AIOps POC no longer have to be long and resource intensive

Gartner predicts that large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. And this prediction is soon turning into a reality. AIOps is showing promising business value as it impacts measurable metrics such as mean time to detect (MTTD), mean time to acknowledge (MTTA), mean time to restore/resolve (MTTR), service Availability, percentage of automated versus manual resolution, and so on.

So, the question is not about validating the business value with the implementation of AIOps. The concern to most CIOs is the speed of those implementation cycles. And, that brings me to address the fundamental challenges that come along with long implementation cycles of your AIOps POC which are addressed by CloudFabrix.

Re-calibrating to launch your next POC with AIOps

Here is what you should know and expect if you are launching your proof of concept, a use case that can be implemented with AIOps. CloudFabrix’s POC Express is designed to bring down validation cycles from a few weeks to just a few days.

Building an Integration Ecosystem

AIOps is all about integration of different data sources and IT operational tools streaming relevant events, metrics and logs. Integration remains the main challenge for those implementing AIOps. You need to have historical data from key data sources i.e. alert data, ticket data, triage data etc. 

CloudFabrix as an AIOps platform identifies the disparity in data formats and the exclusivity that comes with each integration.

AIOps platform has made it seamless for IT teams to access the right performance insights across multiple data sources without resorting to superficial monitoring.

Making sense of data

Enterprise data is disparate and scattered. This is because a single entity is represented differently in different places. There is no single method to establish the relationship between data points. It is important for monitoring tools to look for relationships and ways to debug. Unfortunately, this is not possible with traditional monitoring systems. There are many ways to collect data, and the outcomes can be numerous. The example of the correct use of Knowledge Management to implement a data-driven business model is one key factor to gather quality data from each team.

AIOps with the use of historical data establishes the relationship. It helps to find out how to correlate data coming from various sources without training or the lack of tribal knowledge hidden among people and processes. 

Asset Management 

Data is streamed from assets and the information that is coming from these assets need attention as they are not consistent. Despite configuration management, relationships between assets are not easy to decipher. And, bridging relationships between assets is important to understand the risk and impact of a ticket or incident, and provide further visibility into operations.

This is where asset intelligence takes its place. CloudFabrix AIOps solution can learn this from ingested CMDB data for POCs and live integrations during production implementations. In the event that Enterprises don’t have accurate CMDB or no CMDB, CloudFabrix offers its own asset intelligence module to address the gap.

Diversified Goals

Different stakeholders have different needs. Non-alignment of these needs also increases the duration of the AIOps implementation cycle. This would also result in delivering different outcomes from the AIOps efforts. Your AIOps platform should be able to support the diversity and variety of sources. It needs to be flexible in configuring all those needs.

Unique Machine Learning Model

Each enterprise is different and comes with unique needs. No single machine learning can fit into every enterprise. CloudFabrix comes with a mechanism devised that is easy to build,  experiment, and deploy. It learns from the enterprise’s historic data, the resolution cycle, ticket resolution behavior, and other patterns. This helps in implementing AIOps as a function with the ongoing tasks while fine tuning delays of the project on the go.

Enterprises want to implement POCs with new technologies, but have to wait. The lack of unsupervised ways and models to train, implementation delays occur. Integrations do not have unsupervised ways of learning. The quick way to learn is using historical data without disrupting the existing ITOps processes and at the same time not slowing down the AIOps implementation.

An inclusion of inference models promises that AIOps can deliver immediate value, with analysis of vast IT event datasets for historical and real-time incident analysis. 

Research confirms that 97% of IT organizations agree that AIOps-enabled solutions that deliver actionable insights will help automate and enhance overall IT Operations functions. AIOps tools are preferred for effective Intelligent Alerting, Root Cause Analysis and Threat Detection.

Having said this, AIOps implementation timelines are a function of other human variables too. It depends on the organizational maturity, staff exposure to machine learning, and the right processes in providing required data in a secure manner.