Trusted Data & Governance for AI
Artificial Intelligence (AI) has transitioned from a futuristic concept into a fundamental element driving transformation in the telecommunications sector. It is revolutionizing operations and delivery of value, enabling innovations such as IoT services and smart cities through capabilities like predictive maintenance and automated service delivery.
However, the success of this revolution is contingent upon a critical factor: data quality, governance, and trustworthiness. In the era of AI, data engineering has emerged as the unsung hero of telco transformation, forming the bedrock upon which reliable, transparent, and explainable AI is constructed.
As telecommunications organizations accelerate their AI journeys, the importance of robust data engineering practices has never been more vital. Without these practices, AI applications may produce inaccurate predictions, lead to inefficient operations, and even result in regulatory violations, all of which can undermine profitability and diminish customer trust.
The Cost of Poor Data Quality
The repercussions of inadequate data quality are already manifesting across the industry. Research indicates that poor data costs organizations over £10 million annually on average, stemming from operational inefficiencies, flawed analytics, and poor decision-making.
In the context of AI, these costs can escalate dramatically. Unlike traditional IT systems, AI models do not merely process data—they learn from it. If the training data is incomplete, inconsistent, or biased, the resulting models will replicate and amplify these issues in real-time.
This becomes particularly concerning in the telecom sector, where AI is tasked with making complex decisions across extensive networks. Be it reallocating bandwidth, identifying service disruptions, or targeting customers with personalized offers, the risks associated with AI misjudgments are significant.
In such a high-stakes environment, trusted data is not merely advantageous—it is essential.
The Impact of the Rollout of 5G
With the advent of 5G, the volume, velocity, and variety of data that telecom companies must manage have increased exponentially. From mobile devices and base stations to connected cars and smart sensors, the infrastructure generates petabytes of data daily.
This explosion of data presents opportunities for AI to enhance operational performance. However, it also introduces new engineering challenges that must be addressed to harness the full potential of AI in telecommunications.
