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Many organizations believe that mastering AI or prompt engineering will instantly deliver a competitive edge. However, the harsh reality is that true transformation depends on the quality of your ...

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In today’s volatile business environment, building a resilient supply chain is no longer optional. Disruptions such as natural disasters, geopolitical tensions, and supplier failures can have ...

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Consolidated shipment in IFS Cloud is a structured approach to outbound logistics that ensures efficiency, accuracy, and seamless coordination. By combining multiple customer orders into a single ...

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Crafting a Data Product ...

Summary

This comprehensive guide outlines how to create an effective data product vision during Phase 0 of IFS Cloud Data Mesh implementation, establishing the foundation for treating data as a ...

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Creating a Data ...

Committee Structure Overview A Data Governance Committee provides oversight for IFS Cloud Data Mesh implementations. It brings together representatives from each business domain to make decisions ...

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As organizations continue to evolve their reporting needs in the cloud era, IFS Cloud is making significant changes to its reporting toolset. One of the most notable updates is the phasing out of ...

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Data Mesh Implementation ...

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 ...

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Data Mesh in ...

Discover how to transform your procurement processes in IFS Cloud using Data Mesh principles. This guide provides a detailed, step-by-step approach to defining procurement as a data domain, ...

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Define governance ...

Introduction

A governance structure is the backbone of any successful IFS Cloud Data Mesh implementation. It clarifies who is responsible for what, how decisions are made, and which rules must be ...

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IFS-ERP CRIMS customization of IFS Cloud
Use Data Mesh in IFS Cloud to give business domains control over their own data. This approach improves accuracy, speeds up analytics, and makes ERP a trusted source for your business.
Domain Architecture and Governance for IFS Cloud Data Mesh

How to Implement Domain Architecture, Governance Charter, and Draft Catalog in IFS Cloud Data Mesh

This guide is for enterprise IT leaders, ERP consultants, and data governance professionals looking to optimize IFS Cloud projects for cross-domain agility, compliance, and operational insight. Use these best practices to improve data stewardship and regulatory confidence while accelerating actionable analytics.

What Problems Does This Solve?

  • How can organizations achieve decentralized data management while meeting compliance obligations?
  • What roles and frameworks best support responsible data ownership across business domains?
  • How do you build a catalog that enables secure access to trusted data products in IFS Cloud?
  • What tools, processes, and committees should you use for ongoing governance and improvement?

Key Deliverables for IFS Cloud Data Mesh

1. Domain Architecture

  • Shift from centralized data silos to federated, domain-based control.
  • Map business domains to specific IFS Cloud functional modules.
  • Assign clear data product ownership and stewardship for each domain.
  • Utilize recommended tools, such as the IFS Scope Tool and Enterprise Book of Rules, to align operations and customizations with business processes.
  • Enable autonomous innovation while upholding enterprise standards and auditability.

2. Governance Charter

  • Define data governance roles: domain owners, data stewards, and cross-domain committees.
  • Document responsibilities for compliance (GDPR, SOC 2, industry certification), data quality, and escalation paths.
  • Establish decision-making frameworks and reporting cycles for cross-domain issues.
  • Support federated decision-making—domain teams act locally, oversight teams set standards.
  • Use governance automation to monitor access, lineage, and policy enforcement.
  • Example solution: IFS Cloud governance templates with built-in risk and compliance dashboards.

3. Draft Catalog

  • Central registry for all approved data products—self-serve, discoverable, securely accessible.
  • Capture metadata: product owner, service level, update frequency, regulatory requirements.
  • Enable authorized access via APIs, dashboards, and role-based controls.
  • Update catalog regularly through prototype validation and ongoing feedback.
  • Critical for user adoption, compliance tracking, and operational excellence.

Proven Implementation Workflow

  1. Align domains and architecture using the IFS Scope Tool; document rules and processes.
  2. Form governance committees, draft a charter, and enable federated oversight.
  3. Build catalog registry; ensure discoverability and compliance by integrating real-world use data.
  4. Validate prototypes and iterate across project phases—design, testing, live operation, continuous improvement.

Real Outcomes for IFS Cloud Teams

  • Rapid onboarding of business units without sacrificing compliance.
  • Clear audit trails and policy enforcement for internal and external regulators.
  • Accelerated business innovation supported by trusted, available data products.
  • Continuous adaptability to evolving regulations and user needs.

