A Data Mesh is a decentralized approach to managing data that treats it 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.
This approach replaces centralized control with a federated model. Business domains own and manage their data products while adhering to shared governance standards.
Build governance structure, define domains, set up initial framework, and train the core team.
Deploy 1 – 2 pilot products, enable self-service, and validate governance architecture.
Expand to all domains, add analytics, optimize governance, and build improvement loops.
Technical: Integration complexity & performance gaps.
Fix: Phased rollout & monitoring.
Organizational: Resistance to change & weak governance.
Fix: Executive sponsorship & training.
In modern enterprise ERP implementations, accurately mapping functional modules to business domains is foundational to project success — especially when implementing advanced architectural paradigms like Data Mesh.
The following outlines a structured approach to achieve this alignment during project scoping, highlights key business domains typically involved in IFS mapping, and proposes essential tools to facilitate the implementation.
The IFS Implementation Methodology provides a comprehensive framework for detailing the scope of functional modules through distinct project phases.
Collaboration between the IFS delivery team and customer to define high-level business domains. Key processes are mapped into the IFS Scope Tool, and foundational governance is documented in the Enterprise Book of Rules.
Development of a prototype covering 40 – 50 main end-to-end processes. Collaborative workshops refine the scope to ensure alignment between modules and domain requirements, maximizing adherence to IFS best practices.
Building upon the prototype with additional scenarios. Detailed documentation for configurations, reports, interfaces, and modifications (CRIM objects) is prepared to ensure modules comprehensively support business domains.
Facilitates decentralized data ownership. Each domain manages its data autonomously while interoperating within the unified IFS solution, fostering agility and governance.
Enterprises generally recognize a set of core business domains that serve as the natural structuring units for mapping IFS modules:
Central for documenting and refining scope. Enables process modeling and generation of the Book of Rules.
Captures governance, structure, and operational prerequisites as a master reference document.
Manages Configurations, Reports, Interfaces, and Modifications to ensure alignment with domains.
Project Tracker and Calculator for resource allocation and risk management.
Supports profiling and cleansing. Essential for addressing data domains in the context of Data Mesh.
Visual oversight of domain coverage, usage, training status, and open issues.
Mapping IFS functional modules to business domains involves a systematic methodology supported by powerful tools. Leveraging these capabilities enables solution architects to deliver cohesive solutions aligned with business domains, empowering decentralized data ownership through Data Mesh principles.
References: IFS Implementation Methodology, Scope Tool, Enterprise Book of Rules, Solution Architect guidelines, IFS PM Handbook for Partners, Data Mesh frameworks
A governance structure is the backbone of any successful IFS Cloud Data Mesh implementation. It ensures complex data projects run smoothly with clear accountability and consistent standards.
In IFS Cloud, governance shifts from a fully centralized model to a federated approach. Business teams manage their own data but operate within company-wide rules. This balance fosters innovation while maintaining compliance and security.
The goal is to empower teams to manage their data independently while ensuring alignment with company rules. Standards like data contracts and compliance policies tie everything together, creating a cohesive framework.
Solution architects and stakeholders define data ownership, initial rules, and domain boundaries.
Committees ensure data processes and contracts meet expectations and legal requirements.
Governance processes support ongoing quality, compliance, and change management during operations.
Provide strategic direction and keep business units aligned.
Outline processes and rules for risk and change management.
Tools like IFS Scope Tool and Data Catalog enforce rules in real-time.
Federated governance allows business units to manage their data according to shared standards while central teams oversee compliance. This approach offers several benefits:
Business units adjust data management practices to meet their needs, accelerating decision-making.
Company-wide security and compliance standards apply to all data, regardless of its source.
Shared tools, such as data catalogs and APIs, ensure consistency and reduce manual effort.
Ensure company-wide support and resource allocation (e.g., CDO, CIO).
Set company-wide standards and maintain alignment across teams.
Accountable for the quality, security, and compliance of their domain data.
Oversee the design and improvement of specific data products.
Handle daily operations, cataloging, and documenting data.
Provide tools and automation to enforce governance standards.
Aligns practices and resolves cross-team issues, ensuring smooth collaboration and compliance.
In IFS Cloud Data Mesh, governance roles and processes work together to give business teams the control they need to innovate while ensuring critical rules are never overlooked. This balance supports scalability, compliance, and rapid innovation.
