Artificial Intelligence (AI) is no longer a futuristic concept in the telecommunications sector - it is the driving force behind the industry’s transformation. AI is revolutionising how telcos operate and deliver value, from predictive maintenance and automated service delivery to enabling innovations like IoT services and smart cities.

However, this revolution hinges on one critical factor: data. More specifically, the quality, governance and trustworthiness of the data that powers AI models.

In the AI era, data engineering has become the unsung hero of telco transformation. It is the foundation upon which reliable, transparent and explainable AI is built. As telecommunications organisations accelerate their AI journeys, the need for robust data engineering practices has never been more vital.

Without them, AI applications risk delivering inaccurate predictions, inefficient operations, and even regulatory violations, potentially eroding profitability and customer trust.

The consequences of poor data quality are already being felt. Research shows that bad data costs organisations over £10 million annually on average due to operational inefficiencies, flawed analytics and poor decision-making.    

In the context of AI, these costs can escalate quickly. Unlike traditional IT systems, AI models do not just process data—they learn from it. If the training data is incomplete, inconsistent, or biased, the resulting models will replicate and amplify those issues in real-time.

This is especially concerning in telecoms, where AI makes complex decisions across sprawling networks. Whether it’s reallocating bandwidth, identifying service disruptions, or targeting customers with personalised offers, the risks of AI misjudgement are significant.

In such a high-stakes environment, trusted data is not a luxury - it’s a necessity.

5G has exponentially increased the volume, velocity, and variety of data that telecoms must handle.

From mobile devices and base stations to connected cars and smart sensors, the infrastructure generates petabytes of data daily. This explosion of data brings opportunities for AI to enhance performance, but it also introduces new engineering