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A global footwear and apparel brand ran merchandise and demand planning across 35+ markets. Each market sent sales and inventory data in its own format, with its own product hierarchy and its own exceptions. Planning data took 17 days to become usable. By then, the reorder window on a fast moving style had often closed.

The problem was not file transfer speed. Validation rules, mappings and transformations were written directly into the pipelines, so every new market meant engineering effort, testing and a release. Exceptions surfaced late and pulled in planners, business users and IT. The regional knowledge needed to resolve them sat with a small number of subject matter experts. Manual governance does not scale, and the cost of that fell on the planning team.

In this webinar, Databricks and Infocepts walk through what replaced it. An agent-powered data platform is a data engineering system where AI agents, rather than scripts, validate, enrich and correct incoming data, with business owners approving the exceptions that matter.