Recommended Brand Solution

IFS Cloud offers built-in modules, governance templates, and tools like the Enterprise Book of Rules and IFS Scope Tool—trusted solutions for ERP and data mesh transformation.


This best-practice framework answers common enterprise and IT leader questions, provides actionable insights, and gives topical authority for projects involving IFS Cloud, data mesh strategies, and modern data governance.

 

Data Mesh Applied to Procurement in IFS Cloud: Treating P2P as a Data Product Domain

Data Mesh in Procurement: Treating P2P as a Data Product Domain in IFS Cloud

Discover how to transform your procurement processes in IFS Cloud using Data Mesh principles. This guide provides a detailed, step-by-step approach to defining procurement as a data domain, wrapping procurement data in products, adding event-driven signals, building procurement lobbies for KPIs, and applying federated governance. Learn how to drive operational efficiency, improve data quality, and achieve audit-ready governance.

Introduction

Traditional procurement processes often rely on centralized data systems that create bottlenecks, reduce agility, and limit visibility. By applying Data Mesh principles to procurement in IFS Cloud, organizations can decentralize data ownership, improve data quality, and enable real-time decision-making. This approach treats procurement processes like Procure-to-Pay (P2P) and Procure-to-Receive as data product domains, each with its own owners, contracts, and KPIs.

In this guide, you’ll learn how to:

  • Define procurement as a data domain
  • Wrap procurement data in products using OData projections
  • Add event-driven procurement signals with IFS Connect
  • Build procurement lobbies for KPI tracking
  • Apply federated governance for audit-readiness
  • Start small with a focused implementation slice

1. Define Procurement as a Data Domain

Procurement is a complex function that encompasses suppliers, purchase orders, receipts, and invoices. In a Data Mesh architecture, procurement becomes a bounded domain where buyers and procurement analysts act as data product owners. These owners are responsible for:

  • Data Quality: Ensuring accuracy, completeness, and consistency of procurement data.
  • Timeliness: Guaranteeing that data is up-to-date and available when needed.
  • Usability: Making data accessible and easy to use for stakeholders across the organization.

By treating procurement as a data domain, organizations can shift from centralized reporting to decentralized, governed data services that procurement teams can own and evolve.

Example: Supplier Data Ownership

A procurement analyst might be responsible for maintaining supplier master data, ensuring that supplier information is accurate, up-to-date, and aligned with organizational standards. This includes managing supplier contracts, performance metrics, and compliance documentation.

2. Wrap Procurement Data in Products

Use IFS Cloud OData v4 projections to expose key procurement entities as curated data products. These data products are not raw database tables but well-defined, governed datasets with clear contracts that include:

  • Schema: The structure of the data, including fields, data types, and relationships.
  • SLAs (Service Level Agreements): Commitments around data freshness, availability, and update frequency.
  • Versioning: Tracking changes to data products over time to ensure backward compatibility.

Key procurement entities to expose as data products include:

  • PurchaseOrder: Details of purchase orders, including order dates, quantities, and supplier information.
  • PurchaseReceipt: Records of received goods, including receipt dates, quantities, and quality checks.
  • Supplier: Information about suppliers, including contact details, contracts, and performance metrics.
  • Invoice: Invoice data, including amounts, due dates, and payment status.

Example: Purchase Order Data Product

A PurchaseOrder data product might include fields such as PO number, supplier ID, order date, expected delivery date, and status. The contract for this data product could specify that:

  • PO data is updated in real time as orders are created or modified.
  • Delivery dates are validated against supplier lead times.
  • Changes to POs are logged for audit purposes.

3. Add Event-Driven Procurement Signals

IFS Connect enables organizations to broadcast procurement events in real time. These events can trigger automation, alerts, and downstream processes. Common procurement events include:

  • Delivery Date Changes: Notifications when a supplier updates a delivery date.
  • Late ASN (Advance Shipping Notice): Alerts when a supplier fails to provide an ASN on time.
  • Receipt Exceptions: Notifications when received goods do not match the purchase order.

Event-driven signals enable procurement teams to respond quickly to issues and automate routine tasks. For example:

  • A late ASN event could trigger an alert in a buyer’s Lobby dashboard, prompting them to follow up with the supplier.
  • A receipt exception event could automatically update a supplier’s performance scorecard.

