In 1999, Hershey’s celebrated ERP go-live turned into a Halloween horror story. Rushed configurations and siloed training left the confectioner unable to ship an estimated US $100 million in confirmed orders and shaved 8 percent off its share price overnight. Customers had chocolate on back-order; investors had heartburn. The root cause wasn’t SAP’s code - it was fragmented decision-making during implementation. (FinanSys)

The ERP Paradox

Enterprise suites promise an integrated “single source of truth,” but many implementations turn into siloed units - finance modifies one module, supply chain another, HR a third. Integration, it appears, is more about organisational discipline than a technological feature; even the most robust code base can still break down when teams isolate themselves.

Enter Data Mesh - Autonomy and Adhesive

Zhamak Dehghani’s Data Mesh framework embraces domain autonomy - data as a product owned by the people who know it best - but it also insists on two enterprise-wide binders: self-serve data infrastructure and federated computational governance. Think of them as the “integration bus” that keeps a distributed analytics estate from splintering exactly the way many ERPs have. (ontotext.com)

ERP Pitfalls vs. Data Mesh Risks - and the Governance Antidote

Proof in the Field

• ING Bank utilised an eight-week Data Mesh proof-of-concept to enable domain teams to build their own chat-journey data products on a governed, self-serve platform, thereby accelerating time-to-market for new insights while maintaining compliance. (Thoughtworks)

• Intuit surveyed 245 internal data workers and found nearly half their time lost to hunting for owners and definitions in a central lake. Their Mesh initiative reorganised assets into well-described data products, cutting discovery friction and sparking a “network effect” of reuse across thousands of tables. (Medium)

These early adopters report shorter model-validation cycles, lower duplicate-storage spend, and more transparent audit trails - outcomes eerily similar to what successful ERP programs aimed for but rarely achieved.

Four Steps to Build Mesh-Ready Governance

1. Codify the contract. Publish canonical event and entity models (customer, invoice, shipment) with versioning and SLA dashboards visible to every team.

2. Automate policy as code. Inject lineage capture, PII masking, and quality gates into every pipeline - no opt-out, no manual checkpoints.

3. Create integration champions. Rotate enterprise architects or senior analysts into each domain squad to act as diplomats for cross-team reuse.

4. Measure the mesh, not the modules. Track lead time from data request to insight, re-work hours saved, and incident MTTR. Celebrate improvements to the network, not just local deliverables.

Board-Level Takeaway

Domain autonomy without enterprise glue is a recipe for déjà vu - yesterday’s ERP silos reborn in cloud-native form. Treat federated governance as critical infrastructure, fund it like an R&D platform, and hold leaders accountable for both local agility and global coherence.

Call to action: At your next exec meeting, list the three datasets underpinning your highest-stakes AI initiative. If none has (1) a named product owner, (2) a published contract, and (3) automated policy enforcement, your “unified” future is already fragmenting. Invest in the strands before the system snaps.

1. Introduction to IFS Cloud and Master Data Management

IFS Cloud: Modular, Composable, and API-Driven

IFS Cloud is a next-generation enterprise resource planning (ERP) platform designed to meet the evolving needs of modern organizations. Its architecture is fundamentally modular, allowing organizations to deploy only the components they need - such as finance, supply chain, HR, CRM, and asset management - while maintaining seamless integration across business functions. This modularity is underpinned by a composable system, where digital assets and functionalities can be assembled and reassembled as business requirements change. The platform’s API-driven approach, featuring 100% open APIs, ensures interoperability with third-party systems and supports agile integration strategies. This enables organizations to extend, customize, and scale their ERP landscape efficiently, leveraging RESTful APIs, preconfigured connectors, and support for industry-standard data exchange protocols (EDI, XML, JSON, MQTT, SOAP) .

The Role of Master Data Management (MDM) in IFS Cloud

Master Data Management (MDM) is central to IFS Cloud’s value proposition. MDM ensures that critical business data - such as customer, supplier, product, and asset information - is accurate, consistent, and governed across all modules and integrated systems. By establishing a single source of truth, MDM eliminates data silos, reduces redundancies, and enhances operational efficiency. This is particularly vital in complex ERP environments, where data is often scattered across multiple applications and departments. MDM in IFS Cloud supports regulatory compliance, improves decision-making, and streamlines operations, making it a foundational element for any data-driven enterprise .

