Implementing a data lakehouse for a leading telecom provider

Summary

A leading North American telecom company with over 21 million no-contract prepaid subscribers faced operational inefficiencies due to fragmented systems and poor data integration. Marlabs addressed these challenges by implementing a comprehensive data lakehouse on AWS, enabling real-time analytics, streamlined data management, and enhanced customer insights.

Challenge

The client’s infrastructure was hindered by a fragmented and outdated data environment. Customer records were disjointed across multiple platforms, with data integrations often delayed due to reliance on various vendors. This led to several critical issues: a lack of visibility into customer activity, ineffective loyalty programs resulting in high churn rates, and frequent device stockouts due to poor supply chain intelligence. These operational inefficiencies were compounded by an absence of real-time data access, ultimately limiting the client's ability to make timely, informed decisions.

Solution

Design and architecture

The project began with establishing a solid foundation for the lakehouse by designing and architecting the AWS-based solution. This phase ensured scalability, reusability, and alignment with the client’s long-term strategic goals. The team developed a reusable ingestion framework using PySpark and Python, allowing for efficient and consistent data flow into the new system. The architectural design also emphasized security, performance, and maintainability.

Real-time data integration

With the foundation in place, the next phase focused on enabling real-time data ingestion and integration. The team implemented solutions to consolidate data from various subscription platforms, creating a unified view of customer behavior. Technologies such as Kafka and Kinesis were deployed to support real-time workloads, which enabled accurate and immediate tracking of customer activity. This led to the creation of a comprehensive 360° customer view, empowering the client with better visibility into usage patterns and service engagement.

Data management and compliance

In the third phase, attention shifted to enhancing governance and compliance practices. The team provided training for data stewards and business users to foster better data literacy and accountability. Change management initiatives were introduced to support organizational adoption, while ongoing data quality monitoring ensured sustained reliability. This phase also included the finalization of master data management (MDM) governance policies, aligning the data practices with regulatory standards and internal policies.

Continuous improvement & flexibility

The final phase introduced mechanisms for continuous improvement and future adaptability. Self-service analytics capabilities were rolled out to leadership, enabling more agile and informed decision-making. The system was configured to allow for the seamless addition of new data sources, ensuring the infrastructure could evolve with changing business needs. This forward-thinking design not only made the system more resilient but also more accessible and user-friendly across the organization.

Results

The implementation of the lakehouse delivered measurable improvements to the client’s operations. Real-time data processing eliminated previous bottlenecks and improved responsiveness. Customer insights became more actionable, loyalty initiatives more strategic, and inventory management more precise. These operational gains translated to improved customer retention, enhanced decision-making, and stronger alignment between business units and technology infrastructure.

Impact

• Improved real-time visibility into customer activity and behavior
• Reduced customer churn through better-targeted loyalty programs
• Enabled leadership with self-service tools for data-driven decisions
• Increased agility in adding new data sources and adapting to change
• Strengthened compliance and governance across the organization
• Optimized inventory management and reduced stockouts through better forecasting

This lakehouse initiative demonstrates how a robust, scalable data strategy can transform operational efficiency and strategic decision-making in the telecom industry.