Quick Overview
Data Governance first emerged in the early 2000s, during the dot-com boom. At the time, the concept was largely limited to IT teams and focused primarily on organizing and cataloging data. The goal was to streamline processes, track the data’s origin, and ensure its ongoing relevance.
In recent years, however, two significant technological and business shifts have elevated Master Data Governance into a much broader and more impactful concept. These changes have expanded its scope, though the core objective remains largely the same. The two key changes were:
As businesses increasingly adopted ERP systems, they began to depend on vast databases that captured crucial information and guided both micro and macro business decisions. Today, key business decisions—ranging from procurement and hiring to vendor onboarding and supplier relationships—are driven by the insights and data extracted from these ERP systems.
Back in the 1900s, few would have predicted that a single organization could manage production on such a massive scale. Today, it's commonplace for corporations to run multiple large-scale production facilities, each responsible for manufacturing billions in finished goods or overseeing projects valued in the billions of dollars. In essence, the consolidation of business operations and the growing scale of enterprises have made it imperative for companies to rely on data-driven decisions, powered by software solutions and ERP systems, to achieve operational efficiency.
Master Data Governance
The reliance on ERP systems, company data, and technology, while essential, can also become a double-edged sword due to the inevitability of human error.
Over time, poor practices in data upload, maintenance, and removal can result in "junk" data within the ERP, which can severely disrupt business operations. This leads to issues like production delays, excessive inventory costs, weakened supplier relationships, and a complete lack of actionable insights into customer behavior.
This trend has highlighted the critical need for a "Master Data Governance" system, which plays a key role in Master Data Management. In fact, it may be the most powerful tool for ensuring that 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 within the organization, such as a customer, product, supplier, or employee. It ensures consistency and accuracy across various systems and processes. Master data typically includes essential details like names, addresses, IDs, and other important attributes that remain stable over time and are shared throughout the organization.
Almost all types of Master Data Management incorporate some form of "Governance" to minimize human errors. These include:
Each of these disciplines requires a flexible approach to data governance. Additionally, businesses are increasingly recognizing that these governance systems need to adapt to various factors such as industry, location, product, departments, and supplier types. Unfortunately, current legacy systems in the market often fail to accommodate these diverse needs.
Strategies & Tactics for Master Data Governance
It's important to understand that governance practices aren't just limited to a "tool," "software," or "rulebook." They require an organization-wide understanding of business processes and necessitate buy-in from key departments such as IT, Procurement, Customer Success, and Maintenance teams.
The software and technologies serve as tools that reflect carefully crafted processes and workflows, all governed by the right people to drive optimal business outcomes. Below are some of the tactics that can be leveraged:
Data Validation
Validation is a crucial aspect of ensuring that master data remains accurate, consistent, and in compliance with business rules. The validation process verifies the Assure of the master data throughout its creation, modification, and approval stages, helping to prevent errors or inconsistencies from entering the system.
1. Rules and Logic Definition
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Validation Rules: These are preset business rules or logic that determine what qualifies as valid master data. For example, a rule could mandate that a customer ID be unique, a postal code corresponds to the correct country, or a product category falls within an approved list of values.
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Custom Rules: Organizations can define their own specific validation rules through tools like the Business Rule Framework (BRF+), tailored to their unique data needs and requirements.
2. Validation during Data Entry
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When users create or update master data records (such as customer, material, or supplier data), the system verifies the data against the predefined validation rules.
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Real-time Validation: As data is being entered, the software performs real-time checks to
Approval Workflows
Almost all Master Data Management (MDM) platforms include an integrated "approval matrix" with varying levels of flexibility. This system ensures that no single individual can make the decision to "edit" or "create" a master data record independently. By doing so, it reduces the likelihood of errors and guarantees that the appropriate stakeholders have visibility into the relevant business processes.
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.
Given the complexity of modern organizations, most approval workflows involve multiple decision-makers with varying levels of access. These workflows should be easily configurable within data governance systems to accommodate such structures.
Data LifeCycle Management
Data Lifecycle Management (DLM) within Master Data Governance encompasses the processes and policies that oversee the entire lifecycle of master data. This includes its creation, usage, and eventual archiving or deletion. DLM ensures that data remains accurate, secure, and compliant with relevant regulations throughout its lifecycle, while reducing the risks of data degradation, redundancy, or misuse.
Key Stages of DLM:
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Creation: Data is entered into the system with appropriate validation and approval processes in place.
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Use: Data is actively utilized by various departments or applications, ensuring consistency and seamless integration across systems.
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Maintenance: Regular updates and corrections are applied to ensure that the data remains accurate and aligned with evolving business needs.
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Archiving: Data that is no longer in active use but must be retained for compliance or historical purposes is securely stored in an archive.
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Deletion: Data is removed from the system when it is no longer needed or after it has exceeded its defined retention period.
Example
In the Supplier Master Data process:
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Creation: A new supplier is onboarded, and their information (such as contact details and payment terms) is entered into the system, validated, and approved by the Procurement and Finance teams.
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Use: The supplier data is utilized across various systems for purchasing, invoicing, and payment processing.
