Utilizing Root Cause Analysis Software To Improve End-User Experience
With All Of This Data, Where Do I Start?
We’ve all been there. Your boss calls you, demanding answers on what is causing all of the issues the customers are complaining about. The application and server teams say “it’s the network”, while the network team says “the network is fine, it must be something with the application or servers.” Complexity greatly increases when you introduce SaaS and hybrid cloud for specific portions of your services. It’s not easy trying to analyze all of this data, typically stored on many different tools that are specialized for the teams using them. How do you use root cause analysis software to sift through millions of disparate data points to find the root of the interference?
The Answer Is Correlation!
Correlation is defined as a relationship between two or more things. How does this help you find the problem between all the moving parts between your application and your supporting service delivery infrastructure? Easy. You take advantage of root cause analysis software to know parent-child relationships and status snapshots to isolate root cause. Sound too crazy … too complex … or too good to be true? It’s not, and here’s how it’s done:
- Identify the components within the critical path between your network devices and the application;
- Determine the health of every component and sub-component in the critical path within time;
- Analyze the time to understand which items are truly a cause and which items are simply side effects;
- Components considered to be a cause should be considered as actionable;
- Resolving these incidents to restore the service or improve its performance profile.
The Difference Between Event Filtering, Rules And Correlation.
Filtering: pass events through a mechanism to remove unwanted items. This typically is useful for very small simplex networks and applications but falls short when many meaningful items are received simultaneously.
Rules: Like filtering, rules can be configured to remove unwanted items from your view. Rules are typically used to set a threshold on the number of times we see an event and when we believe it becomes actionable. This also works for smaller solutions and can be combined with filtering to reduce event noise.
Correlation: Analyzing all data points to identify which events meet the required rule and, by logic, determining which event were causal, thereby suppressing the ‘background noise’. This works best in all data centers whether simplistic and complex, public or private, hybrid or yet-to-be-defined … where large-scale events cause ‘event storms’.
How Do I Correlate My Event Streams?
The Optanix Platform was built with this exact problem in mind. With a goal of efficiency in mind, we focused on providing a solution to the problem of identifying the root cause of any incident through root cause analysis software. The benefit can be seen by the over 95% reduction in incidents experienced by teams using the Optanix Platform, in comparison to filter or rule-based solutions. Its ability to provide such a high level of efficiency stems from the ability to capture information from all portions of the monitored data center processed through its advanced decision engine.
Do I Need a Root Cause Analysis Software?
Only if the economics make sense. The Optanix Platform was not designed as a rip-and-replace solution. If what you currently have in place is capable of northbound integrations, the Optanix Platform can consume events from many different sources (our unofficial saying is that: data is our friend!) and process them in unison to determine from where the problems in your data center are stemming.
If you’re interested in a better user experience through ensuring the availability and reliability of our critical business services while also reducing your cost of ownership, consider what the Optanix Platform could do for you. Reach out to us to find out more. We love data … and we love people too!