In today’s IT environment, the foremost challenge faced by ITOps, NOC, DevOps, and SRE teams are manually handling numerous events and identifying the cause of a problem, and rectifying it.
To find out why an issue has occurred, you need to uncover the underlying cause. Sometimes identifying the root cause of a problem can be complex.
However, In this modern environment following the same old traditional method likemanually searching through the metrics or logs to identify what has changed, can consume hours of valuable time, which is why an efficient root cause analysis process and having the right analysis tools in place is vital.
In this article, we’ll look deeper into Root cause Analysis and the benefits of root cause analysis.
Let’s dive deeper…
So, what is Root Cause Analysis?
Root cause analysis is a method used to find the underlying cause of the problem or defect. The primary aim of the root cause analysis is to discover the root cause of a problem or event. The secondary aim is to fully understand how to fix, compensate, or learn from any underlying issues within the root cause. The last goal is to apply what we learn from this analysis to systematically prevent future issues.
Root cause analysis pinpoints the factors that contribute to the problem or event. Root cause analysis reduces cost and time spent by identifying the problems early. This helps in maintaining an agile environment. Modern IT environments are complex and chaotic, and therefore a traditional RCA method cannot address all types of incidents and outages. That’s where Machine learning comes into place. Machine Learning is a subfield of artificial intelligence that focuses on developing and researching algorithms that learn from data. These methods are unbiased and based purely upon historic and real-time data. AIOps provides a range of features and capabilities that together provide a comprehensive solution for any scenario.
Automated Root Cause Analysis capability uses advanced Machine Learning algorithms to help organizations identify changes in infrastructure and applications that cause the majority of today’s incidents and outages. Anomaly Detection plays a crucial role in automated root cause analysis. Anomaly detection compares current behavior with what it considers reasonable, and if it sees a significant deviation, it will flag the component as a potential root cause. In short, Automated Root Cause Analysis uses Anomaly Detection to decide if a component can be a root cause of a problem.
Automated Root Cause Analysis walks through the topological graph and examines each component, figuring out if it is misbehaving or not. By pinpointing the root cause of incidents in real-time, AIOps helps organizations and their operations teams rapidly investigate and resolve the problems faster and easier. It allows you to see the root cause of a problem immediately and to focus on solving it thereby reducing the meantime to repair.
BENEFITS OF ROOT CAUSE ANALYSIS:
- Saves time:
It helps to identify the root cause of a problem faster and saves time. So, that we can concentrate on more important functions.
- Boost employee Productivity:
Improves employee productivity by identifying the root cause of an event faster and easier.
- Real-time threat visibility:
RCA enables the IT department to detect the cause of an event in real-time and helps in resolving them.
- Minimize costs:
Simply stated, solving the problem once is the more cost-effective solution versus continual firefighting.
- Reduce MTTR:
Transform the events into actionable alerts that pinpoint the root causes of the service issues and helps in reducing MTTR ( Mean Time To Repair).
Autointelli AIOps platform automatically detects potential problems in complex IT systems without human interference. It aids in managing large systems, improves customer experience, maintains team productivity and helps to stay competitive. To experience the benefits of Autointelli AIOps platform in your business, Feel free to contact us and talk to our infrastructure experts today. (link to contact us page)
Hope this article was helpful! Are there any likely points you think we missed? Let us know!