Revolutionizing Data Infrastructure in 2025: Exploring Sovereign Clouds and Trends in AI

As we transition from 2023 to 2024, we witness a significant evolution in the realm of artificial intelligence (AI) and data management. If last year was about generative AI chatbots and enhanced search functionalities, the upcoming years focus on the innovative approach of agentic AI. This next generation of AI tools is not only capable of performing complex tasks but also of making autonomous decisions across various digital environments. Amid these advancements, one foundational element remains unchanged: the critical need for a robust and reliable data infrastructure, particularly through the lens of sovereign clouds.

Organizations are increasingly integrating sophisticated AI strategies. This transformation catalyzes various trends centered on managing, securing, and utilizing data effectively. Enterprises now embrace a multicloud approach, explore open data solutions, and implement comprehensive governance frameworks. These activities help prevent vendor lock-in while enhancing operational flexibility. Additionally, there is a growing emphasis on unstructured data, as data marketplaces emerge as vibrant ecosystems featuring pre-trained models and specialized datasets. Innovations in vector and graph databases further drive future developments in the data landscape.

As we look ahead to 2025, industry experts share their insights on how data infrastructure, particularly through sovereign clouds, is set to evolve.

1. The Emergence of Real-time Multimodal Data

Experts anticipate that by 2025, businesses will harness the power of multimodal data alongside AI technologies, leading to substantial operational transformations. This revolution is fueled by what leaders refer to as the Intelligent Data Flywheel. This innovative cycle relies on real-time data to drive AI-enabled insights, which, in turn, fosters ongoing innovation and enhancement. Previously underutilized data types such as images, videos, and sensor data will become essential, leading to improved predictions and automated solutions.

With a dedicated real-time data strategy, AI will autonomously address challenges, optimize practices, and create groundbreaking solutions. Companies will depend increasingly on AI agents to ensure data integrity and extract valuable insights, enabling human resources to concentrate on more complex endeavors. This shift in focus will redefine operational efficiency, accelerating innovation and promoting agility in organizations.

2. Liquid Cooling: A Game-Changer for Data Centers

As demands for AI technologies skyrocket, significant organizations are expected to transition to liquid cooling systems to enhance performance and energy efficiency. Leading hyperscale cloud providers will take the lead in this revolution, utilizing liquid cooling in AI data centers that host numerous AI accelerators and networking devices.

Furthermore, organizations may choose to install AI infrastructure in colocation facilities rather than building their own. This trend is partly driven by the motivation to mitigate costs related to designing and managing expansive intelligence frameworks. Cloud-based rental options will become more prevalent, making liquid cooling the standard approach in AI data centers.

3. Tackling Storage Challenges Amidst Data Explosion

With the data creation rate skyrocketing, forecasts predict that by 2028, an astounding 400 zettabytes of data will be generated, with a compound annual growth rate (CAGR) of 24%. In contrast, storage capacity is projected to increase by only 17% CAGR. This imbalance presents a pressing challenge for companies aiming to harness AI effectively. Businesses will need to strategically plan for more extensive data center spaces and capacity to ensure they can store the required data while maximizing their AI investments.

4. Transforming AI Factories to PaaS

By 2025, there’s an expectation that AI factories will evolve from simple infrastructure-as-a-service models to robust platform-as-a-service (PaaS) offerings. While the foundational services have provided essential support for AI adoption, this next phase will focus on building platforms that foster data affinity and deliver sustained value. Such an evolution will be vital for the long-term growth and competitiveness of AI factories.

5. Ensuring Reliability with Large Datasets

Enterprises are set to embrace extensive datasets while insisting on increased reliability from their AI solutions. Although early AI applications leaned heavily on foundational models trained with public datasets, innovative applications utilizing vast amounts of private enterprise data will now generate real value. Companies will demand more reliable outputs, moving beyond mere demonstrations.

Moreover, collaboration will be essential. AI developers must engage effectively with publishers and content providers to guarantee fair compensation for their data. Establishing licensing agreements will be crucial to avoid potential future disputes over data usage.

6. Analyzing Communication Data for Valuable Insights

By 2025, enterprises will leverage AI to analyze extensive terabytes of communication data—including emails, Slack messages, and Zoom transcripts. AI agents will provide analytical insights, actionable dashboards, and decision-support tools, leading to significant productivity boosts across various sectors.

7. Navigating Data Governance Challenges

As we approach 2025, data governance, accuracy, and privacy issues will emerge as critical barriers to effective AI deployment. Companies will recognize that successful AI outcomes depend heavily on reliable data. The complexities of managing large datasets while ensuring compliance will increase, prompting enterprises to invest in comprehensive data platforms that facilitate consistent management across multiple data sources.

8. The Growth of Unified Data Observability Platforms

By 2025, unified data observability platforms are anticipated to become indispensable for large enterprises. These platforms will enhance insights into performance metrics, data quality, pipeline integrity, cost efficiency, and user interactions. The automation of anomaly detection will further support data reliability and streamline compliance across various industries.

9. Increased Adoption of Sovereign Clouds

In 2025, we can expect a significant uptick in the adoption of sovereign clouds and private cloud solutions. Major cloud providers are heavily investing in the establishment of data centers worldwide to support these services. The demand for such solutions will likely surge, particularly influenced by new regulations, especially from the European region. Organizations with flexible cloud infrastructures will be well-positioned to leverage sovereign alternatives, while those reliant on rigid systems may face challenges in keeping pace with market changes.

10. The Rise of Edge Computing

The growth of edge computing is expected to accelerate, particularly with the rollout of 5G technology. This advancement will enable data processing nearer to its source, thereby reducing latency. The opportunity exists for AI applications to operate efficiently on mobile devices without relying on cloud resources.

Field technicians could utilize 5G access to enhance their work performance through AI, whether in healthcare during emergencies or by scientists making real-time evaluations in the field.

11. Protecting Unstructured Data: A Growing Necessity

As unstructured data accounts for over 90% of all information generated in the past decade, protecting it has become increasingly vital. Historically, data protection efforts focused on mission-critical data; however, the dominance of unstructured data demands new protective strategies. Safeguarding this type of data from cyber threats like ransomware is essential. This can be achieved through moving inactive data to immutable storage, which prevents alterations and boosts security.

To ensure the safety of unstructured data, IT teams are expected to seek management solutions that automate these protective measures. Such solutions will also facilitate auditing sensitive data within AI frameworks, which is paramount as AI technologies continue to progress.