SAP Data Product

Status: Skeleton — under review.
Scope: Domain-oriented data products in SAP landscapes with owner, source, consumers, and quality risks.

What it is

An SAP data product is a curated, governed, and versioned data asset derived from SAP systems (primarily S/4HANA) and exposed for cross-domain consumption. It includes schema, semantics, quality commitments, and ownership.

SAP data product examples

Data Product	Owner	Source System	Primary Consumers	Quality Risks	Integration Patterns
Customer	Sales/CRM domain	SAP S/4HANA, SAP MDG	CRM, Analytics, E-commerce, Marketing	Duplicates, incomplete addresses, CVI sync errors	IDoc, OData, Replication
Supplier	Procurement domain	SAP S/4HANA, SAP MDG	Procurement, Finance, Risk, Ariba	Bank details, tax codes, duplicate vendors	IDoc, OData, Ariba Network, Events
Material/Product	Supply Chain domain	SAP S/4HANA, SAP MDG	Supply Chain, Sales, Manufacturing, E-commerce	Classification, units of measure, translation	IDoc, OData, Events, CDC
Sales Order	Sales domain	SAP S/4HANA	Fulfillment, Billing, Analytics, WMS	Pricing, availability, delivery blocks, incompletion	OData, Events, CDC
Purchase Order	Procurement domain	SAP S/4HANA	Procurement, Finance, EWM, GR/IR	Approval, incompletion, release strategy	IDoc, OData, Events
Delivery	Logistics domain	SAP S/4HANA, SAP EWM	Shipping, Billing, Tracking, WMS	Picking accuracy, serial numbers, batch management	IDoc, OData, Events
Billing Document	Finance domain	SAP S/4HANA	Finance, Tax, Analytics, Output Control	Split rules, output control, pricing	IDoc, OData, Batch
Inventory Position	Supply Chain domain	SAP S/4HANA, SAP EWM	Planning, Sales, Manufacturing, Analytics	ATP vs physical stock, reservations, in transit	OData, CDC, Events
Financial Posting	Finance domain	SAP S/4HANA	Consolidation, Tax, Analytics, Treasury	Cost object, profit center, segment	IDoc, OData, Batch
Business Partner	Master Data domain	SAP S/4HANA, SAP MDG	All domains	CVI sync, role assignment, duplicate check	IDoc, OData, Replication, Events

Design decisions

Decision	Recommendation
Source interface	CDS views with `@Analytics.dataExtraction.enabled` for extraction; OData for API
Quality enforcement	MDG validation rules for master data; Datasphere metrics for analytical
Consumption	Datasphere remote tables (federation) or replication flows (replication)
Analytics	SAC stories and planning models consuming Datasphere semantic models
Governance	Domain owner approves schema changes; central team owns catalog and platform

Operational failure modes

Master data duplicate check false positive → blocked business process
CDS view change removes field used by consumer → downstream pipeline failure
Replication lag exceeds SLA → analytics based on stale data
CVI synchronization error → customer/vendor mismatch between BP and customer master

Monitoring/support model

Datasphere Impact and Lineage Analysis for downstream dependency tracking
MDG data quality scorecards and duplicate check reports
Replication flow monitoring for latency and row counts
SAC story validation for field existence and data freshness

Ownership model

Domain owner: owns data product definition, quality rules, and consumer relations
Master data team: owns MDG configuration, validation rules, and consolidation
Data platform team: owns Datasphere infrastructure, replication, and catalog
Consumer teams: own integration testing and usage compliance

AMS incident patterns

Customer master not replicated to CRM → check IDoc status, partner profile, or BP relationship
Sales order pricing incorrect in analytics → verify condition technique and pricing procedure
Inventory position mismatch between S/4HANA and WMS → check movement types and goods receipt
Financial posting missing in consolidation → verify posting period, cost object, and extractor delta

AI/agent opportunity

Auto-generate data product specifications from CDS view metadata
Predict quality risks from historical error patterns and source system changes
Detect downstream consumer impact from proposed schema changes
Generate root cause analysis for replication lag from system metrics

Source references

Verification limitations

Data product boundaries vary by organization; this catalog provides common patterns.
Content is synthesized from public SAP documentation and architecture practice.
No private implementation details are included.