Data Centers and Infrastructures: The Challenge of Expansion

THE DATA ERA

Data is both a product and a resource in a highly digitalized society in which most of the objects we usually interact with are designed to collect encoded information. In recent years the volume of data created, collected, copied, and consumed has exploded. The zettabyte era began in the mid-2010s, with data volume growing to 120 zettabytes in 2023 and projected to reach 181 zettabytes at the end of 2025, at a compounded annual growth rate exceeding 20% (1 zettabyte = 10^21 byte = 10^12 gigabyte).

There is a direct and obvious correlation between data volume and the size of the Big Data market. Organizations are investing massively in infrastructures and skills to extract value from this data. The chart below presents three different growth scenarios of Big Data market based on three different compounded annual growth rates (CAGR 11.00%, 11.96%, and 12.44%), highlighting the uncertainty but also the sector's significant potential.

Growth will be driven by different factors: the adoption of generative AI, the expansion of cloud computing, increased data volume from IoT (Internet of Things) devices, and the demand for real-time business analytics.

Currently the major market trends are:

• increased adoption of cloud-based Big Data platforms spurred by cost efficiency and scalability
• implementation of 5G networks
• edge computing advancements that reduce latency and improve performance
• development of technologies like generative AI, Machine Learning, and advanced analytics to strengthen prediction capabilities
• growing importance of real-time data processing for customer satisfaction and operational efficiency
• Big Data expansion in healthcare, BFSI, and in the retail sector for personalised services
• greater focus on data security, governance, and compliance in the analytical field

OPPORTUNITIES AND CHALLENGES

These numbers tell a clear story: we are in a pivotal moment in which the ability to manage data is not just a competitive advantage, but it is essential for survival. The gap between volume and utilization is indeed striking. A fascinating paradox exists in the data economy: while organizations invest billions to collect, store, and protect information, only 32% is actually put to work — the remaining 68% goes unleveraged. When we look at the global datasphere, the situation is even more stark: only around 2-3% of all data created is ever analyzed. This is not just waste—it's a symptom of a deeper problem. Why is this happening?

• unstructured data is difficult to process
• organisational silos prevent access
• shortage of expertise for advanced analysis
• lack of data-driven culture in organizations

True value relies on bridging the gap between collecting data and utilizing it. It is thus crucial to invest in companies and solutions that democratize access to data through Artificial Intelligence and Machine Learning, automating the analysis of unstructured data. Companies that that will succeed in analyzing even only 5-10% of their data will have a huge competitive advantage.

THE INFRASTRUCTURE BEHIND THE NUMBER

181 zettabytes of data, generative Artificial Intelligence, and real-time analytics are fascinating elements. But where is all this data physically stored?

Cloud computing, Artificial Intelligence, Internet of Things- are terms that evoke something immaterial. Yet every single byte of those 181 zettabytes must to located somewhere: on hard drive, in server racks, cooled by ventilation systems, powered by megawatts of electricity. The "cloud" is not a cloud at all, it is a global network of massive physical structures, increasingly complex and energy intensive.

Data centers are not simple data warehouses: they are the backbone of the digital economy. Every Google research, every bank transaction, every video stream, every Machine Learning model relies on these infrastructures and their geographical distribution reveals much about the power dynamics, strategic investment and geopolitical challenges of our time.

While Big Data market growth 12% annually, the infrastructure supporting it must evolve rapidly. This is not merely about adding storage capacity. The data center of the future have to face challenges as energy efficiency, 5G network latency, cybersecurity, and environmental sustainability. And with increasingly complex AI models requiring exponentially greater computing power-infrastructure, the situation is changing dramatically.

Let's examine how this critical infrastructure is distributed globally, and what it reveals about the structure of digital power.

This geographical concentration is not random. It reflects decades of infrastructure investments, innovation-friendly policy environments, and integrated technological ecosystem. It also raises issues regarding data sovereignty.

• the United States dominates the sector with 59.2% of global data centers (approximately 12 times more than Germany which holds second place)
• the top five countries (United States, Germany, China, UK, and France) host 75% of global data centres

Regional Dynamics: Asia and Europe

Asia is expected to show strong growth throughout the forecast period, driven by:

• rapid digitalization across emerging economies
• government investments in digital infrastructures
• rising middle class with increasing digital consumption

Europe accounted for 21.2% of the Big Data market share in 2024, with Germany leading in industrial data analysis through the Industry 4.0 initiative that connects over 400,000 production sites. Digital innovation is an increasingly relevant issue for Europe, which is seeking to reduce technological dependence through initiatives such as Gaia-X and Digital Decade framework.

MARKET SHARE BY SECTOR

Which sectors are currently leading this transformation?

• BFSI (Bank, Financial Service, and Insurance) represent the largest share accounting for 35%, driven by the need for real-time analysis in risk management, fraud detection, and personalized customer service
• HEALTHCARE AND LIFE SCIENCES ranks second, fueled by advanced analysis to improve patient outcomes and enable precision medicine
• RETAIL leverages Big Data to decode customer behaviour, optimize inventory management, and deliver personalized customer experience
• MANUFACTURING harnesses Big Data for predictive maintenance and supply chain optimisation

OVERLOOKED CHALLENGES

Energy Sustainability

Data centers are highly energy intensive. With project growth, energy efficiency and the adoption of renewable energy sources will be critical. Anticipated innovations include:

• liquid cooling technologies
• AI-optimized energy management
• green energy-powered facilities

Shortage of Skilled Workforce

This is one of the most underestimated challenges. According to the Future of Jobs report (2025), 59% of the global workforce will need new skills, re-skilling, or upskilling by 2030. Organizations need an ecosystem of complementary expertise spanning the entire data life cycle.

• Data Analyst. Collects, identifies, categorizes, and interprets data. Extracts useful information and develops reports to support business decisions
• Data Scientist. Develops and implements Machine Learning models, analyses complex datasets and generates actionable insights to support business decisions and strategies
• Data Engineer. Designs and maintains data infrastructure, builds pipelines to process large volumes of data
• Business Analyst. Understands business needs, processes and challenges, and proposes solutions using data and technology

The real challenge is not technical but cultural. Data and data analysis, powered by AI, make verifiable and quantifiable what could be considered mere intuitions. A McKinsey & Company (2024) report found that companies that use data as the foundation of their decision-making processes are 50% more likely to achieve revenue growth of 10% or more. But this requires transforming the entire organization into a data-driven culture, in which intuition is validated by evidence, not replaced by it.

WHERE ARE WE HEADING?

The next decade will not reward those who collect the most data, but those who democratize access to insights. AI can bridge the technical skills gap, but only when accompanied by a far more difficult achievement, the cultural transformation from intuition-based to evidence-driven decision-making.

Tools to achieve this are emerging: more efficient infrastructures, democratized AI, and increasingly sophisticated analytics. But an uncomfortable question remains: how many organizations are genuinely prepared to take the leap?