Telco operators provide a range of telecommunication services that require extensive networks. However, these networks are also sources of important data that need to be efficiently analyzed and managed. Much like other industries, the Telco industry too must do its share of troubleshooting to manage networks and complex IT infrastructures. This has been an important factor in the shift to cloud-native architectures and the use of cloud enablement services. These systems improve scalability and agility and allow faster developments. But this also means that Telco operators now must analyze various components. Troubleshooting for such components within the Telco systems can be done through closed-loop automation. The implementation of closed-loop automation allows quick analysis and troubleshooting of network issues so that Telco customers do not have to deal with communication issues.

Understanding Closed-Loop Automation

Closed-loop automation is AI-driven automation that helps to create efficient networks through the following solutions:

• Detection of anomalies
• Determination of anomaly resolution techniques
• Implementation of changes to transform existing networks

In current times, companies must deal with huge volumes of data that are sourced from various locations. This means that all the data needs to be collected, stored, and analyzed. If the data analysis is accurate, the insights generated can assist in prompt troubleshooting and prediction of issues before they cause a system-wide shut down or interference. Thus, the focus for companies across industries remains on IT automation with AI. closed-loop automation is primarily used for anomaly detection. Through the implementation of closed-loop automation, it is possible to detect and resolve issues that may occur in the future.

When businesses use closed-loop automation for IT operations, predictive models are created. Data is then categorized according to these models and analyzed. The insights determine what changes are necessary to create optimized and efficient networks. The recommendations are made based on the predictions of closed-loop automation models. These are then applied to the orchestration level and implemented in the network as actual changes in the system.

When closed-loop automation is used to resolve issues, the predictive insights are combined with solutions provided by specific AI systems. Usually, the AI systems used are a part of robotic automation that help to automate processes related to the detection and resolution of anomalies. In this case, closed-loop automation works for complex processes where a combination of predictive analytics and AI solutions is important for the orchestration to occur automatically and efficiently.

closed-loop automation is also necessary to create intelligent alerts. In many systems, several components may raise alerts for the same anomaly, and this can get difficult to understand and manage. This usually occurs when there is a failure event within general operations. When multiple alerts are raised for the same issue, it increases the load on the operations teams and can also cause glitches within certain business processes. closed-loop automation through machine learning algorithms and models helps to create a series of alerts. These alerts are specific to the causes or effects, and therefore, it becomes easier for operations teams to detect and resolve the failure event.

Closed-loop Automation for Telco Operators

Telco operators work with huge volumes of data that keep growing and need to be analyzed. This leads to a vast IT infrastructure in a company and therefore, there will be multiple networks that are reliant on the information contained within the infrastructure. This is where closed-loop automation comes in. While Telco operators use IT infrastructure managed services, automation of networks is necessary because each operator deals with numerous communications services. If the networks are optimized and automated, there will be less scope for anomalies. Thus, the services provided to Telco customers will not glitch or be disrupted.

Predictive planning and root cause analysis are two applications of closed-loop automation that are of value to Telco companies. Network health is crucial, especially so in the case of Telco operations, and predictive planning through machine learning algorithms can help. Implementation of machine learning through closed-loop automation helps to assess factors like seasonality which can then provide insights to predict the future conditions of the networks. Corrective actions can also be taken based upon these predictive analytics and operations teams can plan how to implement changes within the networks for better outcomes. Anomalies too can occur within the communication channels and Telco operators need to resolve them quickly so that clients do not have to deal with issues in the services. Here, closed-loop automation can be used for root cause analysis. Through effective closed-loop automation, Telco operators can automate certain processes that will monitor patterns and network behaviors. This will help determine specific causes of anomalies. Usually, historical, and current data is analyzed for insights that can determine what is resulting in the anomalies. Once the root causes are determined, Telco operators can implement solutions for the elimination and resolution of these issues. closed-loop automation does not only leverage data for anomaly detection and analysis but also helps to determine the accuracy of the same.

Telco operators are evolving with client needs and currently, the global network standard is 5G. The deployment of 5G can assist in various aspects of telecommunications, including machine connectivity, network slicing, availability of flexible bandwidth, and ultra-low latencies. However, for seamless 5G connections, Telco operators would need to effectively manage several networks and cell sites. This can get quite challenging and therefore, it is necessary to implement closed-loop automation. closed-loop automation will help in the provisioning of multiple network devices at once. This will be done through consistent and dynamic configuration of the devices across systems. Analysis after closed-loop automation will also help to detect issues within these devices and determine if there are any mission-critical functions that need immediate attention.

Conclusion

The role of closed-loop automation does not stop with anomaly detection and determination of network health. For Telco operators, closed-loop automation can also be a part of cybersecurity and compliance. Anomaly detection does help in strengthening cybersecurity features and Telco companies do invest in cyber security and compliance services. Therefore, closed-loop automation can assist in ensuring safe networks and help regulate compliance functions. This allows Telco operators to provide better services and thus, receive better revenue.