Why 2025 will redefine data infrastructure: 11 forecasts from experts

If 2023 is all about AI-powered chat and search bots, 2024 Agent AI introduced – Tools capable of planning and executing multi-step actions across digital environments. from Devin Engineering breakthroughs of Microsoft’s early experiments with See the co-pilotThe innovations were diverse, but one constant remained: the need to keep data infrastructure organized and reliable.

As organizations move toward advanced AI initiatives, several trends have reshaped how data is managed, secured, and used. Companies have increasingly adopted it Multi-cloud, Open dataand open governance strategies to avoid vendor lock-in and gain more flexibility. They also focused on Unstructured datatransforming data marts into hubs that provide pre-trained AI models with proprietary datasets and applications. At the same time, advances in vector and graph databases added new capabilities, laying the foundation for what was to come.

Now, as the AI ​​story continues to evolve, industry leaders are sharing their predictions for how the data infrastructure that supports it will evolve in 2025.

1. Real-time multimedia data will feed an intelligent data flywheel

“In 2025, organizations will fully embrace multimodal data and AI, transforming the way they work and deliver value. At the heart of this transformation is the ‘Intelligent Data Flywheel’ – a dynamic cycle where real-time data powers data-driven insights.” AI, driving innovation and continuous improvement Today’s dark data – images, videos, audio, sensor outputs – will become key to unlocking clearer predictions and smarter, more adaptive automation in real time, ultimately leading to greater understanding. Richness and accuracy of business reality.

“With a flywheel of real-time data, AI will autonomously diagnose problems, improve operations, and create innovative solutions. Businesses will rely on AI agents to ensure that Data qualityDiscover insights and shape strategies, enabling human talent to focus on higher-level tasks. This will redefine efficiency, accelerate innovation, and transform companies into more dynamic and intelligent enterprises.

– Yasmine Ahmed, Executive Director of Strategy and Outbound Product Management for Data, Analytics and AI at Google Cloud

2. Cooling Agent: Liquid-cooled data centers

“As AI workloads continue to drive growth, leading enterprises will move to liquid cooling to maximize performance and power efficiency. Large-scale cloud providers and large enterprises will lead the way, using liquid cooling in new AI data centers housing hundreds of thousands of accelerators Artificial intelligence, networks and software.

“Companies will increasingly choose to deploy AI infrastructure in distribution facilities rather than build their own – in part to ease the financial burden of designing, deploying and operating large-scale intelligence manufacturing. Or they will lease capacity as needed. These deployments will help organizations take advantage of state-of-the-art infrastructure.” infrastructure without having to install and operate it themselves. This shift will accelerate the industry’s wider adoption of liquid cooling as a key solution for AI data centers.

– Charlie Boyle, Vice President of DGX Platforms at Nvidia

3. Global data explosion to create storage shortage

“The world is generating data in unprecedented quantities. In 2028, Up to 400 zettabytes It will be generated at a CAGR of 24%. However, the storage installed base is expected to achieve a CAGR of 17% – and thus (grow) at a much slower pace than the growth in the resulting data. It takes a year to build a hard drive. This disparity in growth rates would disrupt the global balance between supply and demand for storage. As organizations become less experimental and more strategic in using AI, they will need to build more physical data center space and capacity plans to ensure storage supply and fully monetize investments in AI and data infrastructure – while balancing financial, regulatory and environmental concerns. “.

– BST, Executive Vice President and Chief Commercial Officer, Seagate Technology

4. AI factories will evolve into PaaS

“In 2025, AI factories will evolve beyond their initial phase of providing infrastructure as a service, delivering compute, networking and storage services, to delivering platform capabilities as a service. While core services were essential to kick-start AI adoption, the next wave will AI factories will prioritize platforms that drive data convergence and deliver lasting value, and this shift will be key to making AI factories sustainable and competitive in the long term.

– Rajan Goyal, Co-Founder and CEO, DataPelago

5. Companies will use their own huge data sets but will demand reliability

“For the most part, early applications of AI just used basic models trained on massive amounts of public data. As sophisticated RAG applications proliferate and products to produce structured data rapidly mature, applications that leverage massive amounts of private enterprise data will begin to create real value.” But the standards for these applications will be high: companies will demand reliability from AI applications, not just a quick demo.

