IFS-ERP CRIMS customization of IFS Cloud
Data Governance Explained Framework Benefits and Best Practices

What Problem Does This Article Solve?

The "Fragile Pipeline" Dilemma.
In traditional ERP implementations, data extraction is often built on implicit trust and "tribal knowledge." Developers query SQL views or extract Excel dumps without a formal contract. This works initially but fails catastrophically when the system evolves. When IFS Cloud receives a bi-annual release update (e.g., 25R1), or when a business process changes, these fragile pipelines break because there was no agreed-upon "Interface Contract."

This article provides a rigorous framework for Confirming Sharing Agreements. It solves the problem of ambiguity. It guides Enterprise Architects and Data Owners on how to transition from "sending data" to "serving a product," ensuring that every data exchange is governed by explicit schemas, guaranteed SLAs, and strictly defined semantics. It transforms data from a byproduct of the ERP into a reliable, engineered asset.

Phase 2: The Transition from Concept to Contract

Phase 0 and Phase 1 of an IFS Cloud Data Mesh implementation are primarily strategic. They involve defining the vision, establishing the governance committee, and mapping the high-level domains. Phase 2: The Prototype is where the rubber meets the road. It is the phase where we stop talking about "Manufacturing Data" in the abstract and start building specific, versioned Data Products.

The success of Phase 2 depends entirely on the rigorous confirmation of Sharing Agreements. In the Data Mesh paradigm, data is treated as a product. Just as a physical product like a smartphone comes with a specification sheet, a user manual, and a warranty, a Data Product must come with a Sharing Agreement. This agreement explicitly defines what the consumer can expect and what the producer (the Domain Team) is obligated to deliver.

Why "Confirm" in Prototype?

You might ask, "Why do we need to confirm agreements now? Can't we just build the integration?" The answer lies in the cost of change. Changing a data contract during the Prototype phase costs pennies; changing it once hundreds of reports and AI models depend on it costs thousands.

The "Confirmation" process is a negotiation. It is a dialogue between the Domain Owner (who knows the data's limitations) and the Consumer (who knows the business need). This dialogue often exposes hidden complexities: "You want real-time inventory? We only calculate weighted average cost nightly." Confirming the agreement resolves these discrepancies before code is written.

The "Mock Consumer" Test

A critical activity in Phase 2 is the "Mock Consumer" validation. Before the full integration is built, the Domain Team publishes the Draft Sharing Agreement (often an OpenAPI specification). The Consumer Team then attempts to write code or design a report based strictly on that document, without looking at the underlying database. If they have to ask questions, the agreement is incomplete. This "Clean Room" testing ensures the contract is self-describing and robust.

The Four Pillars of an IFS Cloud Sharing Agreement

A Sharing Agreement is not a vague email promising to "send the spreadsheet." Within the context of IFS Cloud and modern Data Mesh architectures, it is a precise technical and legal construct. To be considered "Confirmed," an agreement must fully address four non-negotiable pillars.

The agreement must rigidly define the data structure. In the IFS Cloud world, this typically relates to the definition of the Entity or the Projection being exposed.

  • Field Definitions: It is not enough to say "Order Amount." The agreement must specify: Is it a Float or Decimal? How many decimal places? If it is a Date, is it ISO 8601 format (YYYY-MM-DD) or a Unix Timestamp?
  • Nullability Contracts: This is the most common cause of integration failure. The agreement must explicitly list which fields are Mandatory (Guaranteed Not Null) and which are Optional. Consumers (like AI models) often crash on unexpected nulls.
  • Enumerations: IFS makes heavy use of "Client" vs "DB" values (e.g., 'Planned' vs '10'). The agreement must confirm which value is exposed. Best practice dictates exposing the readable Client value or providing a lookup map.
  • Versioning Strategy: The agreement must state the versioning policy. "This product is exposed via /v1/ShopOrder. Breaking changes will force a move to /v2/." This protects consumers from the "Evergreen" updates of IFS Cloud.

Data has a temporal dimension. Schema defines what data is; SLOs define when and how it is delivered. A structurally perfect dataset is useless if it arrives 4 hours too late for the morning shipping meeting.