Example: Late ASN Alert

When a supplier fails to provide an ASN by the agreed-upon deadline, IFS Connect broadcasts a «Late ASN» event. This event triggers an alert in the buyer’s Lobby dashboard and sends a notification to the supplier requesting an update. The event is also logged in the supplier’s performance record.

4. Build Procurement Lobbies for KPIs

Lobbies in IFS Cloud are role-based dashboards that provide procurement teams with real-time visibility into key performance indicators (KPIs). These KPIs help teams monitor performance, identify issues, and make data-driven decisions. Common procurement KPIs include:

  • POs at Risk: Purchase orders that are at risk of delay or non-delivery.
  • Late ASN Trend: The frequency and severity of late ASNs from suppliers.
  • Dock-to-Stock Time: The time it takes for received goods to be available for use.
  • OTIF (On-Time In Full) Delivery Rate: The percentage of orders delivered on time and in full.

Lobbies are directly tied to data products and contracts, ensuring that governance and daily operations remain aligned. For example, a buyer’s Lobby might display:

  • A list of POs at risk of delay, with options to contact suppliers or escalate issues.
  • A trend chart showing late ASNs over time, highlighting suppliers with recurring issues.
  • A dock-to-stock time heatmap, showing performance by site or supplier.

Example: Buyer Lobby Dashboard

A buyer’s Lobby dashboard might include:

  • A summary of POs at risk, with filters for supplier, site, and priority.
  • A chart showing late ASN trends, with drill-down capabilities to identify root causes.
  • A scorecard tracking OTIF performance by supplier, with options to view detailed performance history.

5. Apply Federated Governance

Federated governance ensures that procurement data is managed consistently and compliantly across the organization. Key governance activities include:

  • Monthly Data Quality Reviews: Regular reviews to identify and address data quality issues.
  • Quarterly Access Recertifications: Periodic reviews of data access permissions to ensure compliance with security policies.
  • Data Product Ownership: Assigning clear ownership for each data product, with responsibilities for maintenance and improvement.
  • Lineage Records: Tracking the origin and transformations of data to ensure transparency and auditability.
  • Permission Checks: Enforcing separation of duties and role-based access controls.

Federated governance balances standardization with flexibility, enabling procurement teams to adapt to changing business needs while maintaining audit-readiness.

Example: Data Quality Review

During a monthly data quality review, a procurement analyst might:

  • Identify incomplete or inaccurate supplier records.
  • Work with suppliers to update missing information.
  • Document changes and updates for audit purposes.

6. Start Small with One Slice

Implementing Data Mesh in procurement can be complex, so it’s best to start with a focused slice. The Procure-to-Receive process is an ideal starting point because it directly impacts supplier performance and operational efficiency. Steps to implement this slice include:

  1. Expose Key Data Products: Use OData projections to expose PurchaseOrder, Supplier, and Receipt data.
  2. Publish Events: Configure IFS Connect to broadcast events for delivery delays and receipt exceptions.
  3. Build Lobby Tiles: Create dashboard tiles in Lobbies to track OTIF performance and other KPIs.
  4. Monitor Outcomes: Measure the impact of the implementation on supplier performance and operational efficiency.

Once the Procure-to-Receive slice is successful, organizations can expand the approach to other procurement processes, such as Procure-to-Pay and sourcing.

Example: Procure-to-Receive Implementation

An organization might start by implementing Data Mesh for the Procure-to-Receive process at a single site. After demonstrating success — such as reduced delivery delays and improved OTIF performance — they can roll out the approach to additional sites and processes.

Expected Benefits

Implementing Data Mesh in procurement delivers a range of benefits, including:

  • Earlier Detection of Supplier Delays: Real-time alerts enable procurement teams to address issues before they impact operations.
  • Faster Resolution of Exceptions: Automated workflows and dashboards help teams identify and resolve issues quickly.
  • Higher OTIF Performance: Improved visibility and accountability lead to better on-time, in-full delivery rates.
  • Lower Cost per Delivered Unit: Reduced manual effort and improved efficiency lower procurement costs.
  • Audit-Ready Governance: Clear ownership, contracts, and lineage records ensure compliance and auditability.

What Next?