“Furthermore, AI companies offering these models will have to play well with publishers and content providers to protect the future of AI development. They will have to enter into licensing agreements with content providers to ensure they receive compensation for the extremely valuable data they provide. This is happening soon, before it turns into a tangle of lawsuits and AI crawler bans.

– Sridhar Ramaswamy, CEO, Snowflake

6. Enterprise agents will devour communications data

“In 2025, organizations will mine terabytes of communication data, such as emails, Slack messages, and Zoom texts, using agents that deliver analytical insights, dashboards, and actionable decision support tools.

“This will lead to significant improvements in productivity across industries.”

– Nikolaos Vassiologlou, Vice President of Research and Machine Learning at RelationalAI

7. Data governance and quality will be the biggest barriers to successful and ethical AI adoption

“In 2025, data governance, accuracy, and privacy will emerge as the top barriers to effective AI adoption. As organizations look to scale AI, it will be realized that successful AI outcomes depend entirely on trustworthy data. Managing and preparing massive amounts of data Ensuring compliance and maintaining accuracy will provide complex challenges that companies will need to overcome by investing in Core data platforms Which enables unified management across diverse data sources.

“As a result, we will see a stronger focus on data governance roles and governance frameworks that align with AI initiatives, as companies realize that unreliable data directly impacts the effectiveness of AI.”

– Jeremy Kelloway, Vice President of Engineering for Analytics, Data and AI at the EDB

“In 2025, unified data monitoring platforms will emerge as essential tools for large enterprises, enabling end-to-end visibility into data infrastructure performance, quality, pipeline health, cost management and user behavior to address complex governance and integration challenges. By automating anomaly detection and enabling insights into Real-time, these platforms will support data reliability and streamline compliance efforts across industries.

– Ashwin Rajiva, Co-Founder and CTO of Acceldata

9. All hail the Sovereign Cloud

“In 2025, we will see a real push into sovereign and private clouds. We are already seeing the largest hyperscalers spending billions of dollars building data centers around the world to deliver these capabilities. This…capacity will take some time to come online; in the meantime, Demand will rise dramatically driven by a wave of legislation coming mostly from the European Union and those with flexible, scalable cloud infrastructure will be able to quickly adopt sovereign or private approaches Consistent and solid they will put themselves behind the curve.

– Kevin Cochrane, Marketing Director at Vultr

10. Ascension Data processing at the edge

“I’m watching the potential expansion of edge computing, driven by the spread of 5G networks, which brings data processing closer to the source and reduces latency. This could help democratize AI. The question is, can we build AI applications?” Effective artificial intelligence that runs on mobile devices, perhaps without relying on cloud resources?

“If 5G technology is available to field technicians, they can leverage AI to assist in their work – whether that’s medical professionals providing diagnosis and treatment in disaster areas where 5G is available but Wi-Fi is not, or engineers and scientists working on-site to make decisions.” Using AI-powered research and real-time calculations.

– Jerrod Johnson, Chief Technology Evangelist at CData

11. Protecting unstructured data will become more urgent

“Traditionally, data protection has focused on mission-critical data because this is the data that needs to be restored the quickest. However, the landscape has changed, with unstructured data growing to comprise 90% of all data created in the past 10 years. The large surface area of ​​petabytes Unstructured data coupled with its widespread use and rapid growth makes it highly vulnerable to ransomware attacks. Cybercriminals can use unstructured data as a Trojan horse to infect an organization. Cost-effective protection of unstructured data from ransomware will become a tactic Defensively, it is critical, from moving cold, inactive data to storing immutable objects where they cannot be modified.

“To this end, IT and storage managers will look for unstructured data management solutions that provide automated capabilities to protect, segment, and audit the use of sensitive and internal data in AI – a use case that is bound to expand as AI matures. Furthermore, they will need to create Systematic ways for users to search through company data stores, organize valid data, verify sensitive data, and transfer data to AI through audit reports.

– Krishna Subramanian, co-founder of Komprise

In short, 2025 promises major advances in enterprise data infrastructure, from multimedia data flywheels to sovereign clouds. However, challenges such as data management and storage shortages will persist. Success in this dynamic space will depend on balancing innovation, trust and sustainability, and turning data into a lasting competitive advantage.