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This paper presents an innovative framework that aims to adapt data management within the telecommunication sector in the rapidly evolving AI era. It deals with the challenge of managing large amounts of data by a group of companies consisting of various subsidiaries, systems, and data lakes. A modular open-source approach is proposed to enhance data monetization, interoperability, trading, and exchange, weighing the demands of the telecommunication industry against the services offered by the framework. The article discusses three prevalent use case scenarios that are mainly encountered in Business-As-Usual working activities: OLAP queries that are predominantly set up in data warehouses, visual and strategic analysis discovery for planning and decision-making, and OLTP processes for daily scanning and monitoring of products, solutions, and resources. Finally, it charts a transformation deployment plan that aligns the existing data architecture with the DATAMITE project’s vision, moving away from the traditional two-tier model towards a much more flexible, data product-focused design.

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Transforming Data Management in Telecommunications: The Case Study of OTE Group’s Integration with DATAMITE Project

  • 会议论文

  • 发表日期：

    2025年

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  • Achilleas Marinakis,
  • Sotiria Petrova,
  • Efstathia Deligeorgi,
  • Christos A. Gizelis,
  • Michalis Kefalogiannis,
  • Vrettos Moulos,
  • Vasileios Siopidis,
  • Nikolaos Tepelidis,
  • Konstantinos Votis,
  • Dimitrios Tzovaras

摘要

This paper presents an innovative framework that aims to adapt data management within the telecommunication sector in the rapidly evolving AI era. It deals with the challenge of managing large amounts of data by a group of companies consisting of various subsidiaries, systems, and data lakes. A modular open-source approach is proposed to enhance data monetization, interoperability, trading, and exchange, weighing the demands of the telecommunication industry against the services offered by the framework. The article discusses three prevalent use case scenarios that are mainly encountered in Business-As-Usual working activities: OLAP queries that are predominantly set up in data warehouses, visual and strategic analysis discovery for planning and decision-making, and OLTP processes for daily scanning and monitoring of products, solutions, and resources. Finally, it charts a transformation deployment plan that aligns the existing data architecture with the DATAMITE project’s vision, moving away from the traditional two-tier model towards a much more flexible, data product-focused design.