Design ML Architecture for Legacy Data

Scenario

You are helping modernize a client platform that relies on legacy systems, fragmented data sources, and manual business workflows. The client wants a scalable ML data architecture that can support production use cases without forcing an immediate full replacement of existing systems.

Question

How do you approach designing a scalable data architecture for a client with legacy constraints?