Data Management and Storage Strategy in the AI Era
Artificial intelligence (AI) is driving an unprecedented surge in data volumes, often unstructured and spread across various parts of an organization. This growth exposes existing weaknesses in data management, such as data silos and sprawl, making it harder to locate, control, and govern data effectively. Pure Storage EMEA’s field chief technology officer, Patrick Smith, shares insights on how organizations can address these challenges with smarter storage strategies.
The Core Challenge for Organizations and Their Data
AI has brought longstanding data issues into sharper focus. While enterprises focus on training large models and scaling AI workloads, they face foundational problems like fragmented data environments and unclear data ownership. The volume and variety of data continue to grow, but many organizations lack a unified platform to manage it.
This leads to reactive, manual data management and uncertainty about investments in data infrastructure. Predicting the growth or success of AI projects is difficult, which complicates decisions on data storage and governance. These factors create significant hurdles as businesses try to keep pace with AI-driven demands.
Key Components to Make Data More Manageable and Valuable
A practical step is to create a virtualized cloud of data that clearly shows where different data sets reside. It’s critical to place data on the right platforms that provide the necessary reliability, availability, and performance, while simplifying management.
Automation plays a central role here. By automating data management based on policies and processes, organizations can reduce manual tasks and avoid errors that come from misconfigurations—such as missed snapshots or improper replication. This approach improves cyber resilience by consistently protecting data and reduces risks tied to human mistakes.
Translating This Into Storage Strategy
Storage should enable efficient data handling, not add complexity. Managing individual storage devices across a sprawling environment can overwhelm IT teams and detract from focusing on data itself. Instead, managing storage at a fleet level through automation and policy-driven processes is more effective.
This shift frees up resources to concentrate on data management and delivering value to business users, while technology handles routine storage operations. Automation reduces operational overhead and creates a more reliable, scalable storage environment that supports AI workloads.
For those looking to deepen their skills in AI and data management, exploring courses on Complete AI Training can provide practical knowledge and tools to build effective AI and data storage strategies.
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