How Data Engineering is Powering
Trusted AI in Telecoms

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AI will become more embedded, more autonomous and more central to decision-making.

The role of synthetic data

Privacy remains a significant challenge in AI for telcos, especially when using customer data for model training. Organisations are under pressure to protect personally identifiable information (PII) while still gaining insights that drive innovation. One solution that is gaining momentum is synthetic data.

Synthetic data is artificially generated but statistically representative of real-world datasets. It allows organisations to train, test and optimise AI models without exposing sensitive or regulated data.

In telecoms, synthetic data can be used to simulate rare network failures, test new routing algorithms, or prototype customer journeys—all in a controlled and compliant environment. This approach enhances privacy and model robustness.

Real-world data is often imbalanced or incomplete, which can bias AI outcomes. Synthetic data allows engineers to create balanced datasets that reflect a wide range of scenarios, leading to fairer and more resilient AI systems.

Embracing an integrated approach and breaking down silos

Advanced tooling and automation alone won’t guarantee AI success - people and culture matter just as much. Telcos must foster a culture of cross-functional collaboration between data engineers, data scientists and domain experts.

Each brings a unique perspective: engineers ensure data quality and infrastructure, scientists design and train models, and business stakeholders provide context and objectives.

Successful AI initiatives start with shared understanding. This means establishing common data definitions, co-developing governance frameworks, and leveraging collaborative platforms where teams can collaborate on datasets, models, and metrics.

Data engineers should be involved early in the AI development lifecycle, not just at the implementation stage, so they can help shape data strategies that support the end goals. Organisations that embrace this integrated approach are better positioned to deliver AI that is not only innovative but also reliable and sustainable.

Entering a new era of AI-ready data engineering

As telcos continue to evolve into digital service providers, the demands on their data infrastructure will only increase.

AI will become more embedded, more autonomous and more central to decision-making. Data engineering must also advance to support this evolution - becoming more intelligent, automated, and aligned with AI requirements.

This means adopting new ways of organising and managing data that make it easier to grow and adapt as needs change. It also involves applying smarter, more efficient ways of working to speed up the development and rollout of AI tools. All this will support innovation, ultimately enabling teams to experiment with data and AI to solve new challenges or improve business processes.

Just as importantly, it’s about giving data teams the right tools to work more independently, monitor how data is used and maintain strong oversight to ensure everything runs smoothly and responsibly.

Ensuring responsible use of AI

At its core, the telecoms industry’s mission is simple: to ensure that every AI-driven decision is grounded in accurate, explainable and trusted data.

Data engineering is not just a technical discipline. It is a strategic enabler of trustworthy and responsible AI. By prioritising data quality, governance and automation, telcos can build the infrastructure required for the next generation of intelligent networks.

In doing so, they will not only optimise operations and increase customer satisfaction but also build a resilient, innovative and future-ready business.