Freshness (Latency)

The agreement must specify the maximum age of the data. "Data in this API reflects transactions up to 5 minutes ago." or "This is a nightly snapshot, refreshed at 02:00 UTC."

Availability (Uptime)

What is the guaranteed uptime? 99.9%? Does the API go down during the IFS Cloud maintenance window? The consumer needs to know to build retry logic.

Retention Policy

How far back does the data go? IFS Cloud operational tables might hold 10 years, but a high-performance API might only serve the "Active" rolling 24 months. This must be codified.

Structure is useless without meaning. The "Semantic Gap" is where business value is lost. The Sharing Agreement must resolve ambiguity using the Business Glossary established in Phase 1.

  • Calculation Logic: If the data product exposes `NetMargin`, how is that calculated? Does it include overhead allocations? Does it account for rebates? The formula must be referenced.
  • State Definitions: What does a status of `Released` actually mean in the Shop Floor Workbench compared to the Planning module?
  • Master Data References: The agreement must confirm that fields like `SiteID` or `CustomerID` reference the corporate standard MDM list, ensuring joinability with other domains.

The agreement must define who can access the product and how that access is controlled via IFS Cloud's security model.

Compliance & PII: If the data contains Personally Identifiable Information (HR data, Customer Contacts), the agreement must state how it is protected. "Employee names are masked for consumers with the `ANALYST_BASIC` role."

Permission Sets: The agreement should specify the IFS Permission Set required to consume the API (e.g., `DATAMESH_FINANCE_READ`).

Usage Constraints: To protect the operational performance of the ERP, the agreement may impose rate limits. "Consumers are limited to 1000 API calls per hour."

Technical Implementation: Codifying the Contract in IFS Cloud

Confirming a sharing agreement is not just a paperwork exercise. In the Prototype Phase, we must implement the agreement technically within the IFS Cloud architecture. We move away from direct SQL access (which is insecure and bypasses business logic) and utilize the native capabilities of the platform to enforce the contract.

Projections & API Explorer

In IFS Cloud, the primary mechanism for a Data Contract is the Projection. The Projection exposes entities via OData/REST APIs.

Implementation: The Domain Owner uses the IFS API Explorer to generate the OpenAPI Specification (OAS) JSON file. This file is the technical contract. It defines every endpoint, data type, and required parameter. The Consumer "signs" the agreement by successfully authenticating (via OAuth2) and parsing this OAS file to build their client.

Data Migration Manager (DMM)

The IFS Data Migration Manager (DMM) is not just for legacy migration; it is a potent validation engine for the Data Mesh.

Implementation: Before data is "Certified" for sharing, it can pass through DMM validation rules. The Sharing Agreement might specify: "ProjectID must exist in the Project Module." DMM enforces this integrity check. If the data fails, it is flagged as "Non-Conforming," protecting the consumer from bad data.

Information Sources

For internal consumers (e.g., users viewing Lobbies or Business Reporter), the Data Product is often an Information Source (IS).

Implementation: The agreement focuses on Performance and Access. "This Lobby Element will load within 2 seconds." Confirming the agreement involves load-testing the underlying IS or Quick Information Source (QIS) to ensure that complex joins do not degrade system performance for other users.

The Negotiation Process: Breaking Silos

Confirming an agreement is a human process as much as a technical one. It involves negotiation between the Domain Owner (Producer), who understands the data's generation and limitations, and the Consumer, who understands the business requirement. In many organizations, these two groups rarely speak the same language. The Prototype Phase forces this dialogue to happen.

The Role of the Governance Committee:
Occasionally, negotiations stall. The Consumer demands 100% real-time data, but the Producer knows this will crash the production server. This is where the Data Governance Committee (established in Phase 0) steps in. They act as the arbitrator, balancing the business value of the request against the technical cost and risk, ultimately ruling on the final terms of the Sharing Agreement.

