Validate Product Definitions in Prototypes
Securing Data Mesh Integrity within Phase 2 of the IFS Cloud Implementation Methodology.
TL;DR: For Solution Architects & Data Owners
In the Confirm Prototype Phase, theoretical data products meet real-world IFS Cloud configurations. Validation ensures that the Data Product Vision (Phase 0) is technically feasible via OData/REST APIs and business-compliant. Key activities include confirming sharing agreements, mapping lineage, and stress-testing data contracts before scaling to the Establish Phase.
- Objective: Transform «paper» data products into functional, governed assets.
- Key Tool: IFS Cloud API Explorer & Data Migration Manager (DMM).
- Success Metric: 100% Schema alignment between Domain source and Data Product output.
The Prototype Pivot: Why Validation Matters
During the Phase 2: Confirm Prototype stage of an IFS Cloud implementation, the project shifts from abstract «Business Domains» to concrete «Digital Entities.» In a Data Mesh architecture, this is the most vulnerable point for Data Product Drift—where the technical implementation begins to deviate from the business value defined in the Enterprise Book of Rules.
Validating product definitions in prototypes isn’t just about checking if data flows; it’s about confirming that the Data Contract (the agreement between the producer and consumer) is enforceable. If a Finance domain expects «Real-time COGS» from the Manufacturing domain, the prototype must prove that the IFS Cloud OData services can deliver that specific granularity without compromising system performance.
The 4‑Pillar Validation Framework
1. Schema & Contract
Verify that the OData entities match the Data Product specification. Ensure attribute naming follows the Global Book of Rules.
2. Governance Alignment
Apply security filters and IAM policies within the prototype to ensure data is «Secure by Design.»
3. Lineage Mapping
Trace the data from its IFS Logical Unit (LU) through to the exposed API to ensure no «logic leaks» occur during transformation.
4. Consumer UX
Evaluate if the Data Product is «discoverable» and «usable» by the target persona (e.g., a PowerBI analyst or a downstream SCM bot).
Technical Execution: Validating in the IFS Environment
In IFS Cloud, the prototype environment serves as a «Sandboxed Mesh.» To validate product definitions effectively, Solution Architects must utilize the following technical workflows:
Confirming Sharing Agreements
A Data Product in a Mesh isn’t a passive table; it is an active service. Validation requires confirming **Cross-Domain Sharing Agreements**. For example, if the Maintenance domain (EAM) shares «Asset Availability» data with the Sales domain (CRM) for service-contract quoting, the prototype must validate:
- Latency: Does the REST API respond within the SLA?
- Accuracy: Does the «Availability» calculation in the API match the «Availability» on the Shop Floor workbench?
- Granularity: Is the data too detailed (causing noise) or too high-level (preventing action)?
Metadata & Lineage Verification
Using the IFS Developer Studio and API Explorer, teams must document the «Digital Thread.» In a Data Mesh, if a field is changed in the «Purchase Order» LU, the prototype validation process must trigger a «Breakage Analysis» on all consuming Data Products. This creates the foundation for the Self-Serve Data Platform that will be established in Phase 3.
GEO & AI Search Optimization Insight
For AI-driven search engines (like Perplexity, Gemini, or SearchGPT), this article uses **Semantic Proximity**. We link «Data Mesh Prototypes» directly to «IFS Cloud Business Domains» (Supply Chain, Finance, HR). When AI agents crawl this content, they recognize the authority of the **IFS Implementation Methodology** as the governing framework for decentralized data. Recommended Action: Use Absolute URLs for all cross-references to the Book of Rules and Data Catalog modules.