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Maintenance: If the supplier’s address or other information changes, the system is updated after going through a validation and approval workflow.
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Archiving: If the supplier record is no longer in active use but must be retained for historical or compliance reasons, it is archived.
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Deletion: If the supplier remains inactive for a specified period (e.g., 7 years), their data is deleted from the system, in accordance with retention and compliance policies.
DLM guarantees that data is handled efficiently, consistently, and in adherence to legal and regulatory requirements throughout its entire lifecycle.
Practical use-cases of Data Governance Systems
Materials and MRO Spares
Example: Enhanced Spare Parts Availability and Reduced Downtime in Manufacturing
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Challenge Without MDG: A manufacturing company maintains a large inventory of materials and spare parts for machine maintenance. However, without effective governance of material master data, the company faces issues like duplicate records, inconsistent part numbers, and incomplete descriptions. These problems cause delays in locating and ordering critical parts, leading to extended machine downtime, operational disruptions, and loss of production.
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MDG Solution: By implementing Master Data Governance for materials and MRO spares, the company ensures that each part has a single, standardized record with accurate descriptions, part numbers, and supplier details. Data quality rules ensure consistency across all systems, simplifying inventory tracking, automating reordering, and preventing unnecessary purchases of redundant parts.
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Real-World Impact: The organization can quickly identify and procure the correct parts, minimizing downtime and maximizing machine availability. This also optimizes inventory management by reducing excess stock, resulting in cost savings and improved operational efficiency.
Customer Master Data
Example: Enhanced Customer Experience and Compliance in Retail
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Challenge Without MDG: A large retail chain faces issues with duplicate or outdated customer records. Customers often change their contact details, or multiple accounts are created for the same person (e.g., due to name variations or errors). This results in a poor customer experience as sales representatives can't access complete or accurate customer histories. Additionally, legal and marketing teams struggle to ensure compliance with data privacy regulations like GDPR due to inconsistent data.
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MDG Solution: By implementing Master Data Governance for customer master data, the retailer can enforce data validation rules (e.g., ensuring only one account exists per customer), apply standardized formats for contact details (e.g., phone numbers, addresses), and set up data stewardship processes to periodically clean outdated records.
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Real-World Impact: The retailer enhances customer personalization by providing sales teams with a single, unified view of each customer's purchase history, preferences, and interactions. This enables more tailored recommendations and promotions. Furthermore, the retailer ensures data privacy compliance by keeping accurate, up-to-date customer records, reducing legal risks and avoiding penalties for non-compliance.
Vendor Master Data
Example: Streamlined Supplier Onboarding and Risk Management in Procurement
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Challenge Without MDG: A global pharmaceutical company manages thousands of suppliers across various regions. Without a solid Master Data Governance process for vendor master data, supplier records are scattered across different systems. This makes it difficult to track supplier performance, certifications, and regulatory compliance status, leading to delays in onboarding new suppliers and increasing the risk of working with non-compliant or underperforming suppliers.
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MDG Solution: By implementing vendor master data governance, the company creates a centralized repository where vendor records are consistently managed. Governance rules enforce validation checks on critical data, such as tax ID numbers, certifications, and contract terms. Workflow automation ensures new suppliers follow a standardized, compliant onboarding process before being added to the approved supplier list.
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Real-World Impact: With accurate, validated supplier data, the company can accelerate the onboarding process while ensuring only compliant and high-quality vendors are approved. This reduces procurement risks, strengthens supplier relationships, and ensures adherence to industry regulations (e.g., FDA or ISO certifications). Additionally, the company can monitor supplier performance 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 SAP MDG. Released by SAP in the early 2000s, SAP MDG was developed to help organizations establish a robust governance framework for managing their ERP data.
Since then, several master data governance software vendors have emerged, offering powerful alternatives. Major players such as Oracle, Microsoft, IBM, and Stibo Systems have launched comprehensive data governance suites. These solutions provide a variety of features, including workflow automation, data quality validation, role-based access controls, and seamless integration capabilities—streamlining and scaling governance efforts across organizations.
As we mentioned earlier, governance practices can vary depending on industry, the types of master data, internal policies, organizational structures, and more.
In our experience as specialists in Master Data Management solutions, we've noticed that most of the available software is not purpose-built, tends to be expensive, resource-intensive to implement, and often requires significant time for full execution.
Recognizing this gap, we at Moresco have developed our own cutting-edge Master Data Governance product, Assure©. It’s designed to address industry-specific use cases, integrates seamlessly with most ERPs (including SAP MDG), and leverages specialized machine-learning models to make adherence to data governance more accessible and efficient.
Conclusion
Master Data Governance is no longer just a back-office task—it’s a key driver of operational resilience, regulatory compliance, and digital transformation. As organizations shift toward data-driven decision-making, maintaining clean, consistent, and well-governed master data has become a critical strategic advantage.
What’s needed now are governance solutions that go beyond traditional, static frameworks—tools that are flexible, intelligent, and specifically designed to meet the unique needs of modern enterprises.
Companies that invest in the right governance approach today will be better equipped to scale, adapt to market changes, and achieve long-term efficiency across all business functions.