The Challenge: Many ERP implementations fail to deliver the promised 414% ROI because data is treated as a byproduct, not an asset.
The Phase 0 Solution: Before technical deployment, you must define a Data Product Vision. This means shifting from centralized data lakes to domain-oriented ownership (Manufacturing, Finance, Asset Mgmt) where data is packaged, managed, and served like a product.
The Outcome: By establishing governance, SLAs, and ownership early, organizations unlock specific IFS Cloud benefits: 15% cost reduction in maintenance, 50% faster decision-making, and an 11-month payback period.
Setting a clear data product vision in Phase 0 forms the foundation for successful IFS Cloud Data Mesh implementation. This foundational phase ensures data gets treated as a product while aligning with decentralized, domain-oriented data mesh principles and supporting specific IFS Cloud business objectives.
A data product vision defines the purpose, value, and expectations for data products within an organization. It transforms thinking from viewing data as a byproduct of business operations to recognizing it as a valuable asset that drives decision making, innovation, and operational efficiency.
Define how data products achieve business objectives. Organizations implementing IFS Cloud typically target 414% three-year ROI and $5.5 million average annual benefits.
Establish measurable expectations. Data products must meet specific Service Level Agreements (SLAs) to remain trustworthy.
Make data products discoverable via catalogs and APIs.
Example: Global manufacturers use role-based catalogs where plant managers see local metrics while executives see consolidated dashboards.
Assign domain accountability.
Security and Audit trails.
Pharma Example: Formula-based modules require data products with complete lot traceability and batch tracking for FDA compliance.
Connect the data product vision directly with strategic business objectives through measurable outcomes.
Track data product adoption rates and business impact:
The Problem: Implementing IFS Cloud without a formal governance structure leads to «Data Swamps» — where data quality degrades, domain ownership is unclear, and the «Single Source of Truth» becomes a myth. 60% of ERP delays are caused by poor data readiness.
The Solution: A Federated Data Governance Committee. Unlike old-school centralized control (which becomes a bottleneck), this committee empowers business domains (Finance, Manufacturing, Service) to own their data products while adhering to global standards set by the committee.
The Payoff: Establishing this committee in Phase 0 results in:
30% faster data migration due to clear decision authority.
Higher user adoption as data trusts are established early.
Regulatory compliance (GDPR, SOX) built into the design, not patched later.
A Data Governance Committee provides the essential oversight required for successful IFS Cloud Data Mesh implementations. It acts as the legislative branch of your ERP ecosystem, bringing together representatives from each business domain to make binding decisions about data standards, compliance, and quality.
In the context of a Data Mesh architecture, the role of the committee shifts from «Command and Control» to «Federated Governance.» In a traditional monolithic ERP setup, IT often tried to police every data entry field. In a modern IFS Cloud implementation, the Committee sets the «Rules of the Road» (Interoperability standards, Security policies, Syntax requirements) while allowing the individual drivers (Business Domains) to navigate their own vehicles.
The objective is to balance central control — necessary for consolidated financial reporting and global supply chain visibility — with domain autonomy, which allows the Manufacturing team to optimize their shop floor data without waiting for permission from the Finance department.
A successful Data Governance Committee is cross-functional by design. It cannot be an «IT-only» meeting. To work effectively within IFS Cloud, it requires the following tiered structure:
Who: CIO, CFO, COO, or CDO.
Responsibility: They do not debate column names. They approve the budget for data quality tools, resolve high-level conflicts (e.g., «Does Manufacturing or Sales own the Customer Delivery Date?»), and enforce adoption. Without visible support from this level, domain owners often deprioritize governance tasks.
Who: VP of Supply Chain, Plant Managers, Financial Controllers.
Responsibility: These represent the «Nodes» in the Data Mesh. They are accountable for the quality of the data *produced* by their domain. If the inventory data is wrong, the Supply Chain Domain Owner is responsible for fixing the root cause process, not IT.
Who: Senior Accountants, Master Schedulers, Maintenance Planners.
Responsibility: The «boots on the ground.» In IFS Cloud, they are often the ones configuring the Data Migration Manager (DMM) templates and validating migration results. They define the specific validation rules (e.g., «Vendor Tax ID is mandatory for EU suppliers»).
Who: Solution Architects, CISO, Legal Counsel.
