Data Access for AI: Getting Started

Enterprise AI initiatives often struggle less with model selection than with providing models and agents secure, timely access to the distributed business data needed for meaningful answers. The piece advocates a federated data-access model that queries data in place across lakes, warehouses, and operational systems, reducing the cost, latency, and regulatory complications of copying all data into a central repository. It recommends applying consistent identity controls, row filters, column masking, and auditing through a unified query layer, while packaging curated data products with tested business definitions and metadata so AI systems can interpret data reliably. A proposed 90-day starting approach focuses on one owned use case, two to four data sources, identification of data that cannot move, deployment of federated querying and minimum governance controls, and use of a defined data product rather than raw catalogs. Centralization and ETL remain useful for workloads requiring locally stored, frequently reused data, but federation is presented as a flexible initial path for governed AI access.

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