Why Centralizing Your Data Isn’t The Same As Integrating It

Centralizing data in a warehouse or lakehouse improves access by placing tables in a shared storage environment, but it does not automatically integrate them because differing entity definitions, identifiers, metrics, and ownership can remain unresolved. The discussion identifies incomplete mergers and acquisitions, proliferation of SaaS applications, and unclear governance as common reasons centralized environments continue to function as silos, illustrated by customer records that use incompatible account IDs and email-based identifiers. It argues that meaningful integration requires shared definitions for entities such as customers and orders, common identifiers, accountable owners, reconciliation of source fields, and durable documentation of business context. Data federation combined with a semantic or context layer is presented as an alternative to copying every dataset, enabling governed queries across distributed systems while applying consistent metric and relationship definitions. The piece also contends that these practices are increasingly important for AI agents, which can otherwise reproduce the inconsistent answers produced by unreconciled data, and describes an Apache Iceberg-based lakehouse as a flexible foundation for storing core data while federating access to external sources.

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