Map the complete data architecture of the client environment — from domain ownership and system of record definition through transformation strategy, schema mapping, and data transport — so integration requirements are fully understood before development begins.
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Data domain mapping
Define the core data domains (products, customers, orders, inventory) and establish ownership, governance, and dependencies across the client environment.
System of record definition
Identify which system owns each data domain and resolve conflicts where multiple systems claim authority over the same data.
Transition and migration strategy
Design the approach for migrating or transforming data from legacy systems to target platforms with sequencing and risk context.
Schema mapping
Map specific fields between source and target systems to validate transformation logic before integration development begins.
Data exchange modeling
Document how data moves between systems — direction, method, frequency, and volume — so integration requirements are complete before development.
Writeback and transport design
Define how data is written back from downstream systems (analytics, warehouse) to source systems and the transport mechanisms required.
Data domain map
A structured view of all core data domains with ownership, governance, and dependency context across the client environment.
System of record register
A definitive record of which system owns each data domain, with conflict resolutions documented and approved.
Transition and migration strategy
A structured approach to data migration from legacy to target systems with sequencing, risks, and validation criteria.
Schema and field mappings
Field-level mappings between source and target systems ready for integration development and validation.
“Most integration failures trace back to unresolved data ownership and undefined transformation logic. Data Intelligence makes both explicit before development begins.”