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Telecom Business Review | Tuesday, July 27, 2021
Managing this data can help businesses set up and optimize the appropriate infrastructure and devices to sell the proper products or services at the right time.
FREMONT, CA: The telecom industry handles numerous data sources, ranging from network design and operations to a vast array of customer information. Businesses must manage this data proactively to set up and optimize the appropriate infrastructure and devices and sell the appropriate products or services at the proper time. Machine learning (ML) and artificial intelligence (AI) provide cost-effective solutions to this problem, and AI use cases in telecom are proliferating.
AI in the telecom industry is not just a fad; it is already altering how we interact with the world and with one another, enabling interactions with people, services, and devices. Innovative telecommunications companies are leveraging the power of ML and AI to solve the unique challenges of the networking sector, in particular by leveraging the ability to process petabytes of data in real time at lower costs than ever before—creating unique revenue opportunities within the telecom industry.
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Predictive Maintenance: Businesses can train an ML algorithm to detect faults in network equipment, such as switches and routers, and predict when they will occur with a few lines of code. When a fault is detected, an alert can be generated automatically. This allows engineers to focus on problem-solving rather than logging alerts and submitting service tickets and support requests.
They can also use an AI system to trigger predictive maintenance operations at specific supply chain nodes. For instance, as new routers enter their data center, they could train an algorithm to detect when one stops sending traffic or has downtime exceeding normal operating levels. Thus, they can determine whether routine maintenance is required or whether something more serious, such as hardware failure, requires further investigation. Predictive analytics to automate some basic maintenance tasks can reduce unscheduled downtime and improve operational efficiency, thereby reducing operational costs and increasing operational efficiency.
Network Optimization: Integrating network management systems with increasingly intelligent automation systems that make more informed decisions about route traffic, design bandwidth, and improve security will significantly impact how networks are constructed and maintained. For instance, the Cisco DevNet AI in the Networking hub provides a collection of best practices for network optimization that promises to use AI to make future networks more flexible and resilient.
This goes beyond standard quality-of-service monitoring; it aims to optimize both bandwidth utilization (as is currently done) and energy consumption. It suggests using deep learning models that classify traffic faster over time without requiring manual updates. It can also accurately predict network anomalies by listening for abnormal network activity, such as unexpected traffic spikes or dips, which can be flagged for further investigation.
Customer Retention Forecast: Predicting customer churn in the B2C market is a typical telecom use case. Imagine if, as a B2B company, businesses could predict the customer churn rate before it occurred. Using a model based on ML, telecom companies can predict when a customer or subscriber is likely to churn. Then, they can take measures to decrease their churn rate.
For instance, they may proactively reach out to customers to assist them in resolving a problem that could cause them to leave. The more precise predictions and responses, businesses will experience fewer customer losses. Companies will also create happier customers who are more loyal.
Automatic Service Configuration: One application of AI is the automatic configuration of services. Telecoms can achieve this by deploying ML algorithms as a bridge between their network management and other systems. The model would rapidly determine the optimal parameter values for a given application or customer and then automatically adjust those values as needed. In addition to being trained on data from the previous usage, such a network automation system can learn from new experiences by observing the behavior of actual customers and their customers.
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