 

Common Friction Points & Resolutions

Friction Point The Producer's Stance The Resolution (The Agreement)
Data Freshness "Real-time extraction hurts my transactional performance. I can only provide a nightly dump." The agreement specifies Near-Real-Time via IFS Connect / Event streams for critical operational data, and batch processing for historical analysis.
Data Quality "I can't guarantee no nulls in the `Description` field because users leave it blank." The agreement mandates a Transformation Rule: The Producer will replace NULL with "N/A" before publication, so consumer scripts don't break.
History "I only keep the current active year in the main transaction table." The agreement defines a Data Lake storage tier (e.g., Azure Data Lake) where the Domain exports history for the Consumer's long-term trend analysis.

Lifecycle Management: When the Agreement Changes

A Sharing Agreement is not a static artifact; it is a living document. IFS Cloud is an "Evergreen" platform, receiving functional updates twice a year. Business processes change. New regulations (like ESG reporting) emerge.

Therefore, the "Confirmation" process must include a Change Management Protocol.

Deprecation Policy

What happens when a data product is retired? The agreement must specify a "Deprecation Notice Period" (e.g., 6 months). The Producer cannot simply turn off the API; they must notify all registered Consumers and provide a migration path to the new version.

Breaking Changes

If the Producer renames a column or changes a data type, this is a "Breaking Change." The agreement dictates that this triggers a major version increment (e.g., from v1 to v2). The v1 endpoint must remain active and supported for a defined period to allow Consumers to refactor their code.

From Prototype to Production

Once the Schema is validated, the SLOs are tested via the Mock Consumer, and the Security is audited by the CISO, the Sharing Agreement is formally "Confirmed."

What does this mean operationally? It means the Data Product is added to the Enterprise Data Catalog. It moves from a "Lab" status to a "Production" status. The Domain Team is now accountable for supporting it. If the API goes down at 2 AM, the Domain Team (or their designated support arm) is alerted, not central IT. Confirming the agreement in Phase 2 creates the template for the entire organization. It establishes the "Trust Architecture" required to scale from a single pilot to a comprehensive enterprise Data Mesh.

Frequently Asked Questions

This constitutes a "Breaking Change." The Sharing Agreement dictates the strict protocol for this scenario. Typically, the Domain Owner is required to maintain the old version of the API (v1) while simultaneously publishing the new structure as (v2). They must provide a formal "Deprecation Notice" to all registered Consumers (usually 3-6 months) to allow them sufficient time to update their integrations. The Data Mesh governance framework prevents the Owner from simply overwriting v1 and breaking downstream consumers.

While specialized "Data Catalog" or "Data Contract" software platforms exist (such as Collibra, Alation, or Atlan), they are not strictly necessary for the Prototype Phase. Simple, accessible tools often work best initially. A version-controlled repository (like Git) containing the OpenAPI specifications (YAML/JSON) and a Markdown document describing the SLAs is sufficient. The critical factors are version control, discoverability, and accessibility, rather than purchasing expensive new software immediately.

IFS Cloud releases functional updates twice a year (e.g., 25R1, 25R2). These updates can occasionally modify the underlying Core Projections or database views. The Sharing Agreement places the burden of stability on the Domain Owner. They must perform regression testing on their Data Products against the Release Candidates. They must ensure that the "Public" interface defined in the agreement remains stable for consumers, even if they have to adjust the internal mapping or logic to accommodate the changes in the IFS platform.

At a minimum, the agreement requires the sign-off of the Domain Owner (Producer) and the Lead Consumer. However, for critical enterprise data sets (such as Master Data, Financials, or HR), the Data Governance Lead and the Security Architect should also act as signatories. This ensures that the agreement complies with enterprise-wide standards for security, naming conventions, and regulatory compliance (GDPR/SOX).

Technically yes, but the primary value of the Data Mesh architecture comes from inter-domain sharing. Internal data usage (e.g., a Manufacturing report used by a Manufacturing planner) usually does not require the formal rigidity of a Sharing Agreement because the producer and consumer are often on the same team or report to the same manager. These agreements are specifically designed to bridge the boundaries between domains, where communication gaps and misaligned priorities usually cause integration failures.