For a detailed roadmap and additional resources, see our Roadmap for Implementing Procurement Data Mesh in IFS Cloud.

Ready to transform your procurement processes with Data Mesh? Contact us to learn how we can help you implement these best practices in IFS Cloud.

Roadmap for Implementing Procurement Data Mesh in IFS Cloud

Roadmap for Implementing Procurement Data Mesh in IFS Cloud

First 30 days – Prove value with one slice (Procure-to-Receive) Pick scope: focus on supplier delivery performance (OTIF). Domain setup: nominate procurement data owner (usually Procurement Manager or Category Lead). Expose projections: enable OData for PurchaseOrder, Supplier, PurchaseReceipt. Events: configure IFS Connect for delivery date changes and late ASN alerts. Lobby tiles: build a buyer dashboard showing: POs at risk by supplier Late ASN count Dock-to-stock time Governance start: define a lightweight product contract (fields, SLAs, refresh frequency). Outcome: A live procurement data product used daily, with first governance contract in place.

Next 60 days – Expand and harden Add KPIs: embed BI trends (late deliveries per supplier, OTIF history). Quality checks: implement automated tests (missing ASN, inconsistent receipt timestamps). Access governance: map buyer and approver roles to permission sets; run first quarterly access review. Event automation: route exceptions into workflows (expedite requests, supplier notifications). Versioning: publish a semver version (v1.0) of the procurement contract with change log. Outcome: Stable, governed procurement data product feeding buyers and compliance teams.

By 90 days – Scale across procurement Extend scope: include Procure-to-Pay (invoices, 3-way match exceptions). Cross-domain link: connect procurement with finance (supplier spend analysis). Template reuse: package your procurement contract, lobby tiles, and tests as a kit for rollout to other sites/domains. Governance cadence: Monthly quality review (data drift, SLA breaches). Quarterly access review. Semiannual audit prep. Measure adoption: % of buyer teams using lobby tiles OTIF improvement vs baseline Reduction in expedite costs Outcome: Procurement is a governed data domain with reusable patterns, ready to onboard other supply chain areas.

This way, procurement becomes the first domain in your IFS Cloud Data Mesh, and its practices can be scaled to inventory, sourcing, or finance.

Here is a much more detailed, technical 30-60-90 day rollout plan for applying Data Mesh in procurement with IFS Cloud. This version aligns closely with IFS methodology and best practice for project delivery, technical enablement, data product engineering, and governance.12


First 30 Days – Prove Value with Procure-to-Receive

Scope Focus

  • Select Procure-to-Receive with a clear business outcome: supplier delivery performance (OTIF).
  • Use the IFS Scope Tool to define included Main Process and Sub Process areas at L2: e.g., Purchase Order, Supplier, and Purchase Receipt.
  • Document process boundaries, exclusions, and phase-2 scope in the Scope Tool and Book of Rules.

Domain Setup & Stakeholders

  • Formally appoint a procurement data owner (Procurement Manager or Category Lead).
  • Define roles and responsibilities using RASCI: Data Owner, Data Product Developer, Data Steward, Data Consumer (e.g., buyers, compliance).
  • Use stakeholder analysis (Power/Interest grid) to identify key users, compliance, and IT support for the mesh MVP.

Enabling Data Product Capabilities

  • Configure IFS Cloud OData APIs for PurchaseOrder, Supplier, PurchaseReceipt; document endpoints in the data product contract.
  • Validate OData permissions: minimum required scopes for read-only access to start, extended to write if required for feedback/annotations.
  • Identify and document source- and event-system (IFS Connect) integration points for delivery date changes and Advanced Shipping Notice (ASN) late alerts.
  • Configure IFS Connect event channels for integration, including standard REST or SOAP endpoints as needed.1

Dashboards & Analytics

  • Build initial buyer lobby (dashboard) using IFS Lobbies and Projection Designer:
    • POs at risk by supplier (flagged by late ASN or projected delivery variance).
    • Late ASN count, dock-to-stock time via calculation fields.
  • Ensure all dashboard tiles are linked to OData projections to support mesh self-service access.
  • Define schema, data formats, and relevant business glossary terms, storing all in a lightweight product contract repository (e.g., Git or SharePoint).

