How Enterprises Can Rethink Data Management in the AI Era

Enterprises are generating data at an unprecedented scale, requiring a shift from traditional storage to AI-driven solutions. Sanjay Agrawal from Hitachi Vantara discusses how enterprises can rethink data management with AI.

- Aanchal Ghatak

Enterprise data is growing at an exponential rate, with IDC projecting that global data creation will reach 175 zettabytes by 2025. A significant portion of this data is unstructured—videos, sensor data, and logs—requiring more than just traditional storage solutions. Businesses now seek intelligent storage systems that offer real-time analytics, automated data classification, and built-in cybersecurity measures to ensure data remains both accessible and secure.

As enterprises generate data at an unprecedented scale, traditional storage and management strategies are no longer sufficient. Sanjay Agrawal, CTO and Head Presales at Hitachi Vantara, India and SAARC, shares how the company is pioneering data management with AI-driven storage solutions, business-centric SLAs, and sustainability-focused innovations.

Why Traditional Storage Strategies Are No Longer Sufficient

The data landscape has evolved dramatically in the past decade. Enterprises now generate diverse forms of data—human, business, and machine data—that require a sophisticated approach to storage and management. While structured business data was once the primary focus, the exponential rise in human and machine-generated data has shifted priorities.

“Our data strategy today is vastly different from a historically, enterprises managed structured data in centralized storage systems. However, the shift towards AI-driven applications, IoT data streams, and real-time analytics demands a new approach.” decade ago. Enterprises are no longer just managing structured business data in terabytes. Now, we are dealing with petabyte-scale human and machine data, requiring a shift from operational management to innovation.” Sanjay adds.

Historically, enterprises managed structured data (databases, transactions) in centralized storage systems. However, the shift towards AI-driven applications, IoT data streams, and real-time analytics demands a new approach.