Quick Overview
Data Governance as a concept emerged in the early 2000s during the dot com era, initially the concept was largely confined to IT teams and primarily entailed categorizing and cataloguing several data points to streamline processes, manage the origin of the data and ensure its relevance
Within the last couple of years, however, two key changes in technology and business landscape has led to the emergence of Master Data Governance as a key concept that encapsulates more or less the same objective but has become much wider in scope. The two key changes were;
Companies began swiftly moving to ERP systems and relying on large databases that captured key information and drove business decisions at a micro as well as macro level – For instance, most business decisions today across procurement, hiring, vendor onboarding and supplier relationships rely on analytics and data that are interpreted from the ERP systems
Very few professionals back in 1900s would have expected that a single entity would be responsible for production at such a large scale. Today, it’s extremely common for corporations to operate multiple large-scale production facilities that are responsible for manufacturing billions in finished goods or servicing projects that are easily north of billions of dollars in scope. In short, consolidation of business activities and the increasing scale of operations have pretty much left companies with no option but to make data-driven decisions by relying on Software solution and ERP systems for better operational ease.
Master Data Governance (Basics)
Due to inevitable human errors, this reliance on ERP systems, company data and technology can be a double-edged sword for companies.
Over time, poor practices in uploading, maintaining and removing the data can lead to “Junk” data in the ERP, which can cripple business processes and lead to production downtime, high inventory carrying costs, poor supplier relationships and a total absence of insights into customer behavior.
This trend has underscored the need for a “Master Data Governance” system that is a critical piece in Master Data Management and possibly the biggest lever that companies can leverage to ensure their Master Data Systems are accurate, reliable and up to date.
A Master Data Record is a central, authoritative set of information about a key entity in an organization, such as a customer, product, supplier, or employee.
It is used across various systems and processes to ensure consistency and accuracy. Master data typically includes essential details like names, addresses, IDs, and other key attributes that remain relatively stable over time and are shared across the organization
Master Data Governance (MDG) functions as a structured control layer over enterprise master data, enforcing data integrity, standardization, and compliance across domains such as Material, Customer, Vendor, and Finance.
It ensures that master data creation, updates, and deactivation occur in a controlled and auditable manner, mitigating risks associated with inconsistent or inaccurate data.
Domain-Specific Models
MDG relies on domain-specific data models that define:
Mandatory and optional fields
Hierarchical relationships
Cross-field dependencies and allowed value lists
These models enforce field-level validation and maintain cross-system consistency.
Example: In the Material Master domain, fields like Material Type and Base Unit of Measure are validated to prevent incorrect entries from entering downstream ERP systems.
Data Quality Enforcement
MDG enforces technical controls across key dimensions:
Completeness: Mandatory fields must be populated before approval.
Consistency: Field values are uniform across systems.
Accuracy: Values match verified reference sources or business rules.
Workflow and Rule-Based Approvals
It implements structured workflows integrated with rule engines (e.g., BRF+) to control approvals, enforce validations, and route change requests based on domain, data type, or business hierarchy.
This ensures that master data adheres to governance policies before it propagates to transactional or analytical systems.
Duplicate Prevention
Master Data Governance uses duplicate detection mechanisms including fuzzy matching and key field comparisons. Records exceeding predefined similarity thresholds are flagged for steward review, preventing redundant or conflicting master data.
System Integration
MDG acts as the authoritative source across ERPs, CRMs, SCMs, and BI systems via:
Synchronous APIs/OData services for real-time propagation
Asynchronous IDocs/event queues for bulk replication
Federated lookup and reconciliation in co-existence or registry models
This guarantees consistent, high-quality master data across the enterprise, reducing operational risk and improving decision-making.
Each of these disciplines require a dynamic approach for data governance. Moreover, companies are increasingly realizing that these governance systems require a certain level of flexibility depending on Industries, location, product, departments and supplier types and the legacy systems in the market today simply don’t provision for these diverse requirements
Strategies & Tactics for Master Data Governance
It must be noted that Governance practices aren’t simply confined to some “tool”, “software” or “rule book” it’s an organizational-level understanding of business processes and require buy-in from IT, Procurement, Customer success and Maintenance teams.
