Data Governance

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 makes sure all teams are working together from the start.

What is Defining Vision and Aligning Strategy?

Defining a vision means deciding what you want to achieve with Data Mesh. It’s about setting a clear goal for how data should help your business. Aligning strategy means making sure this vision matches your company’s main goals and plans. For example, if your company wants to deliver products faster, your Data Mesh vision might focus on making data easier to find and use so that teams can make quicker decisions. This step is about making sure everyone understands why you are moving to Data Mesh and what success will look like.

Key Activities and Best Practices

Assess the Current State of Data: Start by looking at how your company currently manages data. Find out who owns which data, how data is shared, and where there are problems or gaps.
Find Pain Points, Tech Debt, and Data Silos: Identify where data is hard to access, where teams are working in isolation, or where old systems are causing problems. These are the areas where Data Mesh can help the most.
Connect Vision to Business Objectives: Make sure your Data Mesh vision supports your company’s main goals. For example, if your business wants to improve customer experience, your vision should focus on 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: Use workshops and open discussions to get input from different teams. Make sure everyone understands the benefits and their role in the new approach. Use tools like the Lean Value Tree to map out your vision, goals, and how you’ll measure success.

Challenges and Solutions

Lack of Buy-In: Some teams may not see the value or may fear change.
Solution: Communicate clearly about 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: Offer training, support, and incentives for teams taking on 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, not controlled by one central group. Each team is responsible for the quality and security of its own data products. Clear rules and responsibilities help everyone know what is expected and keep 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:

Start with 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.

TL;DR - A golden record is the authoritative, single‑source version of your most valuable data (customers, products, suppliers, etc.). Establishing one boosts decision quality, unlocks operational efficiency, and de‑risks compliance. This playbook shows business leaders how - and why - to make it happen.

What Is a Golden Record?

A golden record is “a single, well‑defined version of all the data entities in an organizational ecosystem,” essentially the single source of truth^[1]. It sits at the heart of Master Data Management (MDM), reconciling and enriching duplicate records scattered across CRM, ERP, and other systems until one trusted profile remains.

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

Why Business Leaders Should Care

Growth & Insight. In the latest Dun & Bradstreet B2B Data Report, 81 % of senior decision‑makers said data’s primary mission is to fuel growth  -  but only 57 % trust their data to do so^[3].
Quality & Cost. HFS Research finds 40 % of enterprise data is “bad or unusable,” draining 25‑35 % of potential value^[4].
Compliance & Risk. One clean record per customer simplifies GDPR “right to be forgotten” actions and slashes duplicate communications.
Operational Efficiency. Staff stop reconciling spreadsheets; systems integrate faster; analytics stop second‑guessing which number is right.

How Golden Records Are Created

The workflow is straightforward, even if the tooling is sophisticated:

Ingest. Pull data from every relevant source system.
Clean & Standardize. Fix format issues and obvious errors.
Match. Use deterministic keys and fuzzy logic to find duplicates.
Merge / Survivorship. Apply rules (or ML) to keep the “best” value for each attribute.
Publish. Feed the mastered record back to consuming systems and analytics.

Data pipelines converging into a single database icon — Figure 1 - Multiple data streams converge into one golden record.

Common Challenges (and Fixes)

Challenge	Counter‑move
Poor data quality at the source	Automate validation and enforce standards before data hits the hub.
Duplicate & conflicting records	Invest in robust matching algorithms and clear survivorship rules.
Integration complexity	Use an MDM platform or data fabric to abstract away source‑system quirks.
Governance fatigue	Assign data stewards and make KPIs (e.g., % duplicates) visible to execs.

Golden Records in Action

Customer 360° View

A retailer merged marketing, e‑commerce, and support data into one golden customer profile. Result: a 19 % lift in first‑call resolution and personalized campaigns that drove a 12 % revenue uptick.

Product Data Consolidation

A manufacturer unified engineering specs, procurement costs, and sales descriptions. New products now launch in weeks, not months, because every channel reads from the same catalog.

Team reviewing unified customer profiles on dashboards — Figure 2 - Unified dashboards powered by mastered data.

Unified Supplier & Compliance Data

Aggregating finance, legal, and operations data into a golden supplier record simplified compliance reporting and reduced risk.

Getting Started

Pick one domain (often customer) and run a proof‑of‑value.
Define success KPIs: duplicate rate, time‑to‑insight, compliance effort saved.
Select MDM tooling that supports matching, survivorship, and governance.
Assign data owners and iterate - golden records are a living asset.

Bottom line: The golden record transforms raw, fragmented data into a strategic asset that drives growth, efficiency, and trust. In an age where data is currency, one clean record is worth more than a thousand conflicting ones.

Data Governance definition and principles

Data governance is a set of policies, processes, and procedures that ensure the availability, security, and integrity of data throughout its entire lifecycle, from creation to disposal. It is a framework for managing data in a way that aligns with business objectives, while ensuring the highest standards of quality, security, and compliance.

