Financial institutions are making data growth their top storage priority, but few are prioritizing the AI-ready infrastructure and stronger data foundations needed to manage that growth at scale, according to a new report from Hitachi Vantara, the data storage, infrastructure and hybrid cloud management subsidiary of Hitachi Ltd. (TSE: 6501). The research found that 35% of organizations cite managing data growth as a top data storage priority, the highest response across the survey, yet only 10% prioritize enabling AI-ready storage and data platforms and just 9% prioritize the implementation of centralized data hubs for governance, reporting, AI/ML and reuse.
Based on a survey of 100 financial services decision-makers across banking, payments and investment firms globally, the research points to a divided market still working to align storage investments with long-term data strategy. While managing data growth ranked highest, it was still only cited by slightly more than one-third of participants (35%), while other near-term priorities closely grouped around governance, accessibility and modernization.
Notably, the survey found that the second-highest priority cited was ensuring data sovereignty, regulatory compliance and policy-driven governance (30%). The findings show that governance and sovereignty requirements are already influencing how financial institutions prepare for an AI-driven future, including:
- 99% of respondents say data sovereignty concerns influence where they run AI workloads
- 19% report that data sovereignty concerns significantly limit AI workload scalability or performance
- When it comes to which factors are most important when looking at object storage, 65% cite cost or total cost of ownership. That’s nearly 20 percentage points higher than the next choice (data resilience and availability, selected by 46% of respondents).
“Financial institutions clearly recognize that data management is becoming more complex, but many are not yet fully addressing what their environments require,” said Octavian Tanase, chief product officer, Hitachi Vantara. “As data volumes grow, organizations need unified data platforms that can span block, file and object storage to reduce fragmentation, improve visibility and support consistent governance for the mission-critical data that financial institutions depend on. This includes unstructured data used for analytics and AI, as well as the mission-critical databases and transaction systems that support core operations. That activation depends on the data availability and resilience needed to keep information accessible, protected and ready for use.”
Cost Concerns Shape Storage Decisions
The research also shows why these priorities can be difficult to align. When selecting object storage platforms, 65% of financial institutions cite cost as the most important factor, underscoring how near-term cost pressures can compete with the longer-term investments needed to ensure proper accessibility, governance and AI readiness. That balance is especially important in financial services, where AI-ready environments must have object storage to support emerging analytics use cases and block storage to handle the mission-critical systems that continue to run core banking, payments and investment operations.
Sovereignty requirements add another layer of complexity, especially as financial institutions determine where AI workloads and data can reside. Of note, 23% restrict AI workloads to specific regions, 21% train models centrally while keeping data local, and 16% split training and inference across locations due to sovereignty rules. Together, these findings show how cost discipline, regulatory control and fragmented deployment models can complicate efforts to build consistent, AI-ready data foundations.
A Two-Speed Market Is Taking Shape
Adoption of object storage is splitting along maturity lines. While 35% of organizations report enterprise-wide deployments across multiple workloads and teams, 36% remain in early-stage or pilot phases, reflecting a two-speed market where some institutions are building scalable, integrated data management processes and others continue to operate in less mature deployment environments.
As financial institutions expand AI and analytics initiatives, object and block storage are becoming more important foundations for how data is stored, governed and reused. Modern object storage can help institutions connect data across teams and workloads, support data lakehouse architectures and reduce the need for complex data movement that can add cost, risk and operational friction. For highly regulated financial institutions, that foundation must also connect with the mission-critical block storage environments behind core databases, transaction systems and other operational data sources that must be managed in highly secure and resilient storage systems.
“Financial institutions are being asked to manage more data, in more places, while maintaining control, resilience and compliance,” Tanase said. “Modern storage platforms, such as Hitachi Vantara’s Virtual Storage Platform One, feature block, object and file storage systems that can help organizations address that complexity by supporting scalable, governed and accessible data environments that serve both traditional retention needs and emerging AI-based demands, including data lakehouse architectures supported by open table formats and native S3 Tables capabilities.”
Across the broader VSP One portfolio, Hitachi Vantara helps organizations bring block and object storage capabilities together as part of a secure, resilient data foundation for mission-critical applications, core data platforms and AI at scale.
Methodology
Hitachi Vantara commissioned FStech to assess evolving infrastructure and data management priorities among financial institutions. The study surveyed 100 global senior decision-makers across banking, payments and investment firms, spanning IT, data, security and AI leadership roles. The research explored object storage usage patterns, platform selection criteria, deployment models, data sovereignty considerations and the growing role of modern storage architectures in supporting analytics and AI initiatives.