The software(s) and technologies are simply a reflection of well-thought of processes and flows, governed by the right personnel for maximizing business outcomes. Some of the tactics that can be leveraged are as follows.
Data Validation
Validation is a key part of ensuring that master data is accurate, consistent, and compliant with business rules. The validation process checks the integrity of the master data during creation, modification, and approval processes, preventing errors or inconsistencies from being introduced into the system.
1. Rules and Logic Definition
Validation Rules: These are predefined business rules or logic that define what constitutes valid master data. For example, a rule might require that a customer ID be unique, a postal code match the correct country, or a product category be within a defined set of valid values.
Custom Rules: Organizations can define custom validation rules using tools – like the Business Rule Framework (BRF+), to fit their specific needs and data requirements.
2. Validation during Data Entry
When users create or update master data records (e.g., customer, material, or supplier data), the system checks the data against the predefined validation rules.
Real-time Validation: As the data is entered, softwares perform validation in real-time to detect issues such as missing mandatory fields, incorrect data formats, or inconsistencies (e.g., incorrect country codes or invalid phone numbers).
Error Messages: If a validation rule is violated, the system generates an error message or warning to notify the user. The user MUST correct the data before proceeding.
Approval Workflows
Pretty much all Master Data Management platforms and solutions have an in-built “approval matrix” with varying degree of flexibility. This ensures that no one personnel single-handedly makes the decision to “edit” or “create” a master data record, thus minimizing errors and ensuring that the right stakeholders have insight into the right business process.
For Example, Suppose an organization requires that supplier master data be validated against a compliance database to ensure that the supplier is not blacklisted or subject to legal sanctions.
When a new supplier record is created or an existing one is modified, the approval workflow automatically routes the record to the compliance officer for approval. The officer verifies that the supplier passes all compliance checks before the record is accepted into the system.
Due to the inherent complexity of organizations today, most approval workflows have multiple decision makers with different access-levels, which should be easily configurable in the data governance systems
Data LifeCycle Management
Data Lifecycle Management (DLM) in Master Data Governance refers to the processes and policies that manage the entire lifecycle of master data from its creation and use to its archiving or deletion. DLM ensures that data is accurate, secure, and compliant with regulations throughout its existence, while minimizing the risks of data decay, redundancy, or misuse.
Key Stages of DLM:
Creation: Data is entered into the system with proper validation and approval.
Use: Data is actively used by various departments or applications, ensuring consistency and integration across systems.
Maintenance: Regular updates and corrections to data are made to keep it accurate and aligned with business needs.
Archiving: Data that is no longer actively used but must be retained for compliance or historical purposes is stored in an archive.
Deletion: Data is removed from the system once it is no longer required or after reaching a defined retention period.
Example
In a Supplier Master Data process:
Creation: A new supplier is onboarded, and their data (e.g., contact details, payment terms) is entered into the system, validated, and approved by Procurement and Finance.
Use: The supplier data is used across systems for purchasing, invoicing, and payments.
Maintenance: If the supplier’s address changes, the data is updated after a validation and approval workflow.
Archiving: If the supplier record is no longer actively used but must be kept for historical or legal reasons, it’s archived.
Deletion: After the supplier is inactive for a certain period (e.g., 7 years), their data is deleted from the system, following retention and compliance policies.
DLM ensures that data is managed efficiently, consistently, and in compliance with legal and regulatory requirements throughout its entire lifecycle.
Practical use-cases of Data Governance Systems
Materials and MRO Spares
Example: Improved Spare Parts Availability and Reduced Downtime in Manufacturing
Problem Without MDG: A manufacturing company relies on a large inventory of materials and spare parts for machine maintenance. However, without proper governance over material master data, the company struggles with duplicate records, inconsistent part numbers, and incomplete descriptions, leading to delays in locating and ordering critical parts. As a result, machines may stay down for longer periods, causing operational disruptions and loss of production time.
MDG Benefit: With Master Data Governance rules in place for materials and MRO spares, the company can ensure that each part has a single, standardized record with accurate descriptions, part numbers, and supplier information. Data quality rules enforce consistency across all systems, making it easier to track inventory, automate reordering, and prevent the purchasing of redundant parts.
