Industry: Technology

  • Transforming Telefonica's business operations with cloud modernization and data transformation.
  • Substantial IT cost savings.
  • Establishing a robust foundation for advanced analytics and AI/ML capabilities.

Benefits & Results

  • Significant IT cost savings and improved operational efficiency.
  • Enhanced data-driven capabilities and faster time to market for analytics solutions.
  • Strong foundation for future advanced analytics and AI-driven strategic business growth.
  • Transitioned from a CapEx to an OpEx model, significantly lowering infrastructure and maintenance costs.
  • Improved scalability and versatility of data operations with cloud-enabled infrastructure.

Background

Telefónica is a global telecommunications company headquartered in Madrid, Spain, established in 1924. It operates key brands such as Movistar, O2, and Vivo, providing extensive mobile, broadband, and digital television services. Telefónica is committed to digital transformation, focusing on leveraging advanced data analytics and AI to enhance customer experience and streamline operations across Europe and the Americas.

Challenges

  • On-premise Hadoop Cluster with limited capabilities restricting data processing and storage scalability.
  • High licensing costs with Teradata Data Warehouse.
  • Limited analytics tools and challenges in deploying machine learning models.

Solution

NowVertical implemented a comprehensive cloud modernization solution using GCP. This included migrating data from legacy platforms to a scalable cloud-based Data Lake, transforming data operations with serverless services, and establishing scalable and customer-centric data models. The solution also incorporated frameworks for advanced analytics, AI/ML storage optimization, and best practices in DataOps and MLOps.

Implementation

  • Data Lake Startup: Built a cloud-based Data Lake on GCP for ingesting structured, semi-structured, and unstructured data from multiple sources.
  • Data Lake Accelerator: Designed a framework to optimize data engineering operations and implemented a serverless Data Lake framework.
  • Machine Learning Industrialization: Developed a framework for operationalizing and producing Advanced Analytics models, applying MLOps best practices to streamline model production and maintenance.
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