Responsibility: Ensuring the «Mesh» holds together. Architects ensure that the Customer ID in CRM maps correctly to the Customer ID in Finance. Compliance officers ensure that PII (Personally Identifiable Information) in the HR module is handled according to GDPR/CCPA.
The committee cannot function if it is lopsided. A common failure mode is a Finance-dominated committee that imposes rigid structures on flexible Manufacturing processes. Each major business domain within the IFS Cloud footprint needs a seat at the table.
Common domains that must be represented include:
Don’t wait until User Acceptance Testing (UAT) to form this group. By then, the data structures are already configured. The committee should be formed in Phase 0 or the early Design Phase.
The committee creates the framework within which the domains operate. Their primary responsibilities include:
Setting data quality standards that apply across all domains. For example, defining the standard format for Addresses, Dates, and Units of Measure. Ensuring that «KG» is used consistently, not mixed with «Kgs» or «Kilograms.»
Approving the «Contracts» between domains. If Manufacturing needs data from Engineering, the Committee ensures that Engineering commits to providing that data with a specific Service Level Agreement (SLA) regarding timeliness and accuracy.
Reviewing compliance with security and regulatory requirements. In IFS Cloud, this translates to reviewing Permission Sets and Segregation of Duties (SoD) matrices to ensure no single user has dangerous levels of access.
Resolving ownership disputes. Who owns the «Customer Master»? Is it Sales (who bring in the customer) or Finance (who bill the customer)? The committee acts as the supreme court for these jurisdiction battles.
Governance is a process, not an event. A typical rhythm for an active implementation includes:
How do you know if the committee is working?
The Committee does not operate in a vacuum; it operates within the software. The most effective committees utilize native IFS Cloud capabilities to enforce their decisions.
The committee approves the Migration Jobs and Validation Rules within DMM. This tool is the primary «gatekeeper» ensuring legacy data meets the new standards before it enters the production environment.
Governance should be visible. Build specific «Data Quality Control Tower» Lobbies. These dashboards display real-time metrics on incomplete records, duplicate customers, or missing mandatory fields, giving the committee a live view of the system’s health.
Automate governance. Use Business Process Automation (BPA) to prevent users from entering bad data. For example, configure a BPA to block the release of a Purchase Order if the Supplier lacks a valid insurance certificate.
Content Validation: This guide aligns with IFS Cloud implementation methodology and standard Data Mesh principles (Zhamak Dehghani). It provides actionable steps for forming a Data Governance Committee, emphasizing the distinction between Strategic (Steering), Tactical (Domain), and Operational (Steward) roles, and integrates specific IFS Cloud tooling references.
Definition and Core Principles Data mesh reimagines data management by splitting ownership, giving each domain control over its own data, treating information as a product, and relying on federated governance and self-serve platforms. This breaks away from classic data lakes and warehouses, helping business teams drive quality, innovation, and responsiveness.12
The Real Shift—Not Just Tech, But Culture Moving to data mesh is not just a technical tweak. It flips corporate culture. Legacy architectures make data teams gatekeepers and force dependence on central IT. Data mesh pushes responsibility and innovation outward, letting business domain experts make—and sometimes break—new rules for their own data. This pressurizes organizations to boost training, redefine accountability, and accept local mistakes as a price for overall agility and stronger data democratization.34
Non-Obvious Impacts and Industry Voices
Data Mesh vs. Traditional Architectures: The Full Table
| Feature | Data Mesh (Decentralized) | Traditional Data Architecture (Centralized) |
|---|---|---|
| Ownership | Domain teams86 | Central IT/data engineering1 |
| Architecture | Distributed, federated6 | Centralized, monolithic1 |
| Data Management | Local pipeline/product control7 | Centralized governance and ETL1 |
| Access/Discovery | Self-serve, open cataloguing7 | Closed, request-based1 |
| Governance | Federated, local adaptability6 | Top-down, rules-heavy1 |
| Observability | Multi-domain, granular toolset needed9 | Central data monitoring1 |
| Scaling | Modular, parallel5 | Dependent on platform redesign1 |
| Agility | High, enables mistakes4 | Slow, cautious, preserves order1 |
| Risks | Ownership confusion, silo resurgence4 | Bottlenecks, slow change, underused expertise3 |
