Modern data structures: enabling high-impact AI-driven telecom operations

Modern data structures: enabling high-impact AI-driven telecom operations

Telecom operators today must navigate increasing data complexity driven by AI, 5G, and IoT. Traditional, siloed data architectures fail to meet the real-time processing and scalability requirements for modern networks. To remain competitive, operators need innovative data management approaches that enable automation, accuracy, and measurable operational impact.

Data mesh and data fabric are emerging as foundational concepts for building AI-driven telecom operations. These architectures shift the focus from centralized systems to domain-oriented, AI-ready data pipelines, offering efficiency and scalability.

Why modern data structures matter

The principles of Data Mesh—a concept pioneered by Zhamak Dehghani—have revolutionized how enterprises view and utilize data. Telecom operators can benefit from:

• Decentralized ownership: data is treated as a product, enabling business domains (e.g., RAN, transport, core) to manage and share their data independently.
• Improved accessibility: cross-domain data becomes reusable, reducing silos and redundancy.
• Scalability: decentralized architectures are inherently scalable and can support the massive data volumes telecom operations require.

Similarly, data fabric integrates data across legacy systems, clouds, and edge networks, providing a unified and virtualized framework to enable AI, digital twins, and predictive modeling.

Real-world impact

By transitioning to modern data structures, operators can achieve tangible, measurable results:

• Cross-domain insights: unified data pipelines deliver real-time, high-accuracy insights for investment prioritization.
• Operational efficiency: cost optimization of millions within months through AI-enabled data architectures.
• AI-readiness: real-time data ingestion and processing to drive predictive maintenance and network performance modeling

These results align with ongoing work within the TM Forum Modern Data Architecture group, which emphasizes:

• Distributed and scalable architectures for AI.
• FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
• Real-time delivery for autonomous network operations.

TM Forum framework alignment

Modern data architectures complement key TM Forum frameworks:

• Open Digital Architecture (ODA): flexible integration across network domains and legacy systems.
• Digital Twins for Decision Intelligence (DT4DI): data-driven decision-making through digital twins and AI.

These frameworks provide telecom operators with a structured path to achieving AI-native operations that are scalable, transparent, and future-proof.

Conclusion

Modern data structures—data mesh and data fabric—are the key enablers for AI-driven telecom transformation. By treating data as a product, embracing decentralization, and ensuring interoperability, telecom operators can unlock:

• Cost efficiencies,
• Faster decision-making,
• High-accuracy AI insights.

To achieve measurable financial and operational impact, the telecom industry must align with modern architectural principles and embrace next-generation frameworks for data management.