Practical Benefit: The organization can quickly identify and procure the correct parts, minimizing downtime and optimizing machine availability. It also improves inventory management by reducing redundant stock, leading to cost savings and more efficient operations.
Customer Master Data
Example: Personalized Customer Experience and Compliance in Retail
Problem Without MDG: A large retail chain struggles with duplicate or outdated customer records. Customers frequently change their contact information, or multiple accounts are created for the same person (e.g., due to name variations or typos). This leads to poor customer experience, as sales representatives cannot access complete or accurate customer histories. Additionally, legal and marketing teams cannot ensure compliance with data privacy laws like GDPR because the data is inconsistent.
MDG Benefit: By implementing Master Data Governance for customer master data, the retailer can enforce data validation rules (e.g., ensuring only one customer account exists per individual), apply standardized formats for contact information (e.g., phone numbers, addresses), and establish data stewardship processes to periodically cleanse outdated records.
Practical Benefit: The retailer improves customer personalization, as sales teams now have a single, unified view of each customer’s purchasing history, preferences, and interactions. This allows them to offer more tailored recommendations and promotions. Moreover, the retailer ensures data privacy compliance by maintaining accurate and up-to-date customer records, reducing legal risk and avoiding fines related to non-compliance.
Vendor Master Data
Example: Streamlined Supplier Onboarding and Risk Management in Procurement
Problem Without MDG: A global pharmaceutical company manages thousands of suppliers across various regions. However, without a proper Master Data Governance process for vendor master data, supplier records are fragmented across multiple systems, making it difficult to track supplier performance, certifications, and regulatory compliance status. This leads to delays in supplier onboarding and higher risks of working with non-compliant or low-performing suppliers.
MDG Benefit: With vendor master data governance in place, the company can create a centralized repository where vendor records are consistently maintained. Governance rules enforce validation checks on key information, such as tax ID numbers, certifications, and contract terms. Workflow automation ensures that new suppliers undergo a standardized, compliant onboarding process before being added to the approved list.
Practical Benefit: By leveraging accurate and validated vendor data, the company can significantly speed up supplier onboarding, while ensuring that only compliant and high-quality vendors are approved. This reduces procurement risks, improves supplier relationships, and ensures compliance with industry regulations (e.g., FDA or ISO certifications). Moreover, the company can track supplier performance metrics over time, leading to better strategic sourcing decisions and cost savings.
Who Can Support with MDG Solutions?
The undisputed leader in Master Data Governance is by far SAP MDG. To enable corporations to better manage their ERP data, SAP released SAP MDG sometime around the 2000s for setting up a governance framework.
Since then, several master data governance software vendors have entered the space with powerful alternatives. Oracle, Microsoft, IBM, and Stibo Systems have each released robust data governance suites, offering a range of features like workflow automation, data quality checks, role-based access, and integration capabilities to streamline and scale governance implementation.
However, as we previously mentioned, Governance practices can differ based on industries, types of master data, internal policies, organization hierarchies and so on.
In our experience so far as a specialist in Master Data Management Solutions, we’ve observed that most of these softwares are not purpose-built, can be expensive and resource-intensive to implement and generally take a whole lot of time to execute.
Seeing this Gap, we at Verdantis have built our own state-of-the art Master Data Governance product (Integrity©) that takes into account industry-specific use-cases, easily integrates with most ERPs (including SAP MDG) and leverages specifically trained machine-learning models to enable adherence to Data Governance more accessible.
Conclusion
Master Data Governance is no longer a back-office function—it’s a core enabler of operational resilience, regulatory compliance, and digital transformation. As organizations accelerate toward data-driven decision-making, the ability to maintain clean, consistent, and governed master data becomes a strategic differentiator.
What’s needed now are governance solutions that go beyond static frameworks—tools that are adaptive, intelligent, and tailored to the specific needs of modern enterprises.
Companies that invest in the right governance approach today will be better positioned to scale, respond to market shifts, and drive sustained efficiency across every function tomorrow.
