How Data Engineering is Powering
Trusted AI in Telecoms
This level of transparency builds confidence in AI-driven decisions and enables organisations to demonstrate accountability, which is a growing expectation from both customers and regulators.
However, if the data feeding these systems is noisy, duplicated, or outdated, the predictions become flawed, leading to costly errors and degraded performance. Data engineers are on the frontlines of solving these challenges. They are tasked with designing pipelines that clean, standardise, and integrate diverse data sources while ensuring data freshness and availability for downstream AI models. The accuracy and effectiveness of AI are directly tied to the rigour and sophistication of these pipelines.
The importance of data governance
One of the most pressing data engineering concerns related to AI in the telecom industry is governance. Companies handle highly sensitive customer and network data, making compliance with privacy regulations such as GDPR and the UK’s Digital.
This starts with data lineage, which includes understanding where data comes from, how it has been transformed, and who has accessed it. Advanced metadata management platforms are increasingly being deployed to provide this visibility, enabling organisations to track data across its entire lifecycle.
By maintaining data integrity and enforcing access controls, telcos can reduce the risk of unauthorised use, bias, or data leakage.
In AI workflows, this governance must extend to the model level. Data engineers and data scientists must work together to document the data used to train models and set foundations for easy monitoring to catch model degradations for timely refreshing/retraining.
This level of transparency builds confidence in AI-driven decisions and enables organisations to demonstrate accountability, which is a growing expectation from both customers and regulators.
The process of automating the data supply chain
AI is now being used to improve data engineering processes. Traditional data processing methods cannot cope with the complexity of today’s telecom environments since manual cleaning, integration, and transformation are not only time-consuming but also error-prone.
Machine learning algorithms can be deployed at various stages of the data pipeline to detect anomalies, fill in missing values and standardise formats - automatically and at scale. This ensures that only high-quality data reaches AI models, reducing drift and improving long-term model performance.
Automation also enhances agility, allowing telcos to adapt quickly to changes in data sources, formats, or business requirements.
Real-time data ingestion tools are further transforming the telco landscape. Using streaming architectures, telcos can feed live data into AI models, enabling split-second decisions that optimise customer experience and network performance.
However, without proper validation and monitoring, real-time pipelines can introduce their own risks. Therefore, automation must be combined with continuous testing and observability, allowing engineers to catch and correct issues before propagating.