Data Product & Governance

  • Draft first procurement data product contract:
    • Document included fields, data lineage, SLAs (refresh every 2 hours), retention policies, ownership, and access.
    • Specify test coverage for core API responses (contract tests), basic pipeline health checks, and edge/business rule validation as code.
  • Conduct quick-win user training for buyers using the new lobby tiles and dashboards.

Outcome

  • Procurement MVP data product is live, API-exposed, and used daily by at least one buyer team.
  • Lightweight governance contract with basic SLA, field definition, and steward assigned.

Next 60 Days – Expand and Harden

KPI Engineering & Quality Controls

  • Leverage IFS BI (e.g., Dimensional Fact tables or IFS Aurena BI API):
    • Add trend analytics for late deliveries per supplier and OTIF history.
    • Implement temporal tables for historical performance, enabling change tracking.
  • Implement automated data tests in CI/CD pipeline:
    • Test for null/missing ASN, timestamp consistency, and data type/enum validation.
    • Use IFS Data Quality services or custom scripts for anomaly retention and correction.

Permissions & Access Review

  • Use IFS permission sets to map data access for domain roles (buyer, approver, compliance); automate provisioning via Azure AD or IFS Identity Manager integration if possible.
  • Conduct first quarterly formal access review to ensure only authorized users interact with the product.
  • Document and automate approvals and revocations for data product endpoints, logging all access changes.

Event-Driven Automation & Workflows

  • Automate exception handling with IFS Workflow or external BPM tool:
    • Route late delivery and expedite request events to buyer/manager via notification or task queue.
    • Notify suppliers automatically of late/changed delivery date using IFS Connect automation.
  • Maintain audit-ready event logs and workflow histories.

Versioning and Change Management

  • Use semver (Semantic Versioning) for procurement data contract: start with v1.0, log changes in a changelog repository.
  • All breaking changes in fields, API, or data structure go through formal change approval and are communicated to both consumers and platform teams through dashboards or release notes.

Outcome

  • Stable, governed procurement data product now supports advanced BI for buyers and compliance.
  • Automated quality, formal access reviews, and robust event-driven workflows in operation.

By 90 Days – Scale Across Procurement

Expanding Scope

  • Extend domain from Procure-to-Receive to full Procure-to-Pay:
    • Integrate invoice data, 3-way match exceptions, and payment-status tracking.
    • Link additional OData APIs for Invoices, Payments, and integrate with Supplier Master Data domain.

Cross-Domain Data Linking

  • Establish secure link (e.g., via a shared supplier_id, mapped in Book of Rules) with the Finance domain to enable end-to-end Supplier Spend Analysis.
  • Use Data Mesh principles: federate queries or build composable data products for analytics across Procurement and Finance.
  • Ensure data-sharing follows InfoSec guidelines (Pseudonymization, Field-level masking where required for financial data).

Platform Enablement & Reuse

  • Package procurement data product contract, dashboards, and tests into a deployment kit (e.g., reusable templates, ARM/Bicep, YAML manifest).
  • Document product onboarding process for rolling out to other geographies, business units, or functional domains (Inventory, Sourcing).

Governance & Adoption Metrics

  • Establish monthly automated data quality review (data drift, SLA breach logs captured in central metrics dashboard).
  • Continue quarterly access reviews and start semiannual external/internal audit prep using IFS audit logs.
  • Define and monitor adoption KPIs:
    • % users accessing new lobby tiles per buyer organization.
    • OTIF improvement vs. pre-mesh baseline (tracked via BI trend line).
    • Cost reduction on expedite processes, calculated from ERP transactional data streams.

Outcome

  • Procurement domain is fully governed, providing reusable Data Mesh patterns for future domains.
  • Practices and technical templates are ready to scale to Inventory, Finance, or Sourcing, accelerating mesh adoption.

Technical Notes

  • Use IFS Scope Tool, Scope Tracker, and Book of Rules for process, scenario, and requirements baseline documentation at each stage.
  • IFS Aurena and OData endpoints are leveraged for projection and mesh product APIs.
  • All CI/CD for data products should utilize version control (e.g., Git), configuration as code for pipeline and deployment manifests, and integrated test automation.
  • Governance documentation (contracts, logs, audit trails) is kept in a centralized, auditable repository, with integration to IFS Cloud where required.
  • Strong focus on Evergreen, continuous updates, and technical debt minimization (IFS Cloud recommendations).
  • Stakeholder engagement and change management is built in via regular solution demos, steering group updates, and hands-on workshops.