2. Understanding Data Contracts in Modern Data Governance

What Are Data Contracts?

Data contracts are formal agreements between data producers (e.g., application teams, business domains) and data consumers (e.g., analytics, reporting, or downstream systems). These contracts specify the structure, semantics, quality, and service-level expectations for data exchanged between parties. They define schemas, metadata, ownership, access rights, and quality metrics, ensuring that both producers and consumers have a shared understanding of the data .

Purpose and Benefits of Data Contracts

• Formalization of Data Exchange: Data contracts clarify what data is provided, in what format, and under what conditions, reducing ambiguity and miscommunication .
• Data Quality and Reliability: By specifying quality standards (e.g., accuracy, completeness, timeliness), contracts ensure that data consumers receive trustworthy data, which is critical for analytics and operational processes .
• Accountability and Governance: Contracts assign clear ownership and stewardship, making it easier to trace issues and enforce data governance policies .
• Compliance and Security: By defining access rights and usage policies, data contracts help organizations comply with regulatory requirements and protect sensitive information .
• Scalability and Efficiency: Standardized contracts reduce integration costs and support the scaling of data products across distributed teams and systems .

3. Relationship Between Master Data Management and Data Contracts

MDM as the Foundation for Data Contracts

MDM provides the authoritative, standardized data that forms the basis for effective data contracts. By ensuring a single source of truth, MDM eliminates inconsistencies and enables organizations to define contracts on top of reliable, governed data assets .

Layering Data Contracts on MDM

• Enforcing Data Quality and Security: Data contracts can be layered atop MDM to specify and enforce data quality metrics, validation rules, and security requirements for data shared between ERP modules or with external partners.
• Interoperability: Contracts define the interfaces and data formats for exchanging master data, ensuring seamless integration across heterogeneous systems and supporting interoperability in complex ERP landscapes.
• Governance and Compliance: The combination of MDM and data contracts strengthens data governance by providing both the data foundation and the operational agreements needed to manage data as a strategic asset .

4. Data Domains in IFS Cloud: Structure and Examples

Concept and Structure of Data Domains

In IFS Cloud, data domains are logical groupings of data assets aligned with key business functions. The platform’s architecture is organized into tiers - presentation, API, business logic, storage, and platform - each supporting the definition and management of data domains. Components within IFS Cloud group related entities, projections, and business logic into coherent capability areas (e.g., General Ledger, Accounts Payable), enabling modular deployment and management .

Table: Example Data Domains in IFS Cloud

The IFS Data Catalog: Classification and Governance

The IFS Data Catalog is a key tool for classifying, indexing, and governing data assets within these domains. It automatically scans data sources, creates metadata catalog entries, and classifies information to support compliance and discoverability. The catalog provides a unified view of the data estate, enabling data stewards to manage data assets effectively and ensure alignment with governance policies .

5. Implementing Data Mesh in ERP Systems Using IFS Cloud Data Domains

Core Principles of Data Mesh

Data Mesh is a paradigm shift in data architecture, emphasizing:

1. Domain-Oriented Ownership: Data is owned and managed by the business domains closest to its source and use .
2. Data as a Product: Each data set is treated as a product, with clear interfaces, quality standards, and product owners .
3. Self-Serve Data Infrastructure: Platform teams provide tools and infrastructure that enable domain teams to build, deploy, and operate their own data products .
4. Federated Computational Governance: Governance is distributed but coordinated, ensuring consistency, security, and compliance across domains .

Using IFS Cloud Data Domains as the Foundation

IFS Cloud’s modular, domain-aligned architecture is ideally suited for Data Mesh:

• Domain Teams: Assign ownership of data domains (e.g., Customer, Supplier) to business units or cross-functional teams, making them responsible for the quality, lifecycle, and delivery of their data products .
• Data Contracts as Product Interfaces: Use data contracts to define the structure, quality, and access policies for each data product, ensuring reliable and governed data exchange within and across domains .
• Self-Serve Infrastructure: Leverage the IFS Data Catalog and API-driven platform to enable discoverability, access, and integration of data products by other teams or external partners .
• Federated Governance: Implement governance policies that are enforced both centrally (e.g., compliance, security) and locally (e.g., domain-specific quality metrics), using the catalog and contracts as operational tools .

