AIOPs as it is..
With IT Infrastructure services and operations expanding its horizons across data centers, containers, virtualized servers, microservices, networks, etc.: There came an inherent need for a one stop solution.
A solution which could answer the questions raised during huge outages including multiple teams. While the legacy log monitoring mechanism was helpful earlier , it wasn’t scalable to predict, correlate and suggest remediation steps. Thus Gartner coined a magic term named AIOPS.
What is AIOPS?
AIOPS stood for Algorithmic IT Operations and now has been redefined as “Artificial Intelligence for IT Operations”. It is the answer to any Why questions your Enterprise Infrastructure Operations teams are asking.
In a nutshell, it is the application of Data Science and Machine Learning to IT operations problems .
Why AIOPS?
A new FAD in the Enterprise Infrastructure Space , this term alone has kept many eyeballs rolling in the past couple of years. The fluid definition still has a defined structure consisting of the following core elements :
- The Elements of AIOps
2. Machine Learning
3. Performance baselining
4. Anomaly detection
5. Automated root cause analysis
6. Predictive insights
Data to Everything :
Data being the new currency, the new oil which rules the business success of many enterprises still has an unexplored potential for many organizations. The early adopters (eg: Splunk Inc.) say that 73% of ITOPs data goes unused . Operations teams work in silos with their own favorite monitoring tools . There is an inherent need for a single platform that unifies all the distributed data and make the best use of AI and ML for IT Operations Analytics.
Future :
“While we can’t predict the future , we can certainly create it” .The mindset shift across teams would help organizations make the best use of AIOPs to benefit teams, reduce silos and improve automated root cause analysis.
: Abhishek Shukla