Hidden Angles and Strategic Implications True transformation in data mesh is about more than toolsets or workflows. It forces organizations to rethink what data means, who owns it, and how value gets created and measured. While mesh unlocks speed and local innovation, it also requires tough, ongoing governance conversations, more nuanced compliance strategies, and a readiness to tolerate chaos and ambiguity while new systems bed in.16
Leaders must champion not just technology but organizational learning. Mesh can amplify voices closest to business outcomes and create a culture where discovery, failure, and reinvention are normal. This advantage comes with the newfound risk of fragmentation, duplication, and uneven accountability, making the role of data leadership and continuous community engagement more important than ever.104
Article covers definition, principles, business impacts, operational edge-cases, observability, governance, and non-obvious cultural tradeoffs. Cited diversified sources. Style matches clear, direct definitions with layered, insightful summary content.7111254
https://www.acceldata.io/blog/scaling-data-operations-why-data-mesh-is-the-future-of-data-management↩↩↩↩
https://www.precisely.com/blog/data-integrity/modern-data-architecture-data-mesh-and-data-fabric-101/↩↩
https://www.splunk.com/en_us/blog/learn/data-mesh.html↩↩↩↩↩↩↩↩↩↩↩↩
https://objectivegroup.com/insights/data-mesh-what-is-it-and-what-is-its-impact-on-data-architecture/↩↩↩↩↩
https://www.starburst.io/blog/10-benefits-challenges-data-mesh/↩↩↩↩↩
https://www.montecarlodata.com/blog-what-is-a-data-mesh-and-how-not-to-mesh-it-up/↩
https://kpmg.com/be/en/home/insights/2023/03/lh-the-impact-of-data-mesh-on-organizational-data.html↩
https://www.keboola.com/blog/data-mesh-architecture-through-different-perspectives↩
https://uk.nttdata.com/insights/blog/data-mesh-a-challenger-to-the-traditional-data-warehouse↩
A Strategic Framework for Data Mesh Governance and Scalable ERP Implementation.
Many ERP implementations fail or become "zombie systems" because they lack a clear governance framework. Organizations often struggle with:
Creating the Enterprise Book of Rules (EBoR) during an IFS Cloud implementation is not merely a documentation exercise; it is a foundational step that integrates company strategy, operational principles, financial controls, and governance within the technical ERP solution. The EBoR acts as the "Soul" of the implementation, ensuring that every configuration, from the Chart of Accounts to the Lead Time Calculation in SCM, is driven by a documented business rule rather than a technical whim.
In the context of IFS Cloud 25R1/25R2, where the shift to "Evergreen" (continuous updates) is mandatory, the Book of Rules becomes even more critical. It dictates how the organization handles new features and updates without breaking the core business logic. It leverages detailed templates and structured workshops to set prerequisites and standards tailored to the specific complexities of the customer’s business environment.
Central to the modern IFS methodology is the IFS Scope Tool. This is not just a project management spreadsheet; it is a sophisticated repository that maps the functional modules of IFS Cloud directly to the customer’s business domains. The Scope Tool serves as the bridge between the high-level Enterprise Book of Rules and the actual technical build.
The Scope Tool functions as a "Single Source of Truth" by capturing:
| Category | Functionality in EBoR | Impact on Implementation |
|---|---|---|
| Business Processes | BPA (Business Process Automation) Mapping | Aligns standard IFS processes with domain needs. |
| CRIM Objects | Customization & Reports Governance | Strictly controls "Scope Creep" by requiring justification. |
| Data Mapping | Migration Logic | Ensures legacy data meets the new Book of Rules standards. |
By maintaining alignment with the evolving Book of Rules, the Scope Tool ensures that when a change is made in the "Confirm Prototype" phase, its ripple effects across data governance and integrated domains are immediately visible and managed.
A significant advancement in modern IFS Cloud implementations is the incorporation of Data Mesh principles. Traditionally, ERP data was treated as a monolith managed by a central IT team. This created bottlenecks and "Data Swamps" where the context of information was lost.
Data Mesh introduces a decentralized approach by assigning ownership of "Data Products" to individual business domains. In IFS Cloud, this means the Finance Domain owns the Customer Master data, while the Production Domain owns the Routing and BOM data.
The EBoR formalizes this by defining Federated Computational Governance. Within this model, a central governance committee sets overarching policies (e.g., "All dates must follow ISO 8601"), while domain stewards are responsible for data quality, compliance, and operational readiness within their specific modules.