Key Components of Data Governance

1. Data Quality: Ensuring data accuracy, completeness, and integrity, through processes such as data validation, data cleansing, and data normalization.
2. Data Security: Protecting data from unauthorized access, use, or disclosure, through measures such as encryption, access controls, and auditing.
3. Data Integrity: Ensuring that data is complete, accurate, and consistent, with regular backups and versioning.
4. Data Confidentiality: Ensuring that data is handled and processed in accordance with organizational data protection policies and applicable laws and regulations.

Governance Process

1. Data Management: Establishing policies and procedures for the management of data, including data discovery, data ingestion, and data stewardship.
2. Stakeholder Engagement: Involving stakeholders in the governance process to ensure that their needs and expectations are met, and that they are aligned with the organization's objectives.
3. Monitoring and Auditing: Regularly reviewing and auditing data management processes to ensure compliance with policies, procedures, and standards.

Goals of Data Governance

1. Ensure data quality: Ensure that data is accurate, complete, and consistent.
2. Protect data security: Protect data from unauthorized access, use, or disclosure.
3. Ensure data integrity: Ensure that data is complete, accurate, and consistent, with regular backups and versioning.
4. Support business objectives: Ensure that data is used to support business objectives, such as decision-making, risk management, or regulatory compliance.
5. Improve business outcomes: Ensure that data is used to drive business outcomes, such as revenue growth, customer acquisition, or efficiency improvements.

Benefits of Data Governance

1. Improved decision-making: Ensures that data is used to inform business decisions.
2. Increased efficiency: Reduces waste and improves productivity.
3. Enhanced customer experience: Ensures that customer data is used to deliver personalized experiences.
4. Better compliance: Ensures that data is handled and processed in compliance with applicable regulations and standards.
5. Increased confidence: Ensures that stakeholders have confidence in the quality and security of the data.

Implementing Data Governance in Your IFS Cloud Journey: A Thoughtful Analysis

Overview

Data Governance can be described as jargon, but in short, it’s all about having your data not just accessible, but also protected and trustworthy throughout its whole life cycle. When you're implementing an IFS Cloud project, it’s essential to have good data governance in practice. It allows you to own your data assets and unlock the full value of your project. This paper offers an uncomplicated introduction to allow you to include data governance within your IFS Cloud project.

I. Data Governance Principles

1. Data Quality: Think of data quality as the foundation of your project. By defining what good data looks like and consistently checking for accuracy, you’re making sure your decisions are based on solid ground.

2. Data Security: Protecting sensitive data is like locking your front door - it keeps unwelcome visitors out. By implementing robust access controls and encryption, you can keep your data safe and sound.

3. Data Integrity: Imagine your data as a puzzle; every piece needs to fit perfectly. Keeping your data complete, accurate, and consistent is vital, so regular backups and tracking changes are must-dos.

4. Data Confidentiality: Respecting data confidentiality is not just about regulations; it's about taking care of the trust your customers and stakeholders place in you. Always handle data following your organization’s privacy policies and relevant laws.

II. IFS Cloud-Specific Governance Considerations

1. Data Storage and Management: Think carefully about where and how you store your data. Having clear policies on retention, archiving, and purging helps ensure your data remains useful without clutter.

2. Data Access and Sharing: Open communication about who can access and share data strengthens collaboration. Creating clear data-sharing agreements ensures everyone is on the same page.

3. Data Security and Access Controls: Building a strong security framework is like creating a safe environment for your data. Regular audits and advanced security measures like encryption go a long way in protecting your information.

4. Data Quality Monitoring and Validation: Consistent monitoring of your data quality is key. Establishing metrics and regular reports not only keeps you informed but also shows your commitment to maintaining high standards.

III. IFS Cloud Implementation Project Governance

1. Data Governance Structure: Setting up a clear governance structure is like assembling a reliable team. Clearly defined roles and responsibilities ensure everyone knows what’s expected of them.

2. Data Management Processes: Developing processes for data management is essential. Like organizing a well-functioning operation, processes for data discovery and stewardship keep everything running smoothly.

3. Data Quality and Security Metrics: Establishing metrics for data quality and security helps you keep an eye on your performance. Think of it as setting benchmarks that guide you toward your goals.

4. Compliance and Regulatory Framework: Keeping up with data protection regulations isn’t just a checkbox task - it’s essential to building trust and protecting your organization.

IV. Implementation Roadmap

1. Phase 1: Data Discovery and Governance Design:

* Begin by identifying your data assets and the key stakeholders involved.

* Develop actionable data governance principles and policies to guide your project.

* Establish effective data management processes to set a strong foundation.

2. Phase 2: IFS Cloud Configuration and Customization:

* Configure the IFS Cloud environment to align with your data management and security needs.

* Customize the system to meet your specific data quality and security requirements.

3. Phase 3: Testing and Quality Assurance:

* Thoroughly test the IFS Cloud setup for data quality and security, ensuring every detail is addressed.

* Validate the system to confirm that your data is accurate, complete, and secure before going live.

4. Phase 4: Live Deployment and Ongoing Governance:

* Launch the IFS Cloud with your data governance and security policies in place.

* Establish continuous data governance practices to adapt and thrive over time.

By following this thoughtful guide, your IFS Cloud implementation project can successfully manage its data assets, paving the way for achieving significant benefits and fostering a culture of trust and collaboration.

Define Vision and Align Strategy: Setting the Foundation for Data Mesh