Executive Summary
Data Mesh is a decentralized way of managing data. It treats data as a product and makes domains responsible for their own data. Combined with IFS Cloud’s project methodology, it creates a framework for strong governance and scalable data management across business domains.
This approach replaces centralized control with a federated model. Business domains own and manage their data products while following shared governance standards.
Core Principles for IFS Cloud
Domain ownership
- Map IFS functional modules to business domains during project scoping
- Set boundaries by process areas such as Supply Chain, Finance, Projects, HR
- Assign each domain responsibility for its own data products
- Align enterprise structure with IFS Cloud domain design
Data as a product
- Design products with clear service levels and quality measures
- Products must be discoverable in the IFS data catalog
- Expose them through REST APIs and OData
- Define validation rules and full metadata
- Document specifications in the Book of Rules
- Set quality metrics in the Data Tracker
- Align SLAs with IFS Cloud capabilities
Self-serve platform
- Use IFS Cloud’s built-in integration and migration tools
- IFS Connect for services and protocols
- REST API with OData for direct access
- Data Migration Manager and Excel Add-in for processing
- Support data sharing across domains with IFS Connect
Federated governance
- Apply governance through the IFS project organization
- Define rules in the Enterprise Book of Rules
- Use IFS Cloud security and access controls for compliance
Implementation Phases
Phase 0: Define Project and Scope
- Mapping IFS Functional Modules to Business Domains
- Define governance structure
- Set data product vision
Phase 1: Initiate Project
- Form a Data Governance Committee with domain reps
- Create the Enterprise Book of Rules
- Define ownership and quality standards
- Deliverables: Domain Architecture, Governance Charter, Draft Catalog
Phase 2: Confirm Prototype
- Validate product definitions in prototypes
- Confirm data sharing agreements
- Establish lineage and metadata processes
- Test governance model
Phase 3: Establish Solution
- Implement full product specs
- Deploy self-service tools
- Automate governance policies
- Configure catalog for discovery
- Set up APIs, dashboards, and security
Phase 4: Implement Solution
- Train teams on data product ownership
- Test end-to-end workflows
- Validate compliance and readiness
Phase 5: Go Live
- Activate production data products
- Monitor performance and governance
- Set up lifecycle management
Data Governance Framework
Committee
- Executive sponsor (often CDO)
- Domain data product owners
- Technical platform team
- Data stewards
Processes
- Automated validation and monitoring dashboards
- Exception handling and quality reporting
- Federated access controls, tagging, and audits
Technology
- IFS Connect for integration
- REST API with OData
- Built-in migration and security tools
- Data catalog, pipeline automation, quality monitoring, and metadata management
Roadmap
Months 1–3
- Build governance structure
- Define domains
- Set up initial framework
- Train core team
Months 4–8
- Deploy 1–2 pilot products
- Enable self-service
- Validate governance and architecture
Months 9–12
- Expand to all domains
- Add advanced analytics
- Optimize governance and performance
- Build continuous improvement loop
Success Measures
Data Products
- Time to release new products
- Adoption rate
- Quality scores
- User satisfaction
Governance
- Compliance rate
- Security incidents
- Process efficiency
- Domain autonomy
Business Value
- Faster decisions
- Lower data management costs
- Better data access
- Higher analytics adoption
Risks and Mitigation
Technical
- Integration complexity → phased rollout and proofs of concept
- Performance issues → good architecture and monitoring
- Security gaps → federated model and regular audits
Organizational
- Resistance to change → training and change management
- Lack of resources → proper staffing and external support
- Weak governance adoption → executive sponsorship and clear roles
Conclusion
Using Data Mesh within IFS Cloud projects creates a modern data model that balances domain ownership with enterprise governance. It makes use of IFS Cloud’s native tools while building a federated architecture for agility and decision-making.
Success depends on strong leadership, change management, and a phased rollout. By combining IFS methodology with Data Mesh, organizations can deliver a working ERP and a scalable data foundation for future growth.