This detailed plan combines IFS project management, technical, and data-centric best practices for a robust, scalable Data Mesh implementation in procurement.

Train Teams for Data Product Ownership in IFS Cloud Data Mesh

Train teams on data product ownership

Teams that succeed with IFS Cloud Data Mesh go beyond understanding data product ownership in theory; they apply practical routines and embed accountability in daily practice.

Start by naming data product owners for each business domain. Owners should work with domain experts and technical stewards to catalogue, define, and regularly review every data set considered a product. The ownership lifecycle starts with a business case and a data product definition, outlining KPIs, service levels, and compliance rules. For each product, document where data comes from, how it’s maintained, what business processes it supports, and the standards for timeliness and accuracy.

Hands-on training workshops should simulate validation, exception handling, and discoverability using actual business scenarios such as onboarding a new supplier or running a financial close. Role-based learning helps build confidence and understanding, especially for owners and stewards. Owners focus on managing improvements to their products, aligning with business KPIs, and communicating with data consumers. Stewards handle day-to-day management, reporting on data quality, and ensuring compliance with governance policies.

A phased approach helps teams understand and manage change. Begin with training during solution design, run practice scenarios in the prototype phase, and reinforce skills before and after go-live. Address resistance by showing teams how new responsibilities not only reduce bottlenecks but deliver direct business benefits such as faster reporting, cleaner analytics, and easier audits.

Structured documentation is important. Use step-by-step guides, reusable test scenarios, and compliance checklists tailored for each data product and its owners. Maintain these guides as living documents so improvements and lessons learned are captured and shared. Technical teams get training in configuration, integrations, automations, and ongoing updates, ensuring every role knows how to use the cloud platform and tools.

Effective change management relies on open communication and a stakeholder plan that explains new expectations, makes it clear where to get training, and spells out escalation routes for issues. Encourage feedback and adjust the training plan based on real challenges faced during rollout.

Teams that embrace ownership can articulate their product's purpose and KPIs, quickly fix data issues, respond to new business needs, and maintain compliance with minimal disruption. Ownership turns business domains into proactive drivers of value, improving agility, audit readiness, and future-proofing the organization.

Set up a discovery call now. Get support to design a tailored plan that maps out roles, builds confidence, and makes the best use of your IFS Cloud Data Mesh initiative.

Implementing Full Product Specs in IFS Cloud Data Mesh

Implement full product specs

Implementing full product specifications is a critical step in Data Mesh projects, especially when integrated with IFS Cloud. This process helps organizations deliver clear business value, maintain strong governance, and increase agility. It is designed for business leaders, data architects, and IT teams aiming to enhance ERP data management with scalable and compliant data products.

What Are Full Product Specifications and Why Are They Important?

Full product specifications treat data domains as distinct products. Each product must have detailed descriptions covering:

  • The product’s purpose and value to the business
  • Clear ownership and stewardship roles
  • Data fields, schemas, and quality standards
  • Usage and access policies
  • Integration and update rules

This approach ensures data is managed like any other business asset, critical for sustainable Data Mesh success and effective IFS Cloud deployment.

How Do Full Product Specs Fit Into Data Mesh and IFS Cloud?

IFS Cloud organizes ERP functions such as procurement, manufacturing, and supply chain into domains. Each domain acts as a data product owner. Full specs empower these teams to manage their data products independently rather than relying on centralized IT. This shift drives faster response times and closer alignment with business goals.

Key Steps to Implement Full Product Specs

  1. Define Product Vision At project start, clarify why the data product exists and its expected business outcomes. Assign ownership to foster accountability.
  2. Use IFS Scope Tool for Detailed Specs Conduct structured workshops to capture field-level details, business logic, compliance needs, and integration points. Link specs to business goals.
  3. Create Formal Data Contracts Specify data schemas, access controls, update frequencies, and service levels. Make contracts easy to access, ideally in a repository tied to IFS Cloud’s Data Catalog.
  4. Assign Domain Ownership Appoint stewards for ongoing product updates, governance, and quality assurance.
  5. Iterate Specs Through Project Phases Continuously refine specs during prototype validation, solution setup, and operational readiness based on user feedback and system configurations.
  6. Apply Federated Governance Balance central oversight and local stewardship to maintain standards and adaptability.