Metadata is the "label on the can." It makes the data product discoverable. The Sharing Agreement should mandate specific metadata tags (e.g., Domain Name, Data Classification, Refresh Rate, Owner Contact Info). This allows the Data Product to be indexed by the Enterprise Data Catalog, allowing other users in the organization to find the data they need without sending emails to IT to ask "where is the sales data?"
Define Vision and Align Strategy: Setting the Foundation for Data Mesh

Define Vision and Align Strategy for Data Mesh

Introduction

A Data Mesh is a new approach for organizations to manage and utilize their data. Instead of having one central team in charge of all data, Data Mesh gives different business teams the power to own and operate their data. This approach helps make data more useful, trusted, and available across the company. Starting your Data Mesh journey with a clear vision and strategy is the most crucial step. It sets the direction, helps everyone understand the goals, and ensures all teams work together from the start.

What is Defining Vision and Aligning Strategy?

Defining a vision involves determining what you aim to accomplish with Data Mesh. It’s about setting a clear goal for how data should help your business. Aligning strategy means ensuring that this vision aligns with your company’s main goals and plans. For instance, if your company aims to accelerate product delivery, your Data Mesh vision could focus on making data more accessible and usable, enabling teams to make faster decisions. This step ensures everyone understands the rationale behind moving to Data Mesh and what constitutes success.

Key Activities and Best Practices

  • Assess the Current State of Data: Begin by examining your company’s current data management practices. Identify who owns each dataset, how data is shared, and where problems or gaps exist.
  • Find Pain Points, Tech Debt, and Data Silos: Identify areas where data is difficult to access, teams are working in isolation, or outdated systems are causing issues. These are the areas where Data Mesh can help the most.
  • Connect Vision to Business Objectives: Ensure your Data Mesh vision aligns with your company’s key objectives. For instance, if your business aims to enhance customer experience, your vision should prioritize making customer data more reliable and accessible.
  • Get Executive Support and Funding: Leadership support is key. Leaders need to understand the value of Data Mesh and be willing to invest in the changes required.
  • Build a Shared Vision: Utilize workshops and open discussions to gather input from various teams. Make sure everyone understands the benefits and their role in the new approach. Utilize tools like the Lean Value Tree to map out your vision, goals, and success metrics.

Challenges and Solutions

  • Lack of Buy-In: Some teams may not see the value or may fear change.
  • Solution: Clearly communicate the benefits and involve teams early in the process.
  • Unclear Goals: Without a clear vision, teams may pull in different directions.
  • Solution: Use simple, shared language to describe your vision and goals. Make sure everyone agrees on what success looks like.
  • Resistance to Change: People may be used to the old way of working.
  • Solution: Provide training, support, and incentives to help teams transition into new roles.

Data Governance Considerations

Data governance is about making sure data is managed properly, securely, and ethically. In Data Mesh, governance is shared across teams, rather than being controlled by a single central group. Each team is responsible for the quality and security of its own data products. Clear rules and responsibilities help everyone understand what is expected, thereby keeping data trustworthy. 

Business and Cultural Impact

A clear vision and strategy help build trust across the business. When everyone knows the goals and their role, it’s easier to share data and work together. This leads to better data quality, faster decision-making, and a culture where teams feel ownership and pride in their data. Over time, this supports innovation and helps the business grow.

Practical Tips and Checklist

Tips:

  • Begin by defining the organizational boundaries you already have.
  • Use simple tools like the Lean Value Tree to map out your vision and goals.
  • Communicate openly and often with all teams.
  • Be flexible and ready to adjust your approach as you learn and grow.

Checklist:

  • Have you defined a clear vision for Data Mesh?
  • Is your vision aligned with business goals?
  • Have you identified current data challenges and opportunities?
  • Do you have executive support and funding?
  • Have you involved all key teams in the planning process?
  • Are roles and responsibilities clear?
  • Is there a plan for ongoing communication and learning?

Conclusion

Defining vision and aligning strategy is the foundation of a successful Data Mesh journey. It ensures everyone is working toward the same goals and sets up your organization for long-term success. By starting with a clear vision, aligning with business objectives, and involving all teams, you create a strong base for the next steps in your Data Mesh transformation. 

