Organizations continue to provide sub-optimal experiences for their digital services because, despite an abundance of monitoring, they can’t autonomously determine the root cause of a service-impacting issue.  And, if the root cause can’t be determined without significant human intervention, then organizations continue to miss opportunities to automate remediation and prevent issues from having a detrimental impact on their customers’ experiences.

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 service organization have potential upstream or downstream impacts on the customer experience. But without visibility to the entire service delivery ecosystem, customer experience impacts are often not understood triggering service incidents that could have been avoided or quickly contained.

What’s needed is to understand the complete customer impact chain through the implementation of a wide-scope AIOps application.  This gives you—

• Cross-boundary views of the issues that matter most

Operational data is often held in “silos,” applications, and databases that belong to different groups. Tools are used to monitor various systems but there’s no simple mechanism for aggregating and cross-correlating all of the data in real-time to understand the cause and effect relationships.

Ecosystem observability enables the monitoring of all signals across all service layers including customer experience.

• Quicker root cause analysis, identification and issue resolution

Monitoring tools generate lots of alarms and create many false positives. The amount of noise created often causes key signals to be ignored while red herrings are chased. Enabling all of the available data to be analyzed together using AI and ML, the cause can be distinguished from the symptoms, the issues resolved more rapidly, and operating cost reduced with more efficient and effective service operations management. Understanding the cause of the problem, the customer segments impacted, and under what conditions along with the ability to relay all of this information to the right fixer group are the key to improving operational effectiveness.

• Advance warnings of emerging performance and health problems 

By comparing new service events to baselines, patterns across the service delivery ecosystem can be detected and acted upon before they become problematic and impact the customer experience.

• A foundation for Change Assurance  

What-if analysis can be implemented and the business and customer impact of change with the deployment of new technology, software upgrade, or process change can be tested prior to a wide-spread rollout to reduce risk.

If It Were Easy
“If it were easy, everybody would be doing it,” goes the saying. The same holds true for delivering optimal customer experience and service performance.  What’s required is a wide-scope AIOps platform that provides for three key capabilities.

Ecosystem observability

Learns and observes how all the interrelated systems impact the customer experience, monitors what’s happening on the application layer, the network layer, and the infrastructure layer, and detects changes automatically.

Explanatory AI and ML

Delivers the advanced analytics and machine learning to accurately detect anomalies and determine what is the cause, what is symptomatic, and what customer populations are impacted with visual explanations of all analysis and actions.

Experience and service assurance

Remedial actions can be automated and customer-affecting issues can be predicted and acted upon prior to customer impact.

To process and interpret multiple data streams, ecosystem observability begins with analytics that can ingest data at ultra-high speeds, then create time-series models for interpretation. Time-series modeling, which charts data events over time, is simple in concept but not in operation because it requires, in addition to highly sophisticated modeling algorithms, an ability to create and access “big-data” contextual models. So, in addition to raw processing speed, the analytics engine needs to be capable of scaling exponentially.

The analytics will then compare results with baseline values, find commonalities, predict future states, and then turn that information into visual displays for use.

When a service issue arises, an effective AIOps Application will identify the issue no matter where it lies and prevents multiple IT teams in different areas working on the same problem and creating multiple service tickets. An effective AIOps application should be able to identify the cause, the symptoms, areas of impact, and potential resolution creating one ticket with all affected areas notified of the issue, root cause, and automated resolution implemented.

AIOps: The Best First Step
The question for many is where best to start. The best answer, the answer with the best future, is in understanding the complete customer impact chain, leveraging analytics, AI and ML to resolve more quickly, and automate wherever possible to improve operational efficiency with a wide-scope AIOps application.