This phase is about setting the "Constitutional" framework. We move beyond simple project management and begin drafting the initial Enterprise Book of Rules using industry-specific templates and the findings from the sales cycle.
Key Activities:
In this phase, theory meets reality. We refine the Book of Rules through a series of "Conference Room Pilots" (CRP). We develop prototype processes to validate how data flows across domains. For example, how a Sales Order (SCM Domain) triggers a Financial Posting (Finance Domain) and whether the governance rules established in Phase 1 hold true.
Workshops: Intense sessions where the IFS Scope Tool is used to align business requirements with standard IFS functionality, identifying any necessary CRIM (Customization, Report, Integration, Modification) objects.
The "Build" phase. The Enterprise Book of Rules is extended with detailed solution designs. This isn't just about configuration; it's about Data Productization. Domain stewards work on data migration routines, ensuring that data being pulled from legacy systems is "cleansed" to meet the new Book of Rules standards.
Testing Strategy: Integration testing ensures that the federated governance model works. We test not just "does the button work," but "does the data ownership remain intact during this transaction?"
Preparing for the "Big Day." This phase focuses on the human element and technical finalization. The Book of Rules is finalized and used as the basis for End-User Training (EUT). We don't just teach users which buttons to click; we teach them the Rules of the system.
Readiness Checks: Final cutover plans, load testing of the OData APIs, and ensuring that domain stewards are fully trained to manage their data products once the system is live.
The system is live, but the methodology doesn't end. We transition to a state of centralized oversight and decentralized operation. The Enterprise Book of Rules becomes a living document, updated through a formal "Change Management" process whenever the business evolves or IFS releases a new update.
Continuous Improvement: Post-go-live audits ensure that domains are adhering to the rules and that the Data Mesh is functioning as intended.
Governance in this framework is deliberately federated. A common mistake in ERP projects is trying to control everything from the center, which leads to slow decision-making and business frustration. Conversely, no control leads to chaos.
Focused on the "Macro" level. They define the global data standards, integration protocols, and the overall architecture of the IFS Cloud environment. They own the "Master" Book of Rules.
Focused on the "Micro" level. They apply the global rules to their specific business context. If Finance needs a new sub-ledger, the Finance Domain Steward ensures it complies with the global Book of Rules before it is implemented.
The synergy between the Enterprise Book of Rules and Data Mesh principles, achieved through the disciplined IFS Implementation Methodology, results in more than just an ERP system. It creates a robust, scalable, and agile digital core.
By shifting to decentralized data ownership supported by a centralized governance model, enterprises can innovate and respond dynamically to changing business requirements without sacrificing compliance or operational excellence. In the era of Cloud ERP, the Book of Rules is not just a document—it is your competitive advantage.
In enterprise software implementations, such as those involving IFS Applications, clear definitions of ownership and quality standards are critical to project success and long-term solution sustainability. They form part of the governance and operational steering models that ensure both accountability and excellence in delivery and ongoing management.
Ownership refers to the assignment and acceptance of responsibilities for various elements of the project and solution throughout its lifecycle.
This well-articulated ownership framework reduces ambiguity, fosters engagement, and aligns the delivery organization with customer business goals.
Quality standards constitute the defined benchmarks for deliverables, processes, and product fitness to meet customer expectations and compliance needs.
Embedding these quality standards assures that the delivered solution not only meets initial requirements but remains sustainable and effective.
The complexities of modern enterprise data environments demand new paradigms like Data Mesh to complement traditional data ownership models.
Incorporating Data Mesh principles into the project fosters data democratization, enhances data ownership clarity, and embeds data quality as a foundational attribute of the implemented solution.
The IFS Cloud offering transforms traditional ownership and quality paradigms by leveraging cloud-native architectures and managed services.
The IFS Cloud implementation methodology adapts the traditional multi-phase approach with cloud-focused accelerators and operational safeguards:
This methodology enables customers to maximize the benefits of cloud agility while ensuring disciplined ownership and uncompromised quality standards.
Ownership and quality standards remain the twin pillars of successful enterprise software implementations, with evolving best practices adapting to innovations like Data Mesh and IFS Cloud. Combining domain-oriented data ownership with cloud shared responsibility models, supported by robust implementation methodologies, ensures that organizations can deploy, govern, and evolve their ERP solutions with confidence, security, and continuous value delivery.