. An AIOps based analytics platform can guide you through the steps required to solve it.
3. Filter incidents
If an incident occurs within the IT infrastructure, then a unique ticket is generated to find it. IT teams identify an incident with the aid of its unique ticket number. Once a ticket is raised, the monitoring systems send alarms to IT teams. However, multiple tickets can be generated for a similar type of incident. For example, if your official website is down, multiple users can raise a ticket regarding it. You don’t want redundant incidents to appear and bog down the speed of IT teams.
AI data analytics monitoring tools can aid in filtering redundant incidents and simplifying the task for IT teams. The incident-to-ticket ratio should always be 1 to reduce the alert noise. AIOps platforms in the market make sure that the alert noise of incidents is reduced for better observability.
4. Prioritize the incidents based on their impact
Not all incidents have the same degree of impact on the organization. For example, an incident with the organization’s mobile application is less impactful than an incident within the secure storage servers. The incident with the mobile application also needs to be managed but not before securing the storage servers. IT teams need to prioritize the incidents based on their impact and, doing so manually is an uphill task. This is where organizations use AIOps based analytics platforms for measuring the impact of each IT incident and prioritizing them accordingly.
5. Monitoring of incident response process
While your IT teams are working on an incident, you need to monitor the incident response process closely. Organizations create an incident report after resolving an IT issue. Incident reports can be used in the future for solving similar types of incidents easily. An AIOps based analytics platform remembers if any incident has occurred twice and can recall the steps required to solve it.
Organizations are moving towards proactive incident management with the aid of new-age technologies and AI has played a substantial role in lowering alert noise and predicting power outages. Organizations are understanding that slow incident response affects business reliability and AIOps based analytics platforms serve as the most efficient solution!