Golden Record in the Context of Master Data: Your Single Source of Truth

Golden Record in the Context of Master Data: Your Single Source of Truth

Master Data Management

The Golden Record

The single source of truth that drives decision quality, operational efficiency, and compliance.

TL;DR: A golden record is the authoritative, single‑source version of your most valuable data (customers, products, suppliers). Establishing one is not just a technical exercise, it is the only way to de-risk compliance and unlock genuine business insight.

What Is a Golden Record?

A golden record is a single, well-defined version of a data entity within an organizational ecosystem. It sits at the heart of Master Data Management (MDM), reconciling duplicate records scattered across your CRM, ERP (like IFS Cloud), and legacy systems until one trusted profile remains.

"A golden record provides the complete 360‑degree view of an entity — nothing missing, nothing duplicated, always current."

How It Is Created

The workflow transforms raw noise into strategic assets through five distinct stages:

01
Ingest: Pull data from every relevant source system (CRM, ERP, PLM).
02
Clean & Standardize: Fix formatting errors, casing, and obvious data corruption.
03
Match: Use deterministic keys and fuzzy logic to identify duplicates across systems.
04
Merge (Survivorship): Apply rules to retain only the "best" value for each attribute.
05
Publish: Feed the mastered record back to consuming systems for analytics and operations.

Common Challenges & Fixes

Challenge The Counter-Move
Poor Source Quality Automate validation and enforce standards before data hits the hub.
Conflicting Records Invest in robust matching algorithms and clear survivorship rules.
Integration Complexity Use an MDM platform or Data Fabric to abstract source-system quirks.
Governance Fatigue Assign data stewards and make KPIs (e.g., % duplicates) visible to execs.

The Cost of Bad Data

40%
of enterprise data is "bad or unusable."

HFS Research finds this drains 25-35% of potential operational value.

Golden Records in Action
  • Customer 360°

    Merging marketing and support data. Result: 19% lift in first-call resolution.

  • Product Consolidation

    Unifying engineering and sales specs. Result: New products launch in weeks, not months.

  • Supplier Compliance

    Aggregating finance and legal data to simplify risk reporting.

Frequently Asked Questions

The terms are often used interchangeably. However, technically, a Master Record is the container within an MDM system, while the Golden Record is the result of the cleansing and matching process—the "perfect" version that is published to the business.

This is handled via Survivorship Rules. You define logic (e.g., "Always trust the CRM for phone numbers" or "Trust the most recently updated record") to determine which data point "survives" into the Golden Record.

IFS Cloud enforces data integrity, but it is primarily a transactional system. To create true Golden Records from multiple disparate systems (e.g., Salesforce + IFS + Legacy), you typically need a dedicated MDM strategy or tools like the IFS Data Migration Manager to cleanse data before it enters the ERP.

Sources: Dun & Bradstreet, HFS Research, TechTarget.

Data Governance in IFS Cloud: Definition, Framework, and Best Practices
Data Governance definition and principles

Definition of Data Governance

Strategic Framework

Data Governance in IFS Cloud

A comprehensive guide to ensuring data quality, security, and compliance throughout the lifecycle.

Data governance is not just IT bureaucracy; it is the backbone of your ERP investment. In the context of IFS Cloud, it aligns technical capabilities with business objectives, ensuring that your data supports digital transformation rather than hindering it.

Key Components

Effective governance in IFS Cloud rests on four pillars. Neglecting one destabilizes the whole structure.

1. Data Quality

Automated validation and cleansing. Using IFS Cloud to profile data and handle exceptions before they impact reporting.

2. Data Security

Role-based access controls (RBAC) and encryption. Aligning IFS Cloud security with ISO 27001 and GDPR standards.

3. Data Integrity

Versioning and immutable audit trails. Ensuring consistency through backup strategies and change management workflows.

4. Confidentiality

Compliance-driven handling of PII. Utilizing data masking and anonymization capabilities within the ERP.

The Governance Process

Step 1: Assess Maturity

Use analytics to identify gaps in quality and compliance. Establish a baseline.

Step 2: Define Policies

Develop enforceable standards for ownership, retention, and security protocols.