Diagram: Data Mesh with IFS Cloud Data Domains

[Customer Domain]---[Data Contract]---\
[Supplier Domain]---[Data Contract]----> [Data Catalog & Self-Serve Platform] <---[Consumer: Analytics, Reporting, External APIs]
[Product Domain]----[Data Contract]---/

6. Case Studies and Practical Insights

Real-World Examples

• Saxo Bank: Saxo Bank adopted a data mesh architecture to modernize its data infrastructure, leveraging event-driven technologies and secure data mesh solutions. This enabled decentralized data ownership, improved operational efficiency, and enhanced data security .
• Siemens: Siemens has modernized its data infrastructure and analytics capabilities, moving towards decentralized data management and improved accessibility - key tenets of Data Mesh - by partnering with cloud and analytics providers .

Outcomes

Organizations implementing Data Mesh in ERP or similar environments report:

• Improved Agility: Decentralized ownership allows teams to respond faster to business needs.
• Data Democratization: Self-serve platforms and clear contracts make data more accessible and usable across the organization.
• Enhanced Governance: Federated governance ensures compliance and quality without stifling innovation .

Challenges and Best Practices

Key Challenges

• Data Silos and Shadow IT: Decentralization can lead to new silos if not managed with strong governance .
• Integration Complexity: Migrating and integrating legacy data with cloud ERP systems is complex and error-prone .
• Regulatory Compliance: Ensuring compliance in multi-tenant cloud environments requires robust controls .
• Cultural Resistance: Shifting to domain ownership and new governance models can face organizational pushback .

Best Practices

• Develop a Scalable Governance Plan: Establish clear policies, procedures, and tools for data quality, security, and compliance .
• Standardize Data Language: Use metadata and data catalogs to create a common understanding of data assets .
• Embed Governance in Daily Operations: Integrate governance into workflows, not as an afterthought .
• Continuous Monitoring and Improvement: Use KPIs and regular reviews to ensure ongoing data quality and compliance .
• Invest in Training and Change Management: Educate teams on new roles, responsibilities, and the value of data governance .

7. Conclusion

Implementing IFS Cloud Master Data as Data Contracts within a Data Mesh framework represents a powerful approach to modernizing data management in ERP systems. By leveraging IFS Cloud’s modular, API-driven architecture and robust MDM capabilities, organizations can establish reliable, governed data domains that serve as the foundation for domain-oriented data ownership and productization. Data contracts formalize the expectations and responsibilities around data exchange, enhancing data quality, reliability, and compliance.

When combined with Data Mesh principles - domain ownership, data as a product, self-serve infrastructure, and federated governance - this approach delivers tangible benefits: improved business agility, democratized data access, and robust governance. Real-world examples from organizations like Saxo Bank and Siemens demonstrate the transformative potential of this strategy.

As ERP environments grow in complexity and scale, adopting these modern data management practices is essential for organizations seeking to unlock the full value of their data, drive innovation, and maintain a competitive edge in the digital era.

For data architects, ERP professionals, and business leaders, the path forward is clear: embrace modular, governed, and product-oriented data management with IFS Cloud and Data Mesh to future-proof your enterprise data landscape.

Data domain mapping is often the silent saboteur of enterprise data governance programs. At first glance, defining domains seems like child’s play – just drawing boxes around related data. Yet when domains remain undefined or poorly mapped, governance efforts stall and falter. Many organizations overlook this critical foundation, and their governance initiatives suffer as a result.

When data domains are undefined, confusion reigns: no one is sure who owns what data, and governance can grind to a halt. Teams lack clarity on scope and responsibilities, making it nearly impossible to enforce policies or improve data quality. The remedy lies in organizing data into logical domains. Establishing clear domain groupings with assigned owners jumpstarts governance by bringing structure and accountability to an otherwise chaotic data landscape.

Key Benefits of Data Domain Mapping

1. Logical Groupings Simplify the Data Catalog: Data domains group related data logically, acting like large sections in a library for your enterprise information linkedin.com. By separating data into domains (often aligned to business functions like Finance, HR, Sales), you bring order to sprawling datasets rittmanmead.com. This logical grouping simplifies your data catalog structure, making it easier for users to find what they need rittmanmead.com. In short, domains provide a clear, high-level structure for otherwise siloed or disorganized data collections linkedin.com.

