Six Quick Wins For Successful AIOps Implementation

by | Jun 6, 2024 | 1 comment

Problem: Digital transformation is a long and winding road to – where, exactly? You’ve got data coming from everywhere today – from machines, networks, cars, and tractors. You’ve also got data coming from your company’s operations, from sensors, switches, firewalls, and log files. It’s easy to become bewildered by the possibilities.

Solution: Act now. Look for the data that counts the most, that’s most usable right away, that you can act upon. Find some quick wins – some successes – to build confidence, optimize operational efficiency, and improve your customers’ experience.

As consumer expectations for faster, more reliable, and personalized services rise, network service providers must move away from focusing solely on traditional performance measures and look to capabilities such as wide-scope AIOps to offer a fuller picture of how operations affect the customer experience.

One – Get Visible
Seeing is believing, so shine a light on those operations you’re upgrading or those that are disconnected by incompatible processes or technologies. Use real-time analytics to build visibility across operational silos. Use customizable dashboards to see where operations connect to customers. Make the most of incoming data to take fast action when there’s a problem. An AIOps application that learns continuously can fine-tune parameters and improve service assurance and customer experience.

Two – Find Needles In Haystacks
Terabytes of new data create lots of noise – too much for humans to sift through to find problems. Many AIOps platforms leverage advanced anomaly detection, AI and ML to add context to incoming data streams and cross-correlate alarms or signals to not only reduce alarm noise but to distinguish between symptoms and the cause of the problem. Explanatory AI can also provide a visual and written explanation for what was discovered so that action can be taken quickly and the issues resolved.

Three – Reduce Risk, Accelerate Resolution with More Effective Change Assurance
Know exactly what populations may be affected by an operational and infrastructure change or a service quality issue. Service delivery systems have become increasingly complex with a lot of moving and interconnected parts. Daily changes occur within the service ecosystem. Software upgrades, infrastructure modifications, or other changes implemented by the services organization have potential upstream or downstream impacts on the customer experience.

AIOps can reduce business risk by identifying if customers are adversely impacted in any way by planned changes. Customer populations that would be affected by the change and how they would be affected can be identified before widespread rollout. In the case of service incidents, quickly identifying impacted customer populations allows you to notify these customers that you are aware of the problem and the fix is in process. This reduces service calls and delivers confidence to customers that you’re on top of the situation.

Four – Dig Deeper with Autonomous Operations
Anomalies become incidents when they’re triggered by multiple, parallel events. The more that machines and applications talk to one another – a mark of digital transformation – the harder it becomes for humans to identify problematic interactions. Automated incident management, performed by real-time analytics, is able to sort through the clutter to discover multiple overlapping failure patterns.

AI and ML capability within AIOps also support the execution of automated closed-loop processes where remedial action can be automated or prescriptive remedial actions presented to the resolver group to improve operational efficiency.

Five – Predict The Future
You may not want to see the mountains of data coming in, but your real-time AIOps analytics engine does. You may not want to wade through company data lakes, but the real time analytics engine will. All this data creates context. And context – served up via machine learning models – is where analytics and machine learning can predict issues and enable pre-emptive action to be taken. Take NSPs for example, it’s one thing for them to identify one part of the network infrastructure that needs to be fixed. It’s another to anticipate problems in the network and understand the impact it has on revenue-generating operations to properly plan and prioritize the proactive action. It’s about being predictive and making dynamic changes before failure occurs with improved change assurance processes.

Six – Bring The Quick
AIOps and advanced real time analytics can help you make the most of your important data and help you ignore the not-so-important. But you want to get started now to build some early successes. The way to do that is to leverage an AIOps analytics application that can automatically learn your ecosystem ontology. Learning the interdependencies that exist between your processes and the impact component parts of the system related to the customer experience. This allows you to get started quickly and prioritize the areas where transformational change will have the greatest impact.


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