Step 3: Establish Stewardship

Assign data domains to business units. Train teams on accountability.

Step 4: Implement Controls

Configure validation rules and cleansing workflows inside IFS Cloud.

Step 5: Deploy Security

Map policies to GDPR/HIPAA requirements using built-in security features.

Step 6: Monitor & Optimize

Continuously refine processes based on performance metrics and feedback.

Why Do This?

  • Better Decisions: Reliable data feeds accurate analytics.
  • Efficiency: Less time fixing errors, more time working.
  • Customer Trust: Personalized, error-free engagement.
  • Reduced Cost: Automated management reduces overhead.
Strategic Goals
  • Ensure Data Quality
  • Protect Security
  • Meet Compliance (GDPR)
  • Build Stakeholder Confidence
Need a Roadmap?

Ready to implement data governance in IFS Cloud?

Contact Our Experts

Frequently Asked Questions

Data Management is the technical execution (backup, storage, integration), while Data Governance is the strategy (policies, roles, definitions). Governance sets the rules; Management follows them.

Yes. IFS Cloud includes features for Data Management, Information Lifecycle Management (ILM), and specialized tools like the Data Migration Manager and Data Quality Dashboard to support your governance framework.

It is a shared responsibility. IT handles the infrastructure and security, but Business Units (Finance, SCM, HR) must act as Data Stewards, owning the quality and definition of the data they generate.
Implementing Data Governance in Your IFS Cloud Journey: A Thoughtful Analysis

Implementing Data Governance in Your IFS Cloud Journey

Data Governance in IFS Cloud

Quality, Security, and Compliance without the fluff.

Implementing IFS Cloud without a solid data governance plan is like building a skyscraper on sand. Data governance isn’t about bureaucracy; it’s about making sure your data works for you, ensuring accuracy, security, and utility across your organization.

Why It Matters

Without proper governance, you risk making business decisions based on outdated information, exposing sensitive data to breaches, and failing to realize the full potential of your ERP investment. Good governance turns these risks into opportunities for growth.

The Four Pillars of Success

1. Data Quality

Poor data costs millions. Essential for financial reporting and inventory management.

  • Define clear data standards per area.
  • Implement validation rules to prevent bad entry.
  • Tip: Use the Data Quality Dashboard to monitor completeness.
2. Data Security

Protecting your most valuable asset from breaches and unauthorized access.

  • Implement Role-Based Access Control (RBAC).
  • Enable field-level security for sensitive info.
  • Set up audit trails to track access.
3. Data Integrity

Ensuring consistency and reliability throughout the data lifecycle.

  • Implement change tracking for master data.
  • Automate point-in-time recovery backups.
  • Use validation features to prevent corruption.
4. Data Compliance

Meeting GDPR, SOX, and industry standards to build trust.

  • Document handling procedures.
  • Conduct regular compliance audits.
  • Train employees on data protection.

The 4‑Phase Roadmap

Phase 1: Assessment

Identify critical assets and assess risks using Data Discovery tools.

Phase 2: Design

Configure security settings and validation rules via Solution Manager.

Phase 3: Implementation

Test policies, train users, and run pilots in a staging environment.

Phase 4: Continuous Improvement

Monitor quality dashboards and refine policies regularly.

Your Governance Team

  • Data Owner: Accountable (e.g., CFO).
  • Data Steward: Manager (e.g., Inventory Mgr).
  • Data Custodian: Tech control (e.g., IT).
  • Data User: Daily user (e.g., Sales Rep).
Success Metrics
  • Accuracy Rate: >98%
  • Resolution Time: ‑50%
  • Security Incidents: 0
  • Audit Findings: 0 Major

Governance FAQs

No. While the scale differs, small companies often face higher risks because one bad dataset (e.g., wrong inventory costs) can have a larger proportional impact on their bottom line.

IFS Cloud includes «Personal Data Management» features that allow you to anonymize data subject records upon request and set automatic retention policies to purge old data, ensuring you meet the «Right to be Forgotten.»

Yes. Governance should be centralized. You can define global validation rules (e.g., Part Numbering standards) that apply to all Sites, while allowing local sites to manage their specific transactional data.

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