This blog is a continuation of Part 1: AIOps Will Continuously Drive Digital Transformation Of Enterprises
AIOps is a new platform that offers better business insights and competitive advantages. One of the most valuable things AIOps can do is to seek out the root-cause of issues, helping you to mitigate problems before they turn into major concerns or crises. This results in real-time problem-solving and increased business value, as well as an improvement in process speed and efficiency.
Benefits from AI based RCA and Resolution Processes
Deploying AIOps-driven RCA and resolution processes can generate a number of benefits.
AI based RCA:
Automated prediction of future incidents.
Automated recommendation of remediation steps for future incidents.
Elimination of redundancy in RCA process.
Resolution processes:
Automation of resolution process (auto-remediation).
Incident Response Process:
Automatic detection/correlation between incidents; Automatic resource allocation based on SLAs; Automatic communication to stakeholders/decision makers with real-time updates
Elimination of Redundancy in RCA process
Redundancy is the enemy of efficiency. Redundancy wastes time, money, people and resources. It can be difficult to eliminate redundancy in a business process like RCA because it's so easy to fall back into old habits when you're used to them—but it can be done.
One way that businesses have successfully reduced redundancy in their RCA processes is by making sure that no one person is responsible for doing everything themselves. Instead, teams are built around each step in the process with individuals assigned specific tasks within their team's scope of work (such as running reports or analyzing data). This means that everyone knows what they need to do while still keeping their work separate from everyone else's. This also helps prevent duplicate effort between different parts of the same task because tasks are separated out into different areas instead of being done concurrently by multiple people working together on one thing at once!
Automated Prediction of Future Incidents
As you might imagine, the ability to predict future incidents is a major benefit of AIOps. However, it's not only the benefit that matters but also how we can solve it: by using an automated process and machine learning algorithms.
Using AIOps, we can build our own model based on historical data and then use it to predict future incidents before they happen. This will allow us to proactively prevent these incidents instead of handling them after they occur.
The first step in this process is gathering all relevant data. Depending on your organization’s environment and operational maturity level, this might not be as straightforward as you would hope for; however, working with big data analytics tools like Hadoop or Spark can help mitigate some of these issues since they can handle massive amounts of information at once across multiple servers/devices simultaneously without slowing things down too much or costing too much money (though still pricey compared with traditional methods). Once collected from all relevant sources within an organization like security devices or servers/applications themselves then there are several different tools available which may work well depending on what type(s) exist already within one particular organization's current setup.
Automated Recommendation of Remediation Steps for Future Incidents
With AIOps RCA, you can create an automated solution to:
Recommend remediation steps for future incidents.
Reduce the time to resolution.
Reduce the cost to resolution.
Reduce the impact of incidents on business.
Reduce frequency of incidents
Extract patterns from historical data to predict future incidents
For instance, let's look at the following example:
If you find that your network performance is getting worse over time and the number of incidents has increased since last month, then this could be an indication that something is wrong.
If you find that a particular component has been failing frequently in all types of devices and this trend persists every month, then it will help you prioritize incidents based on which components are affected most often.
With these insights into your network's health, you can better understand how changes to your environment (e.g., adding more users) affect its performance and predict future incidents so that they can be prevented or mitigated before they happen.
It’s clear that AI based RCA has huge potential for improving the quality of IT operations. We can use AIOps solutions to learn from historical data, understand trends and behaviors, predict future incidents, recommend and even automate remediation steps for them. This will help us eliminate redundancy in RCA process, reduce risks, and improve our response times, scalability and standardization.
