Communication Service Providers (CSPs) have always been meticulous about their data. Historically, even before the hyperscalers made data accumulation, management, and analytics a central pillar of their strategy, CSPs were amassing vast quantities of data. Reason: Every voice and video call, text message, broadband connection, product, network element, and customer interaction generates an enormous amount of data. Telecom operators were among the first to utilize their data for operational efficiency, customer satisfaction, regulatory compliance, and fraud detection. They mastered the art of transforming data into information, with a primary focus on reporting and decision making. However, today they face a conundrum: they have abundant access to data, but are unable to use artificial intelligence (AI) to transform it into actionable intelligence (for a detailed look at the challenge, download Wipro’s State of Data4AI Report 2025).
The handicap is not new. Over the last decade or so, data lakes established by CSPs have steadily turned into data swamps, posing a significant barrier to effective data utilization and undermining the potential value of data-driven decision-making. Moreover, this has created “data technical debt” rather than monetizing data.
As the variety, volume, and complexity of data increased, legacy systems struggled to keep pace, resulting in compromised data quality and inadequate governance. Investment in Big Data technology and the creation of data lakes, once the promised cornerstone of the business, began falling short of lofty business expectations. In my conversations and interactions with CSP executives, ~80% consider data to be one of their most valuable assets. They realize that data can help with personalization, thereby improving customer retention (#1 Priority for the industry) and provide early insights into churn, thereby allowing the CSP to take corrective action. The data is also critical for capacity planning, pricing strategies, predictive maintenance, network troubleshooting, product development, optimizing promotions, and fraud detection.
Sophisticated capabilities to manage data are now becoming available. The technology behind graphics processing units (GPUs) has evolved, unlocking new possibilities across various industries. GPUs can efficiently manage computationally intensive tasks that are crucial for deploying AI models. Admittedly, GPU costs are currently fluctuating, following a period of stabilization in 2024. This is due to surging demand, supply chain challenges, and tariffs affecting production costs. However, GPU costs are expected to normalize as competition between NVIDIA, AMD, Intel, Qualcomm, and others intensifies and the market becomes more saturated.
GPUs will enable CSPs to handle data at scale and drive innovation efficiently. Groundbreaking modernization is on the horizon, but CSPs are likely to be caught unprepared. If they want to take the buzz of AI to reality, they must first refocus on data hygiene and get their data house in order by focusing on:
