AI Ready Data: Data Preparation, Versioning, and Vector Stores — The Expanding Role of Storage in the AI Pipeline

As AI adoption accelerates across enterprises, data readiness has emerged as one of the most critical determinants of model quality and operational success. This session presents narrative across three foundational aspects of the AI data journey—data preparation, Git style data versioning, and vector stores—and highlights how storage plays a balanced role as both enabler and implementer of these workflows.

We begin with data preparation, examining how raw, diverse enterprise data becomes structured, enriched, and usable for AI systems. Rather than detailing algorithms or pipeline code, we explore how storage platforms participate in this process: facilitating scalable access, supporting metadata growth, enabling efficient transformations, and allowing organizations to maintain consistent, reproducible.

The narrative then moves to Git style data versioning, which is becoming essential for AI governance and reproducibility. We discuss the principles of branching, commits, lineage, and time travel—and how these concepts translate to large, evolving datasets. Storage is shown here not only as the durable foundation for versioned assets but also as an active implementer of lineage tracking, immutability guarantees, and reproducibility.

The final segment focuses on vector stores, now a core component of retrieval augmented generation, semantic search, and context aware inference. Without diving into index internals, we explain how vector stores change workload behaviors, require new patterns of data retrieval, and introduce different scaling expectations. Storage steps into a dual role: enabling fast, consistent access to embeddings while implementing persistence, durability, and scalability models that allow vector stores to operate reliably in enterprise environments.