2. Clear Ownership and Accountability: Each domain is aligned with a specific business unit or function, which means that unit takes ownership of “its” data linkedin.com. This alignment establishes clear accountability. For example, the finance team owns finance data, the sales team owns sales data, and so on getdbt.com. Assigning domains by business area ensures that subject-matter experts are responsible for data quality and definitions in their domain rittmanmead.com. With designated domain owners, there’s no ambiguity about who manages and governs a given dataset – stewardship is baked in.

3. Beware the Hidden Complexity: Mapping data domains is not as easy as drawing boxes on an org chart. In fact, it’s one of the most underestimated challenges in data governance linkedin.com. Defining the right scope and boundaries for each domain – and getting consensus across departments – can take months of effort linkedin.com. What looks simple on paper often grows complicated in practice, as teams debate overlaps and definitions. It’s critical to recognize this hidden complexity early. Underestimating it can derail your governance program, turning a “beautiful idea on paper” into frustration linkedin.com. Patience and careful planning are required to navigate the domain mapping maze of decisions.

4. Scoped Governance for Quick Wins: The beauty of domain-driven mapping is that it lets you tackle data governance in manageable chunks. Rather than boiling the ocean, you can prioritize one or two domains to begin governance initiatives on a smaller, controlled scope linkedin.com. Focusing on a high-value domain (say, customer or finance data) allows you to implement policies, data quality checks, and catalogs in that area first, delivering quick wins to the business. This domain-by-domain approach is “elegant [and] manageable”linkedin.com – it builds momentum. By demonstrating success in a well-chosen domain, you create a template that can be rolled out to other domains over time. This incremental strategy prevents overwhelm and proves the value of governance early on.

5. Improved Discoverability and Team Autonomy: Organizing by data domains doesn’t just help users find data – it also empowers teams. A domain-oriented data architecture enhances discoverability by grouping data that naturally belongs together, allowing data consumers to know where to look. Moreover, because each domain team manages its own data assets, they gain greater autonomy to innovate within their realm. Modern decentralized data frameworks (like data mesh) highlight that giving domain teams ownership leads to faster, more tailored solutions – with data made “easily consumable by others” across the organization getdbt.com. Teams closest to the data have the freedom to adapt and improve it, while enterprise-wide standards provide governance guardrails. In other words, domain mapping enables a balance: local autonomy for domain teams within a framework of central oversight. Federated governance models ensure that even as teams operate independently, they adhere to common policies and compliance requirements getdbt.com. The result is a more agile data environment where information is both discoverable and well-governed.

Conclusion – Structure for Success: Logical domain structures ultimately drive trust in data. When everyone knows where data lives and who stewards it, confidence in using that data soars. Clarity in domain ownership and scope unlocks fast governance wins by allowing focused improvements. In essence, the right structure silences the “silent saboteur” that undermines so many governance efforts. By mapping your domains, you take control of your data – and set the stage to master it.

Sources:

1. Charlotte Ledoux, “The Data Domains Map Enigma” – LinkedIn Post linkedin.com

2. Jon Mead, “How to Get a Data Governance Programme Underway... Quickly” – RittmanMead Blog rittmanmead.com rittmanmead.com

3. Daniel Poppy, “The 4 Principles of Data Mesh” – dbt Labs Blog getdbt.com getdbt.com

4. Daniel Poppy, “The 4 Principles of Data Mesh” (Federated Governance) – dbt Labs Blog getdbt.com

Introduction

Data is everywhere in modern organizations. Companies collect information from customers, sales, operations, and more. But as data grows, it becomes harder to manage and use. Traditional data systems often rely on one big, central team to handle everything. This can lead to slowdowns, confusion, and missed opportunities.

Data Mesh is a new way to solve these problems. Instead of putting all the responsibility on a single team, Data Mesh treats data as a product. It gives different business teams the power to own, share, and maintain their own data. These teams work together, following shared rules, to make sure data is useful, trusted, and easy to find. This approach helps organizations move faster, make better decisions, and get more value from their data .

Why Does It Matter?