Real-World Use Case: Procurement Domain

For example, in procurement, full product specs include scoping purchase order data, defining API contracts, setting SLAs for data refresh, and formal governance workflows. Testing pipelines validate compliance before rollout. This setup accelerates onboarding and improves data quality.

Business Benefits of Full Product Specs

  • Clear accountability for data product ownership
  • Faster onboarding and iteration cycles
  • Stronger compliance with organizational and regulatory standards
  • Easier scaling to new domains or business units

Final Thoughts

Implementing full product specifications with IFS Cloud and Data Mesh leads to a scalable, compliant, and business-aligned ERP data platform. It clarifies governance, accelerates delivery, and ties data assets directly to measurable business outcomes. Organizations looking to improve their data strategy and ERP operations should consider partnering for IFS Cloud Implementation services.

One more thing ...

List technical steps for creating data product specifications in IFS Cloud

Creating Data Product Specifications for IFS Cloud Data Mesh

List technical steps for creating data product specifications in IFS Cloud

Here are the technical steps for creating data product specifications in IFS Cloud. These steps reflect best practices from IFS Cloud methodology and Data Mesh principles within the platform.

Technical Steps

  • Define Scope and Domain Ownership
    • Use the IFS Scope Tool to map business requirements to functional modules.
    • Assign responsible domain owners and stewards for each data product, ensuring clarity on accountability and lifecycle management.
  • Capture Requirements and Product Vision
    • Document intended business outcomes, key stakeholders, and integration touchpoints.
    • Conduct workshops to refine goals and confirm high-level requirements for your data product offering.
  • Detail Product Specification
    • List all data fields, entities, and relationships that compose the data product.
    • Specify business logic, validation rules, and transformation requirements.
    • Align schema definitions with IFS standard models or extend them using configuration tools if needed.
  • Establish Data Contracts and Governance
    • Define interfaces, APIs, and data sharing agreements in line with enterprise policies.
    • Specify access controls and compliance rules using IFS Cloud’s role-based authorization and data access control features.[^6]
    • Use the Enterprise Book of Rules to document governance processes, quality standards, and operational SLAs.
  • Configure and Implement in IFS Cloud
    • Leverage IFS configuration tools (like Application Configuration Packages) to implement required fields, objects, and process flows.[^4]
    • Develop or adapt BPA Workflows for processes requiring automation or advanced approval logic.
    • Create necessary documentation and attach as part of the data product package.
  • Validate, Test, and Iterate
    • Prototype the data product in the relevant test environment and validate with end users.
    • Confirm operational readiness, accuracy, and compliance.
    • Gather feedback and update specifications and configurations before moving to production.
  • Document and Onboard
    • Register the final product spec in IFS Cloud’s data catalog, linking to technical documentation and business definitions.
    • Ensure ongoing stewardship is in place and communicated to all stakeholders for continuous improvement and governance.

These steps deliver robust, governed, and business-aligned data products within IFS Cloud, supporting both operational efficiency and analytics needs.

Validate Product Definitions in Prototypes

Step-by-step guide to validating data product definitions in IFS Cloud Data Mesh prototypes. Ensure governance, quality, and stakeholder alignment.

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

Tool Purpose
IFS Scope Tool Document and refine prototype scope and business domain alignment.
Enterprise Book of Rules Formalize data product definitions, governance rules, and sharing agreements.
IFS Data Migration Manager Validate data integrity and migration processes.
IFS Connect Test API-based data sharing and integration.
Data Catalog Manage metadata, lineage, and discoverability.
Data Tracker Monitor quality metrics and SLAs.

Success Metrics for Phase 2

Metric Target
Prototype Coverage 40 – 50 main business processes validated.
Data Product Quality 95% of products meet defined SLAs.
Governance Compliance 100% of data sharing agreements documented.
Stakeholder Satisfaction 90% approval rate in validation workshops.

Common Risks and Mitigation

Risk Mitigation Strategy
Misaligned Domain Ownership Clarify roles in workshops and document in the Book of Rules.
Data Quality Issues Implement automated validation and manual reviews.
Governance Gaps Test exception handling and update processes iteratively.

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!

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