Data Product Extension
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Data products live in catalogs. Applications live in enterprise architecture. Nothing connects the two — until now.
This extension closes the gap between data catalogs and enterprise architecture. It introduces the Data Product fact sheet type to SAP LeanIX. LeanIX becomes the catalog of catalogs: the single place where data architects and enterprise architects make decisions together — focused on the business-critical data products that matter for EA decisions, with business lineage from applications to data products to consumers, grounded in your conceptual data model.
Details
As an enterprise architect, I want to see business lineage from applications to data products to consumers, so I can plan safe transformations without blindsiding downstream systems when I decommission an application.
As a data architect, I want to know which applications and AI agents consume my data products, so I can assess the blast radius before changing schemas or deprecating datasets.
As a solution builder, I want to discover which data products already exist before scoping a new solution, so I can reuse instead of rebuild.
As a program lead transforming from a legacy data warehouse to a data mesh, I want to map which reports and tables become which data products — and who consumes them — so I can transition domains incrementally without breaking downstream analytics.
As a data governance lead, I want data products grounded in our conceptual data model, so business terms stay consistent across catalogs and the EA repository.
As a compliance officer, I want documented lineage from business-critical data products to applications for GDPR, DORA, and EU AI Act audits, so evidence is on-demand instead of a last-minute scramble.
This extension requires no other extension.
Disclaimer
Important: The Data Product fact sheet includes 15 standard fields and 7 relationships that cover common enterprise use cases. This metamodel is a starting point — we expect customers to extend it to their individual needs. Add custom fields (Data Quality Score, SLA, Cost Center), additional relationships, or subtypes as your data governance requirements demand.