Data Mesh matters because it helps organizations:

• Reduce bottlenecks in data delivery: When only one team manages all the data, requests pile up and everyone waits. Data Mesh lets teams work in parallel, so data moves faster to where it’s needed .
• Achieve higher data quality and trust: Teams that know the data best are responsible for it. This means fewer mistakes and more reliable information .
• Align data with business value: Data is managed by the people who use it every day. This ensures that data supports real business needs and goals .
• Build a scalable and agile data ecosystem: As the company grows, Data Mesh makes it easier to add new data sources and teams without slowing down .

How to Implement Data Mesh: Step-by-Step

Implementing Data Mesh is a journey. Here’s a simple, step-by-step guide to get started:

1️⃣ Define Vision and Align Strategy

• Assess your current state - pain points, tech debt, silos:
  Start by looking at how your data is managed today. Where are the slowdowns? Are there old systems or data silos that make things harder?
• Align with business objectives and outcomes:
  Make sure your data goals match your company’s big-picture plans. Data Mesh should help the business, not just IT.
• Secure strong executive sponsorship and funding:
  Get leaders on board. Their support and resources are key for success .

2️⃣ Identify Data Domains

• Break down your enterprise into business-aligned domains (e.g., Sales, Finance, Ops):
  Divide your company into logical areas, each with its own data needs.
• Assign clear ownership and accountability to each domain:
  Make sure every domain has a team responsible for its data.
• Focus on high-impact domains first for a phased rollout:
  Start where you’ll see the biggest benefits, then expand .

3️⃣ Form Cross-functional Data Product Teams

• Include data engineers, analysts, product owners, and business SMEs:
  Build teams with a mix of skills - technical and business.
• Empower teams with full lifecycle responsibility for their data:
  Teams should own their data from creation to sharing and maintenance.
• Promote a mindset of ownership, not just custodianship:
  Teams should treat data as a valuable product, not just something to store .

4️⃣ Define and Deliver Data Products

• Each product must have clear SLAs, metadata, lineage, and APIs:
  Set clear rules for how data is delivered, described, and accessed.
• Prioritize discoverability and reusability:
  Make it easy for others to find and use your data products.
• Establish feedback loops between producers and consumers:
  Listen to users and improve data products based on their needs .

5️⃣ Build a Self-Service Data Platform

• Provide tooling for data ingestion, transformation, governance:
  Give teams the tools to bring in, clean, and manage data themselves.
• Enable CI/CD pipelines, data observability, quality checks:
  Automate testing and monitoring to keep data reliable.
• Focus on developer experience and autonomy:
  Make the platform easy to use, so teams can work independently .

6️⃣ Apply Federated Computational Governance

• Set global policies: privacy, compliance, security:
  Create company-wide rules to keep data safe and legal.
• Define who governs what at central and domain levels:
  Decide which rules are managed by central teams and which by domains.
• Ensure automation over manual enforcement:
  Use automated tools to check and enforce rules, reducing human error .

7️⃣ Enable Data Discoverability

• Deploy a searchable data catalog (e.g., Alation, Collibra, Amundsen):
  Make it easy for everyone to find data products.
• Auto-register products, metadata, and ownership:
  Keep the catalog up to date automatically.
• Make it easy to find, understand, and trust data:
  Good catalogs help users know what data is available and how to use it.

8️⃣ Promote Cultural Shift & Training

• Upskill product owners and domain teams on product thinking:
  Teach teams how to manage data as a product.
• Foster a culture of sharing, curiosity, and accountability:
  Encourage teams to share data and learn from each other.
• Celebrate early adopters and internal case studies:
  Highlight successes to inspire others and build momentum .

Conclusion

Data Mesh is changing the way organizations manage and use data. By moving away from a single, central data team and empowering business domains, companies can deliver data faster, improve quality, and better support business goals. Each step - from defining your vision to building a self-service platform and applying federated governance - helps create a data ecosystem that is scalable, agile, and aligned with real business needs.

When teams own their data and work together, everyone benefits. Data becomes easier to find, trust, and use. The company can respond faster to new opportunities and challenges. By following these steps, you can build a Data Mesh that unlocks the full value of your data and supports your organization’s success now and in the future.

Real-World Example:
Companies like Saxo Bank, Gilead, and PayPal have adopted Data Mesh to break down data silos, improve data quality, and speed up data delivery. These organizations have seen better collaboration, faster insights, and more business value from their data .

This overview is designed to help you understand Data Mesh and start your journey toward a more effective, scalable, and business-aligned data ecosystem.