Executive Summary
A Data Mesh is a decentralized approach to managing data. It treats data as a product, making 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, adhering to 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 and 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
- Crafting a Data Product Vision
Phase 1: Initiate Project
- Creating a Data Governance Committee for IFS Cloud Data Mesh
- 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 the 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 a governance structure
- Define domains
- Set up initial framework
- Train the 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 a 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
Examples
How to use Data Mesh in procurement processes in IFS Cloud?
Conclusion
Using Data Mesh within IFS Cloud projects creates a modern data model that balances domain ownership with enterprise governance. It leverages IFS Cloud’s native tools to build a federated architecture that enhances 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.
FAQ
What is Data Mesh?
Data Mesh is a decentralized approach to data management. It treats data as a product and shifts responsibility from central teams to business domains. This approach uses four core principles. Domain ownership, data as a product, self-serve data platform, and federated governance.
Why is Data Mesh important for IFS Cloud?
Data Mesh is important for IFS Cloud because it aligns with the platform’s modular, domain-driven design. It enables business domains to own and manage their data products, ensuring better data quality, faster access, and more agile decision-making.
What are the four core principles of Data Mesh?
The four core principles are domain ownership, data as a product, self-serve data platform, and federated governance. These principles work together to create a scalable, decentralized data ecosystem.
How do you implement Data Mesh in IFS Cloud?
Implementation involves several phases. Defining project scope, initiating the project, confirming prototypes, establishing the solution, implementing the solution, and going live. Each phase includes specific tasks such as validating product definitions, deploying self-service tools, and training teams.
What is federated governance in Data Mesh?
Federated governance means applying governance standards across domains while allowing each domain to manage its own data products. It ensures consistency, security, and compliance without centralizing control.
What tools does IFS Cloud provide for Data Mesh?
IFS Cloud offers tools such as IFS Connect for integration, a REST API with OData for data access, Data Migration Manager for data processing, and built-in security and access controls for compliance.
How do you measure the success of a Data Mesh implementation?
Success is measured by metrics such as time to release new data products, adoption rate, quality scores, user satisfaction, compliance rate, security incidents, process efficiency, and domain autonomy.
What are the risks of implementing Data Mesh, and how can they be mitigated?
Risks include integration complexity, performance issues, security gaps, resistance to change, lack of resources, and weak governance adoption. Mitigation strategies include phased rollouts, good architecture, regular audits, training, proper staffing, and executive sponsorship.