Validate Product Definitions in Prototypes

Introduction

Validating product definitions in prototypes is a critical step in Phase 2: Confirm Prototype of your IFS Cloud Data Mesh implementation. This phase ensures that your data products are accurately defined, aligned with business domains, and ready for scalable deployment. By validating prototypes, you mitigate risks, confirm data sharing agreements, and establish robust governance  -  setting the stage for a successful, domain-driven data architecture.

Why Validate Product Definitions in Prototypes?

• Risk Mitigation: Identify and resolve issues early, reducing costly rework in later phases.
• Stakeholder Alignment: Ensure business domains and technical teams agree on data product scope, quality, and ownership.
• Governance Readiness: Test federated governance models and confirm compliance with enterprise standards.
• Data Sharing Confidence: Establish clear agreements for cross-domain data access and usage.

Step 1: Review Prototype Scope and Business Alignment

Objective:

Ensure the prototype covers 40 – 50 main end-to-end business processes and aligns with IFS Cloud’s modular design.

Actions:

1. Map IFS Functional Modules to Business Domains:
   • Use the IFS Scope Tool to document and refine the scope of each data product.
   • Align modules (e.g., Finance, Supply Chain, HR) with business domains and processes.
   • Example: For a manufacturing domain, validate that the prototype includes production planning, shop floor control, and quality assurance data products.
2. Conduct Collaborative Workshops:
   • Engage domain owners, data stewards, and technical teams to review prototype functionality.
   • Refine scope to minimize customizations and maximize adherence to IFS best practices.
3. Document Findings:
   • Update the Enterprise Book of Rules with validated process flows, data ownership, and governance rules.

Step 2: Validate Data Product Definitions

Objective:

Confirm that each data product meets the criteria for Data as a Product (clear service levels, discoverability, validation rules, and metadata).

Actions:

1. Checklist for Data Product Validation:
   • Service Levels: Define SLAs for availability, freshness, and accuracy.
   • Discoverability: Ensure products are listed in the IFS data catalog and accessible via REST APIs/​OData.
   • Validation Rules: Implement automated validation (e.g., data quality checks, format compliance).
   • Metadata: Document specifications in the Book of Rules and track quality metrics in the Data Tracker.
2. Example: Supply Chain Data Product
   • Definition: Inventory levels, demand forecasts, and supplier performance metrics.
   • Validation: Automated checks for data completeness, timeliness, and integration with procurement modules.
3. Tools to Use:
   • IFS Data Migration Manager: Validate data integrity during prototype testing.
   • IFS Connect: Test API endpoints for data sharing between domains.

Step 3: Confirm Data Sharing Agreements

Objective:

Establish clear agreements for how data products will be shared across domains.

Actions:

1. Define Data Contracts:
   • Specify what data is shared, who can access it, and under what conditions.
   • Example: Finance domain shares cost center data with Project Management, but only for approved projects.
2. Document Agreements:
   • Use the Enterprise Book of Rules to formalize contracts, including: 
     • Access controls (roles, permissions).
     • Usage policies (e.g., read-only vs. editable).
     • Audit trails for compliance.
3. Test Data Sharing:
   • Simulate cross-domain workflows (e.g., procurement requesting budget data from finance).
   • Validate that IFS Cloud security and access controls enforce agreements.

Step 4: Establish Lineage and Metadata Processes

Objective:

Ensure transparency and traceability for all data products.

Actions:

1. Implement Lineage Tracking:
   • Use IFS Cloud’s built-in tools to map data flows from source to consumption.
   • Example: Track how raw production data transforms into a «Shop Floor Efficiency» dashboard.
2. Metadata Management:
   • Tag data products with ownership, quality scores, and business context.
   • Example: Metadata for a «Supplier Performance» product includes the domain owner, update frequency, and linked SLAs.
3. Automate Metadata Updates:
   • Configure the Data Catalog to auto-populate metadata from prototype tests.

Step 5: Test the Governance Model

Objective:

Validate that federated governance processes work in practice.

Actions:

1. Simulate Governance Scenarios:
   • Test exception handling (e.g., data quality issues, access requests).
   • Example: Trigger an alert if inventory data fails validation and escalate to the domain owner.
2. Review Roles and Responsibilities:
   • Confirm that domain data owners, stewards, and technical teams understand their roles in governance.
   • Use the IFS Project Organization structure to assign accountability.
3. Tools to Use:
   • IFS Security Tools: Validate role-based access and compliance.
   • Dashboards: Monitor governance KPIs (e.g., compliance rate, security incidents).

Step 6: Iterate and Refine

Objective:

Incorporate feedback and prepare for Phase 3 (Establish Solution).

Actions:

1. Gather Stakeholder Feedback:
   • Conduct reviews with domain owners and technical teams.
   • Example: Adjust validation rules if users report false positives in data quality checks.
2. Update Documentation:
   • Revise the Enterprise Book of Rules and Data Tracker based on prototype results.
3. Plan for Phase 3:
   • Identify gaps (e.g., missing APIs, additional training needs) and address them in the next phase.

Key Tools and Resources

Success Metrics for Phase 2

Common Risks and Mitigation

Next Steps: Transition to Phase 3

• Implement Full Product Specs: Deploy validated data products with automated governance.
• Train Teams: Conduct sessions on data product ownership and self-service tools.
• Prepare for Go-Live: Finalize compliance checks and performance monitoring.

Conclusion

Validating product definitions in prototypes is foundational to a successful IFS Cloud Data Mesh implementation. By following this guide, you ensure that your data products are robust, governable, and aligned with business needs  -  setting the stage for scalable, domain-driven data management.

Ready to implement?

Book a Free IFS Cloud Data Mesh Consultation or Download the Phase 2 Checklist. Let’s ensure your prototypes are production-ready!