Transforming Telcos: Data Mesh and Agentic AI as Catalysts for Growth

Note: This article primarily focuses on the concepts of Data Mesh. A separate article will cover the details of Agentic AI.

Unlocking Next-Level Growth and Optimization

Telco operators today face dual challenge:

• Managing ever-growing volumes of operational and analytical data
• Meeting strict service-level objectives (SLOs) for innovative business demands.

Traditional monolithic data architectures — such as centralized data lakes — often struggle under the weight of increasing data complexity, siloed ownership, high latency, and limited agility.

In contrast, a modern, distributed data management approach, represented by the Data Mesh paradigm, offers a scalable, self-service foundation that empowers domain teams and supports real-time, context-rich insights. When combined with advanced techniques such as Large Language Models (LLMs) and Agentic AI, Operators can not only optimize their OSS/BSS systems and network performance but also accelerate their transformation into agile, digitally driven enterprises.

A. Rethinking the Data Management Journey

Legacy Challenges

Over time, enterprises, and telecoms continue refining the data management strategies in response to evolving business objectives and market pressures. Traditional approaches have typically involved:

Centralized Monoliths: Data from operational systems (for example, network elements, customer records, or billing systems) is funneled into a single, central data lake. While this can work for batch processing, it often fails to meet near-real-time requirements and slows innovation.

Fragmentation and Siloes: As data volume and diversity increase, disparate teams create their own data models and pipelines. This results in duplicated efforts, lack of transparency in data lineage, and difficulties in ensuring consistent data quality and governance.

Limited Agility: Centralized data teams frequently become bottlenecks. e.g. a data scientist may need raw data for model training, while a network operations team requires near-real-time insights on tower / site performance metrics. A one-size-fits-all solution fails to serve these diverse needs.

The Need for a Unified, Distributed Approach

To overcome these limitations, modern organizations are moving toward an architecture that unifies both operational and analytical data on a common, self-service infrastructure. Such a model needs to be:

Distributed: Decentralizing data ownership by aligning it with business domains.

Self-Service: Empowering domain teams to independently build, manage, and consume data products.

Federated: Implementing a central layer of governance that standardizes policies across domains without sacrificing local autonomy.

This unified approach ensures that every team — from network operations to marketing — can access the precise data they need quickly, while maintaining a holistic view of the enterprise’s data assets.

B. Data Mesh: A Modern, Decentralized Data Architecture

Background and Core Principles

The Data Mesh vision represents a paradigm shift in how organizations manage data. Rather than relying on a monolithic data lake, Data Mesh organizes data around domain boundaries. This approach is built on four foundational principles:

1. Domain-Oriented Decentralized Data Ownership:
Responsibility of data is shifted to the teams that generate it. Each domain (whether it handles network performance, customer interactions, or billing ) needs to be accountable for the full lifecycle of its data. This shall reduce bottlenecks, ensuring that data quality is maintained by those who best understand it.

2. Data as a Product:
Data is treated as a strategic product that must be discoverable, addressable, understandable, and trustworthy. Each data product is defined as an autonomous unit that encapsulates:

• Code: The data pipelines and APIs needed to process and serve the data.
• Data and Metadata: The actual datasets, enriched with metadata such as quality metrics and semantic definitions.
• Infrastructure Dependencies: The storage and compute resources required for processing.

3. Self-Serve Data Infrastructure as a Platform:
A modern, self-service data platform provides domain teams with the tools and services they need to ingest, transform, store, and share their data autonomously. By abstracting the complexity of data provisioning and management, the platform enables rapid innovation and reduces reliance on centralized IT support.

4. Federated Computational Governance:
In a decentralized ecosystem, consistent oversight is critical. Federated governance combines global standards with local autonomy by enforcing policies — such as data security, quality, and compliance — through automated, code-driven processes. This model ensures that even as individual domains operate independently, they adhere to a common set of rules, ensuring trust across the organization.

These principles establish a flexible, scalable architecture that is particularly well-suited to the diverse data needs of modern telecom operators.

C. The Rise of LLMs and Agentic AI in a Data Mesh Environment

Enabling Contextual Intelligence

Large Language Models (LLMs) have revolutionized the way organizations interpret data. Unlike traditional analytics tools that process data in rigid, token-based formats, LLMs can capture the nuance and context inherent in vast, unstructured datasets. This capability is essential for telecom operators, where understanding subtle patterns in customer behavior, network performance, and operational anomalies can drive significant improvements.

Advancing Autonomous Decision-Making with Agentic AI

Building on the strengths of LLMs, Agentic AI systems go further by providing:

Adaptive Self-Supervised Learning: Continuously learning from real-time inputs to improve predictive accuracy.

Proactive, Autonomous Actions: Automatically adjusting network configurations, rebalancing workloads, or flagging issues before they impact service.

Seamless Integration: Consuming high-quality, domain-specific data from the Data Mesh to drive operational efficiency.

Together, LLMs and Agentic AI can empower telecom operators to respond rapidly to emerging challenges , whether it’s mitigating network congestion in a 5G environment or dynamically optimizing OSS/BSS business processes.

D. Leveraging both worlds

A Unified model for the techco

By merging the principles of Data Mesh with the adaptive capabilities of LLMs and Agentic AI, telcos can create a highly responsive data ecosystem. This shall offers several key benefits:

Real-Time, Domain-Driven Insights: Decentralized data ownership ensures that every business domain produces and refines data that is immediately useful to both internal and cross-domain consumers.

Autonomous Optimization: Agentic AI systems leverage the rich, contextual data products to make proactive adjustments across networks and operational systems.

Seamless Interoperability: Federated governance and self-service infrastructure facilitate the smooth exchange and integration of data across domains, leading to faster time-to-insight and innovation.

Scalable Operations: Distributed data management minimizes bottlenecks and allows each domain to scale independently while adhering to universal standards for quality, security, and compliance.

Mitigating Risk : As GenAI introduces new data risks, including sensitive data leakage through LLMs. Data mesh helps mitigate these risks by providing transparent results into what data was sourced to produce an output from a model, reducing model risk and identifying how information was sourced.

5. Conclusion

The transformation from a traditional, monolithic data architecture to a modern, decentralized Data Mesh is a critical evolution for telcos seeking to stay competitive in a rapidly changing market. By embracing the core principles of domain-driven data ownership, data as a product, self-serve infrastructure, and federated computational governance, organizations can create an ecosystem where data flows freely, insights are generated in real time, and operational agility is dramatically enhanced.

Coupling this foundation with the advanced capabilities of LLMs and Agentic AI enables telecom operators to not only optimize network performance and streamline operations but also drive strategic innovation.

This integrated, vendor-neutral approach empowers every domain to contribute to a unified, intelligent data infrastructure — paving the way for a smooth transition from a traditional telco to a dynamic, future-ready techco.

References:
https://www.oreilly.com/library/view/data-mesh
https://martinfowler.com/articles/data-mesh-principles.html
https://www.snaplogic.com/glossary/data-mesh
https://www.getdbt.com/blog/key-components-of-data-mesh-federated-computational-governance
https://arxiv.org/abs/2304.01062