A Common Data Plane Simplifies Hybrid Cloud and AI

Andy Gremett
Sr. Product Marketing Manager, Product Solutions Marketing

June 8, 2026

Hybrid cloud was meant to simplify IT — but for many organizations, it has done the opposite.

As data spreads across on-premises systems, multiple clouds and edge environments, complexity (not flexibility) has become the defining challenge. With AI initiatives now dependent on distributed, high-quality data, this complexity directly impacts performance, governance, and cost.

The lack of a unified view and thereby management of data is the biggest issue spurred by complexity. This is where a common data plane becomes essential.

Why a Common Data Plane Matters

Hybrid cloud introduces flexibility, but also fragmentation. As a result, many organizations face:

Data silos across block, file, and object storage
Demanding AI workloads requiring fast, consistent access to distributed data
Inconsistent policies across security and compliance
Unpredictable costs as consumption scales
Labyrinthine operations from disconnected tools
Limited data mobility across environments

This fragmentation makes it difficult to extract value from data at scale. And without a unified layer to manage, govern, and move data consistently organizations can struggle to modernize effectively. A common data plane addresses both challenges by aligning technology capabilities with business outcomes, as well as architectural and operational constraints, like so:

Challenge	Operational Impact	Architectural Outcome
Siloed data across block, file and object	Slower AI pipelines and fragmented workflows	Unified data access with a single view across all environments
Inconsistent policies	Compliance risk and security gaps	Standardized governance and policy enforcement across workloads and locations
Limited data mobility	Inflexibility and cloud/vendor lock-in	Seamless movement across on-premises and cloud
Tool sprawl	Higher operational/manual overhead	Simplified management with fewer tools
Poor cost visibility	Budget overruns and inefficiencies	Improved transparency and cost control, plus optimized consumption

Enabling AI and Modern Data Workloads

AI initiatives amplify the need for a common data plane.

Training and inference pipelines rely on fast, reliable access to distributed datasets that often span multiple environments. Without a unified approach, organizations encounter bottlenecks such as data latency, duplication, and inconsistent governance. These challenges slow down innovation and increase operational risk.

A common data plane provides the foundation AI needs by enabling consistent access, policy enforcement and data movement across environments — ensuring that data is available where and when it’s needed.

Moving From “Hybrid By Accident” to a Unified Data Strategy

Modern data platforms are evolving to support this model. Solutions built on software-defined storage, for example, can extend a common data plane across on-premises and cloud environments by standardizing policies, enabling mobility, and simplifying operations without requiring application changes.

Approaches like Hitachi Vantara’s VSP One reflect this shift, extending a common data plane through a unified set of data services across block, file, and object environments. By combining software-defined flexibility with purpose-built infrastructure, VSP One enables organizations to apply consistent policies, scale independently, and maintain enterprise-grade performance and resilience across hybrid cloud.

Industry research underscores the urgency of this transition: 44% of organizations cite IT infrastructure constraints as the top barrier to scaling AI, according to the Flexential 2025 State of AI Infrastructure Report. At the same time, reporting from ITPro shows that many organizations still operate in “hybrid by accident” environments marked by fragmented tools, limited visibility, and operational inefficiencies reinforcing the need for a unified approach to data access, control and management across environments.

This is where modern data platforms differentiate—by extending a consistent operational model across block, file and object data, while enabling disaggregated scalability and enterprise-grade resilience across hybrid cloud.

The goal is not just infrastructure modernization, but alignment between data strategy and business priorities—supporting agility, resilience and innovation environments.

A Data Foundation to Rely On

Infrastructure limitations, fragmented data environments, and inconsistent operations continue to slow progress at organizations as they scale AI and hybrid cloud strategies — all challenges that are systemic across modern IT estates. With the right data foundation, organizations can simplify operations, improve visibility, and scale AI initiatives with confidence.

A common data plane enables: