Master Data Governance (MDG) is the practice of improving data management and handling of Master Data. In addition, Master Data is the primary data field you use to manage a business or organization. For example, you might buy materials from suppliers or vendors to produce products and deliver them to your customers. To understand how Master Data works, let’s discover 8 Master Data Governance best practices in this article.   

Importance of MDG 

Having consistent and accurate data about the materials, suppliers, products, customers, and partners will help you improve your processes’ efficiency and accuracy, such as making payments and reporting. Therefore, practicing MDG will not only help you improve your processes, but it will also lead you to avail yourself of other significant benefits.  

MDG will effectively reduce your operating costs, and we can minimize the chances of the risk occurring. Without effective MDG, data discrepancies between different systems of an organization cannot be eliminated. For example, customers’ names can be categorized separately into sales, logistics, and customer service systems.  

This issue can also complicate data integration efforts and cause data integrity issues that affect the accuracy of Business Intelligence applications, corporate reporting, and analytics. Furthermore, we cannot identify and correct data errors which further affect the efficiency and analytics of Business Intelligence. After clearly understanding the importance of Master Data and MDG, we can conduct Master Data Governance best practices in the most effective way.    

Master Data Governance Best Practices 

Below is a summary of some of the critical categories and practices for Master Data Governance best practices:  

  • Policies  
  • Rules and Regulations  
  • Catalog  
  • Process Design  
  • People  
  • Workflow  
  • Matrix 
  • Measure your Concentration for MDG Technologies  


The first of Master Data Governance best practices is policies. MDG guarantees that they treat internal and external policies and regulations as part of MDG. Policies can guide you through various aspects of MDG, such as quality, privacy and security, data storage and disposal, and risk management. For example, the division of the responsibility between those who can create cost center Master Data in an accounting system and those who can approve the creation of cost centers has a risk control policy that will help prevent accounting frauds.  

Rules and Regulations 

Policies determine how you want to operate; rules and regulations define how these policies will be implemented and enforced. For example, you may have a policy stating that employees must consent to process personal information before using it. In addition, a rule can define consent attributes that should be part of the critical definition of consumer data, such as billing, marketing, and third-party sharing.     

Another rule is that the consent attributes may need to be collected before the customer’s record can be created and approved. And a third principle can verify marketing consent before sending and using customer data in a marketing automation system. It is not uncommon to have multiple rules to meet the requirements of a single policy.    


Master Data Governance best practices includes several catalog features such as:   

  • Identify and develop documentation of the Master Data domains included in applications and other sources, such as data warehouses.    
  • Verify the accuracy and completeness of the Master Data on each resource.    
  • Test the stability of Master Data definitions on all sources.   

Understanding Master Data, knowing its exact position and location, and explaining how it matches your definitions and policies is essential for managing Master Data.    

Process Design 

Just as a catalog documents the location of Master Data, the process map shows how Master Data flows between resources as part of business activities. Understanding the master data resources and implementing them helps see things better. 

In this process, how to use the data must include the risk of compliance and rules to make policies work. To illustrate this, consider the clinical trial process. You need to understand where the data is collected, what system it is derived from, and third parties to enforce the standard for receiving and sending data and policies.    


Data Governance documents provide information about the organizational work necessary for the success of Master Data Management activities. These key people include:  

  • Business Professionals determine the standard definitions of the organization’s Master Data and the types and standards required for various business processes.     
  • Data Managers are responsible for optimizing data quality issues in specific areas of Master Data.    
  • The IT staff is responsible for the database, applications, and processes architecture and management.    
  • Legal and Security Agencies are responsible for the confidentiality and security of data. General leaders form governing councils to resolve disputes between different organizational functions.   


  • Once you have identified your key people, you will need to document the workflow that will allow them to collaborate:    
  • Workflow for Master Data creation determines the method of generating demand.    
  • Many organizations must be involved in the parallel workflow approval, activation, and business application process. 


Master Data Governance best practices also determine the metrics used to measure and manage data. For example, typical technical parameters include the number of copy records on an application, the accuracy and completeness of Master Data, and how many personal data attributes are encoded and masked. While this metric can be helpful in the technical management of master data, major organizations may often try to understand better how these technical metrics affect business performance measurement.    

Measure your Concentration for MDG Technologies 

From a governance point of view, technology defines the type of resources required for MDG. These technologies include links and metadata scanners to help the source master catalog and line and process management. Also, the features that help with process mapping and workflow.  

The Bottom Line 

Master Data Governance is an ongoing project. The company should have updated information about the industry, market, and changing requirements. As the national policies, government regulations, and business requirements vary, the company should update the MDG program. Also, it is essential for the company to regularly review its process and technologies and confirm they continue to support the MDG program and its goals. Make necessary adjustments if there are improvements. All of these actions will help organizations carry out Master Data Governance best practices with the most beneficial results. 

Through many years working in data solution industry, we truly understand the loss of an organization for dealing with data solution issues. That’s the reason why we built SimpleMDG – a Master Data Governance to help businesses solve their pain in data solution.  

Click here to request a Demo for SimpleMDG.