The telecommunications sector in the UK is rapidly evolving and is driven by the integration of artificial intelligence (AI). AI enhances network management, customer service, and operational efficiencies, allowing telecom companies to deliver higher-quality services and better customer experiences.
The deployment of 5G technology is further accelerating this transformation, providing the infrastructure needed for advanced AI applications and Internet of Things (IoT) integration.
By utilizing the Hadoop-based data platform, we build solutions perfectly suitable for telecommunication networks, including technical monitoring systems that respond to events in fractions of seconds.
AI is revolutionising the telecommunications sector in the UK, driving significant advancements in network management, customer service, and operational efficiency. Investing in AI technologies provides telecom companies with a competitive edge, enabling them to innovate and meet the evolving demands of the market. The continued rollout of 5G and the integration of AI will further enhance the capabilities and growth potential of the telecommunications industry.
AI enables telecom companies to optimise network performance through real-time analytics and predictive maintenance, reducing downtime and improving service quality (Race Communications).
AI-driven chatbots and virtual assistants provide 24/7 customer support, efficiently handling inquiries and resolving issues, significantly enhancing customer satisfaction and reducing operational costs (Race Communications).
Automating routine tasks through AI and robotic process automation (RPA) allows telecom companies to reduce operational costs and improve efficiency. For example, AI can automate data entry, billing, and customer account updates (Race Communications) (Grand View Research).
AI provides deep insights into customer behaviour and network usage, allowing telecom companies to make data-driven decisions, offer personalised services, and optimise resource allocation (Grand View Research).
Integrating AI with 5G technology enables telecom companies to scale their operations effectively, meet increasing demand, and support a wide range of IoT applications (Allied Market Research).
Challenge:
The organization needed a strong solution to effectively oversee and manage diverse data sources, encircling Call Detail Records (CDR), technical system logs, billing system information, and financial accounting data. Additionally, they needed to develop real-time reports using CDR data.
Solution:
Empowered by Open-Source technologies, we constructed an Enterprise DataHub, resulting in decreased technical support expenses. This enterprise data management platform assists organizations in securely storing, processing, and analyzing various types of data. Our platform is designed for the collection and analysis of both streaming and archival data. Apache NiFi serves as a critical element, automating the movement, processing, enrichment, and storage of data.
Result:
The Enterprise DataHub facilitated swift, seamless, and secure data import, guaranteeing real-time data tracking. This enabled the customer to enhance their capacity for promptly delivering and processing data as well as analytical resources for their on-site staff, and provide services’ responses nearly instantaneously.
- Automation of data collection about technical state across all parts of network.
- Visualization of data in а flexible/agile manner for different users.
- А single view across the network and related enterprise systems.
Challenge:
Creating a cost-efficient system to oversee the quality of the technical network and mobile communication services.
Solution:
Developing a Big Data platform for nearly real-time collection of log files from six hardware vendors, involving transformation, storage, and presentation of information. Establishing a virtualization layer to tackle challenges in multi-channel data integration and comprehensive representation of data sourced from diverse origins.
Utilizing the gathered parameters, the system conducts over 1 million data processing operations on various objects daily, computes Key Quality Indicators (KQI) and Key Performance Indicators (KPI), and supplies data for web-based dashboards in near-real-time. Additionally, it generates more than 65 reports and prepares showcases for Business Intelligence (BI) systems.
Result:
The solution offers a robust monitoring system that efficiently handles large volumes of data. It computes KQI/KPI, presents near-real-time web-based dashboards, and offers comprehensive reporting, assisting the decision-making processes.
- Faster credit processing, improved data accuracy, and seamless integration for financial institutions.
- Real-time processing of 55,000 files per hour, integration of over 1,000 data objects and cost-effective scalability.
Challenge:
The current system for collecting and analyzing equipment status data wasn’t capable of providing online data processing, resulting in delays of 10-30 minutes between problem registration and service department notification. Additionally, the solution architecture posed limitations on expanding stored data volume without sacrificing processing speed. Furthermore, cross-analysis of data from different sources failed to meet business requirements.
Solution:
The solution involved the creation of a Big Data platform for real-time collection, processing, storage, and visualization of data. The structuring of the visualization layer addressed the challenge of comprehensive representation of data generated and stored across different systems.
Our solution successfully met business requirements:
- Integration of over 1000 data objects into the new system.
- Processing of 55,000 files per hour from various sources.
- Development of more than 65 reports and online dashboards.
- Collection and storage of 1 PB of data annually.
Result:
- Linear scalability of data storage capacities based on Apache Hadoop.
- Cost minimization through the use of Open-Source products.
- Multi-channel integration and preparation of data showcases with Tibco Data Virtualisation.
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